Large language models (LLMs) are becoming increasingly useful for programming and robotics tasks, but for more complicated reasoning problems, the gap between these systems and humans looms large. Without the ability to learn new concepts like humans do, these systems fail to form good abstractions — essentially, high-level representations of complex concepts that skip less-important details — and thus sputter when asked to do more sophisticated tasks.

Luckily, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have found a treasure trove of abstractions within natural language. In three papers to be presented at the International Conference on Learning Representations this month, the group shows how our everyday words are a rich source of context for language models, helping them build better overarching representations for code synthesis, AI planning, and robotic navigation and manipulation.

The three separate frameworks build libraries of abstractions for their given task: LILO (library induction from language observations) can synthesize, compress, and document code; Ada (action domain acquisition) explores sequential decision-making for artificial intelligence agents; and LGA (language-guided abstraction) helps robots better understand their environments to develop more feasible plans. Each system is a neurosymbolic method, a type of AI that blends human-like neural networks and program-like logical components.

LILO: A neurosymbolic framework that codes

Large language models can be used to quickly write solutions to small-scale coding tasks, but cannot yet architect entire software libraries like the ones written by human software engineers. To take their software development capabilities further, AI models need to refactor (cut down and combine) code into libraries of succinct, readable, and reusable programs.

Refactoring tools like the previously developed MIT-led Stitch algorithm can automatically identify abstractions, so, in a nod to the Disney movie “Lilo & Stitch,” CSAIL researchers combined these algorithmic refactoring approaches with LLMs. Their neurosymbolic method LILO uses a standard LLM to write code, then pairs it with Stitch to find abstractions that are comprehensively documented in a library.

LILO’s unique emphasis on natural language allows the system to do tasks that require human-like commonsense knowledge, such as identifying and removing all vowels from a string of code and drawing a snowflake. In both cases, the CSAIL system outperformed standalone LLMs, as well as a previous library learning algorithm from MIT called DreamCoder, indicating its ability to build a deeper understanding of the words within prompts. These encouraging results point to how LILO could assist with things like writing programs to manipulate documents like Excel spreadsheets, helping AI answer questions about visuals, and drawing 2D graphics.

“Language models prefer to work with functions that are named in natural language,” says Gabe Grand SM ’23, an MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and lead author on the research. “Our work creates more straightforward abstractions for language models and assigns natural language names and documentation to each one, leading to more interpretable code for programmers and improved system performance.”

When prompted on a programming task, LILO first uses an LLM to quickly propose solutions based on data it was trained on, and then the system slowly searches more exhaustively for outside solutions. Next, Stitch efficiently identifies common structures within the code and pulls out useful abstractions. These are then automatically named and documented by LILO, resulting in simplified programs that can be used by the system to solve more complex tasks.

The MIT framework writes programs in domain-specific programming languages, like Logo, a language developed at MIT in the 1970s to teach children about programming. Scaling up automated refactoring algorithms to handle more general programming languages like Python will be a focus for future research. Still, their work represents a step forward for how language models can facilitate increasingly elaborate coding activities.

Ada: Natural language guides AI task planning

Just like in programming, AI models that automate multi-step tasks in households and command-based video games lack abstractions. Imagine you’re cooking breakfast and ask your roommate to bring a hot egg to the table — they’ll intuitively abstract their background knowledge about cooking in your kitchen into a sequence of actions. In contrast, an LLM trained on similar information will still struggle to reason about what they need to build a flexible plan.

Named after the famed mathematician Ada Lovelace, who many consider the world’s first programmer, the CSAIL-led “Ada” framework makes headway on this issue by developing libraries of useful plans for virtual kitchen chores and gaming. The method trains on potential tasks and their natural language descriptions, then a language model proposes action abstractions from this dataset. A human operator scores and filters the best plans into a library, so that the best possible actions can be implemented into hierarchical plans for different tasks.

“Traditionally, large language models have struggled with more complex tasks because of problems like reasoning about abstractions,” says Ada lead researcher Lio Wong, an MIT graduate student in brain and cognitive sciences, CSAIL affiliate, and LILO coauthor. “But we can combine the tools that software engineers and roboticists use with LLMs to solve hard problems, such as decision-making in virtual environments.”

When the researchers incorporated the widely-used large language model GPT-4 into Ada, the system completed more tasks in a kitchen simulator and Mini Minecraft than the AI decision-making baseline “Code as Policies.” Ada used the background information hidden within natural language to understand how to place chilled wine in a cabinet and craft a bed. The results indicated a staggering 59 and 89 percent task accuracy improvement, respectively.

With this success, the researchers hope to generalize their work to real-world homes, with the hopes that Ada could assist with other household tasks and aid multiple robots in a kitchen. For now, its key limitation is that it uses a generic LLM, so the CSAIL team wants to apply a more powerful, fine-tuned language model that could assist with more extensive planning. Wong and her colleagues are also considering combining Ada with a robotic manipulation framework fresh out of CSAIL: LGA (language-guided abstraction).

Language-guided abstraction: Representations for robotic tasks

Andi Peng SM ’23, an MIT graduate student in electrical engineering and computer science and CSAIL affiliate, and her coauthors designed a method to help machines interpret their surroundings more like humans, cutting out unnecessary details in a complex environment like a factory or kitchen. Just like LILO and Ada, LGA has a novel focus on how natural language leads us to those better abstractions.

In these more unstructured environments, a robot will need some common sense about what it’s tasked with, even with basic training beforehand. Ask a robot to hand you a bowl, for instance, and the machine will need a general understanding of which features are important within its surroundings. From there, it can reason about how to give you the item you want. 

In LGA’s case, humans first provide a pre-trained language model with a general task description using natural language, like “bring me my hat.” Then, the model translates this information into abstractions about the essential elements needed to perform this task. Finally, an imitation policy trained on a few demonstrations can implement these abstractions to guide a robot to grab the desired item.

Previous work required a person to take extensive notes on different manipulation tasks to pre-train a robot, which can be expensive. Remarkably, LGA guides language models to produce abstractions similar to those of a human annotator, but in less time. To illustrate this, LGA developed robotic policies to help Boston Dynamics’ Spot quadruped pick up fruits and throw drinks in a recycling bin. These experiments show how the MIT-developed method can scan the world and develop effective plans in unstructured environments, potentially guiding autonomous vehicles on the road and robots working in factories and kitchens.

“In robotics, a truth we often disregard is how much we need to refine our data to make a robot useful in the real world,” says Peng. “Beyond simply memorizing what’s in an image for training robots to perform tasks, we wanted to leverage computer vision and captioning models in conjunction with language. By producing text captions from what a robot sees, we show that language models can essentially build important world knowledge for a robot.”

The challenge for LGA is that some behaviors can’t be explained in language, making certain tasks underspecified. To expand how they represent features in an environment, Peng and her colleagues are considering incorporating multimodal visualization interfaces into their work. In the meantime, LGA provides a way for robots to gain a better feel for their surroundings when giving humans a helping hand. 

An “exciting frontier” in AI

“Library learning represents one of the most exciting frontiers in artificial intelligence, offering a path towards discovering and reasoning over compositional abstractions,” says assistant professor at the University of Wisconsin-Madison Robert Hawkins, who was not involved with the papers. Hawkins notes that previous techniques exploring this subject have been “too computationally expensive to use at scale” and have an issue with the lambdas, or keywords used to describe new functions in many languages, that they generate. “They tend to produce opaque ‘lambda salads,’ big piles of hard-to-interpret functions. These recent papers demonstrate a compelling way forward by placing large language models in an interactive loop with symbolic search, compression, and planning algorithms. This work enables the rapid acquisition of more interpretable and adaptive libraries for the task at hand.”

By building libraries of high-quality code abstractions using natural language, the three neurosymbolic methods make it easier for language models to tackle more elaborate problems and environments in the future. This deeper understanding of the precise keywords within a prompt presents a path forward in developing more human-like AI models.

MIT CSAIL members are senior authors for each paper: Joshua Tenenbaum, a professor of brain and cognitive sciences, for both LILO and Ada; Julie Shah, head of the Department of Aeronautics and Astronautics, for LGA; and Jacob Andreas, associate professor of electrical engineering and computer science, for all three. The additional MIT authors are all PhD students: Maddy Bowers and Theo X. Olausson for LILO, Jiayuan Mao and Pratyusha Sharma for Ada, and Belinda Z. Li for LGA. Muxin Liu of Harvey Mudd College was a coauthor on LILO; Zachary Siegel of Princeton University, Jaihai Feng of the University of California at Berkeley, and Noa Korneev of Microsoft were coauthors on Ada; and Ilia Sucholutsky, Theodore R. Sumers, and Thomas L. Griffiths of Princeton were coauthors on LGA. 

LILO and Ada were supported, in part, by ​​MIT Quest for Intelligence, the MIT-IBM Watson AI Lab, Intel, U.S. Air Force Office of Scientific Research, the U.S. Defense Advanced Research Projects Agency, and the U.S. Office of Naval Research, with the latter project also receiving funding from the Center for Brains, Minds and Machines. LGA received funding from the U.S. National Science Foundation, Open Philanthropy, the Natural Sciences and Engineering Research Council of Canada, and the U.S. Department of Defense.

Large language models (LLMs) are making a significant impact in the realm of artificial intelligence (AI). Their impressive generative abilities have led to widespread adoption across various sectors and use cases, including content generation, sentiment analysis, chatbot development, and virtual assistant technology. Llama2 by Meta is an example of an LLM offered by AWS. Llama 2 is an auto-regressive language model that uses an optimized transformer architecture and is intended for commercial and research use in English. It comes in a range of parameter sizes—7 billion, 13 billion, and 70 billion—as well as pre-trained and fine-tuned variations. To learn more about Llama 2 on AWS, refer to Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart.

Many practitioners fine-tune or pre-train these Llama 2 models with their own text data to improve accuracy for their specific use case. However, in some cases, a challenge arises for practitioners: the high cost of fine-tuning and training. As organizations strive to push the boundaries of what LLMs can achieve, the demand for cost-effective training solutions has never been more pressing. In this post, we explore how you can use the Neuron distributed training library to fine-tune, continuously pre-train, and reduce the cost of training LLMs such as Llama 2 with AWS Trainium instances on Amazon SageMaker.

AWS Trainium instances for training workloads

SageMaker ml.trn1 and ml.trn1n instances, powered by Trainium accelerators, are purpose-built for high-performance deep learning training and offer up to 50% cost-to-train savings over comparable training optimized Amazon Elastic Compute Cloud (Amazon EC2) instances. This post implements a solution with the ml.trn1.32xlarge Trainium instance type, typically used for training large-scale models. However, there are also comparable ml.trn1n instances that offer twice as much networking throughput (1,600 Gbps) via Amazon Elastic Fabric Adapter (EFAv2). SageMaker Training supports the availability of ml.trn1 and ml.trn1n instances in the US East (N. Virginia) and US West (Oregon) AWS Regions, and most recently announced general availability in the US East (Ohio) Region. These instances are available in the listed Regions with On-Demand, Reserved, and Spot Instances, or additionally as part of a Savings Plan.

For more information on Trainium Accelerator chips, refer to Achieve high performance with lowest cost for generative AI inference using AWS Inferentia2 and AWS Trainium on Amazon SageMaker. Additionally, check out AWS Trainium Customers to learn more about customer testimonials, or see Amazon EC2 Trn1 Instances for High-Performance Model Training are Now Available to dive into the accelerator highlights and specifications.

Using the Neuron Distributed library with SageMaker

SageMaker is a fully managed service that provides developers, data scientists, and practitioners the ability to build, train, and deploy machine learning (ML) models at scale. SageMaker Training includes features that improve and simplify the ML training experience, including managed infrastructure and images for deep learning, automatic model tuning with hyperparameter optimization, and a pay-for-what-you-use billing structure. This section highlights the advantages of using SageMaker for distributed training with the Neuron Distributed library—specifically, the managed infrastructure, time-to-train, and cost-to-train benefits of its associated resiliency and recovery features, and is part of the AWS Neuron SDK used to run deep learning workloads on AWS Inferentia and AWS Trainum based instances.

In high performance computing (HPC) clusters, such as those used for deep learning model training, hardware resiliency issues can be a potential obstacle. Although hardware failures while training on a single instance may be rare, issues resulting in stalled training become more prevalent as a cluster grows to tens or hundreds of instances. Regular checkpointing helps mitigate wasted compute time, but engineering teams managing their own infrastructure must still closely monitor their workloads and be prepared to remediate a failure at all hours to minimize training downtime. The managed infrastructure of SageMaker Training includes several resiliency features that make this monitoring and recovery process streamlined:

Cluster health checks – Before a training job starts, SageMaker runs health checks and verifies communication on the provisioned instances. It then replaces any faulty instances, if necessary, to make sure the training script starts running on a healthy cluster of instances. Health checks are currently enabled for the TRN1 instance family as well as P* and G* GPU-based instance types.
Automatic checkpointing – Checkpoints from a local path (/opt/ml/checkpoints by default) are automatically copied to an Amazon Simple Storage Service (Amazon S3) location specified by the user. When training is restarted, SageMaker automatically copies the previously saved checkpoints from the S3 location back to the local checkpoint directory to make sure the training script can load and resume the last saved checkpoint.
Monitoring and tracking training – In the case of a node failure, it’s important to have the visibility of where the failure occurs. Using PyTorch Neuron gives data scientists the ability to track training progress in a TensorBoard. This allows you to capture the loss of the training job to determine when the training job should be stopped to identify the convergence of the model for optimal training.
Built-in retries and cluster repair – You can configure SageMaker to automatically retry training jobs that fail with a SageMaker internal server error (ISE). As part of retrying a job, SageMaker replaces any instances that encountered unrecoverable errors with fresh instances, reboots all healthy instances, and starts the job again. This results in faster restarts and workload completion. Cluster update is currently enabled for the TRN1 instance family as well as P and G GPU-based instance types. Practitioners can add in their own applicative retry mechanism around the client code that submits the job, to handle other types of launch errors, such as like exceeding your account quota.

For customers working with large clusters of hundreds of instances for a training job, the resiliency and recovery features of SageMaker Training can reduce total time for a model to converge by up to 20% via fewer failures and faster recovery. This also enables engineering teams to monitor and react to failures at all hours. Although SageMaker training jobs are suitable for general-purpose training use cases with customizable configurations and integration with the broader AWS ecosystem, Amazon SageMaker HyperPod is specifically optimized for efficient and resilient training of foundation models at scale. For more information on SageMaker HyperPod use cases, refer to the SageMaker HyperPod developer guide.

In this post, we use the Neuron Distributed library to continuously pre-train a Llama 2 model using tensor and pipeline parallelism using SageMaker training jobs. To learn more about the resiliency and recovery features of SageMaker Training, refer to Training large language models on Amazon SageMaker: Best practices.

Solution overview

In this solution, we use an ml.t3.medium instance type on a SageMaker Jupyter notebook to process the provided cells. We will be continuously pre-training our llama2-70b model using the trn1.32xlarge Trainium instance. First, let’s familiarize ourselves with the techniques we use to handle the distribution of the training job created in our solution to contiuously pre-train our llama2-70b model using the Neuron distributed training library.

The techniques used to convert the pre-trained weights in the convert_pretrained_weights.ipynb notebook into a .pt (PyTorch) weights file are called pipeline parallelism and tensor parallelism:

Pipeline parallelism involves a training strategy that combines elements of pipeline parallelism to optimize the training process by splitting a batch or deep neural network into multiple microbatches or layers, allowing each stage worker to process one microbatch.
Tensor parallelism splits tensors of a neural network into multiple devices. This technique allows models with large tensors that can’t fit into the memory of a single device.

After we convert our pre-trained weights with the preceding techniques in our first notebook, we follow two separate notebooks in the same sagemaker-trainium-examples folder. The second notebook is Training_llama2_70b.ipynb, which walks through the continuous pre-training process by saving our checkpoint of converted model weights in the first notebook and prepping it for inference. When this step is complete, we can run the Convert_Nxd_to_hf.ipynb notebook, which takes our pre-trained weights using the NeuronX library and converts it into a readable format in Hugging Face to serve inference.

Prerequisites

You need to complete some prerequisites before you can run the first notebook.

First, make sure you have created a Hugging Face access token so you can download the Hugging Face tokenizer to be used later. After you have the access token, you need to make a few quota increase requests for SageMaker. You need to request a minimum of 8 Trn1 instances ranging to a maximum of 32 Trn1 instances (depending on time-to-train and cost-to-train trade-offs for your use case).

On the Service Quotas console, request the following SageMaker quotas:

Trainium instances (ml.trn1.32xlarge) for training job usage: 8–32
ml.trn1.32xlarge for training warm pool usage: 8–32
Maximum number of instances per training job: 8–32

It may take up to 24 hours for the quota increase to get approved. However, after submitting the quota increase, you can go to the sagemaker-trainium-examples GitHub repo and locate the convert_pretrained_weights.ipynb file. This is the file that you use to begin the continual pre-training process.

Now that you’re ready to begin the process to continuously pre-train the llama2-70b model, you can convert the pre-trained weights in the next section to prep the model and create the checkpoint.

Getting started

Complete the following steps:

Install all the required packages and libraries: SageMaker, Boto3, transformers, and datasets.

These packages make sure that you can set up your environment to access your pre-trained Llama 2 model, download your tokenizer, and get your pre-training dataset.

After the packages are installed, retrieve your Hugging Face access token, and download and define your tokenizer.

The tokenizer meta-llama/Llama-2-70b-hf is a specialized tokenizer that breaks down text into smaller units for natural language processing. This tokenized data will later be uploaded into Amazon S3 to allow for running your training job.

After following the above cells, you will now download the wikicorpus dataset from the Hugging Face dataset.
Tokenize the dataset with the llama-2 tokenizer that you just initialized.

By tokenizing the data, you’re preparing to pre-train your Llama 2 model to enhance the model’s performance to expose it to the trilingual (Catalan, English, Spanish) text data in the wikicorpus dataset to learn intricate patterns and relationships in the dataset.

After the data is tokenized, run the following cell to store the training dataset to s3:

The cell above makes sure that you define the training_input_path and have uploaded the data to your S3 bucket. You’re now ready to begin the training job process.

Run the training job

For the training job, we use the trn1.32xlarge instances with each of the instances having 32 neuron cores. We use tensor parallelism and pipeline parallelism, which allows you to shard the model across Neuron cores for training.

The following code is the configuration for pretraining llama2-70b with trn1:

Now you can define the hyperparameters for training. Note that adjusting these parameters based on hardware capabilities, dataset characteristics, and convergence requirements can significantly impact training performance and efficiency.

The following is the code for the hyperparameters:

Now you specify the Docker image that will be used to train the model on Trainium:

The image we defined is designed for PyTorch training with Neuron optimizations. This image is configured to work with PyTorch, using Neuron SDK version 2.18.0 for enhanced performance and efficiency on Trn1 instances equipped with AWS Trainium chips. This image is also compatible with Python 3.10, indicated by the py310, and is based on Ubuntu 20.04.

Prior to starting your training job, you need to configure it by defining all necessary variables. You do so by defining the training job name, checkpoint directory, and cache directory:

The parameters enable you to do the following:

The training job allows you to identify and track individual training jobs based on timestamps
The checkpoint directory specifies the S3 URI where the checkpoint data, weights, and other information are stored for the trained model
The cache directory helps optimize the training process by storing and reusing previously calculated values, from the checkpoint directory, reducing redundancy and improving efficiency
The environment variables make sure that the training job is optimally configured and settings are tailored to enable efficient and effective training using features like RDMA, optimized memory allocation, fused operations, and Neuron-specific device optimizations

After you have defined your training job and configured all directories and environment variables for an optimal training pipeline, you now set up your PyTorch estimator to begin the training job on SageMaker. A SageMaker estimator is a high-level interface that handles the end-to-end SageMaker training and deployment tasks.

The entry_point is specified as the Python script run_llama_nxd.py. We use the instance_type ml.trn1.32xlarge, the instance count is 32 (which was previously defined as a global variable in the configuration code), and input_mode is set to FastFile. Fast File mode in SageMaker streams data from Amazon S3 on demand, which optimizes data loading performance by fetching data as needed, reducing overall resource consumption. For more information on input, refer to Access Training Data.

Finally, you can start the training job with the SageMaker fit() method, which trains the model based on the defined hyperparameters:

You have successfully started the process to continuously pre-train a llama2-70b model by converting pre-trained weights with tokenized data using SageMaker training on Trainium instances.

Continuous pre-training

After following the prerequisites, completing the provided notebook, and converting the pre-trained weights as a checkpoint, you can now begin the continual pre-training process, using the checkpoint as a point of reference to pre-train the llama2-70b model. The techniques used to convert the pre-trained weights in the convert_pretrained_weights.ipynb notebook into a .pt (PyTorch) weights file are called pipeline parallelism and tensor parallelism.

To begin the continuous pre-training process, follow the Training_llama2_70b.ipynb file in the sagemaker-trainium-examples repo.

Given the large size of the llama2-70b model, you need to convert the pre-trained weights into a more efficient and useable format (.pt). You can do so by defining the hyperparameters in your configuration to store converted weights and checkpoints. The following are the hyperparameters:

If you look at the hyperparameters, the output_dir is used as a reference for pre-training. If you are at this cell, you should have already followed the Training_llama2_70b.ipynb notebook and gone through the process of setting up your SageMaker client and Docker image, and preparing the pre-trained weights for pre-training. You’re now ready to perform the continuous pre-training process on the llama2-70b model.

We use the following parameters to take the pre-trained weights stored in output_dir in the convert_pretrained_weights.ipynb file to be reused continuously for pre-training:

After these hyperparameters are implemented, you can run the rest of the notebook cells to complete the continuous pre-training process. After the SageMaker estimator has completed the training job, you can locate the new checkpoint in the S3 checkpoint directory containing the weights. You can now locate the convert_Nxd_to_hf.ipynb file to get the checkpoint ready for inferencing.

Convert the Neuron Distributed checkpoint for inferencing

Checkpoints play a vital role in the context of distributed training with the NeuronX library because it has checkpoint compatibility with Hugging Face Transformers. You can get the training job output ready for inferencing by taking the training job that is saved as a NeuronX distributed checkpoint and converting the weights into .pt weights files.

To convert the checkpoints to Hugging Face format using NeuronX, you first need to save the S3 nxd_checkpoint_path directory:

After you save the checkpoint in the nxd_checkpoint_path directory, you can save your hyperparameters and configure your SageMaker estimator, which makes sure the pre-training process can begin. You can now run the fit() function within the estimator to convert the pre-trained weights into a checkpoint for inferencing with the following cell:

Summary

You have successfully performed continuous pre-training on a llama2-70b model by converting your pre-trained weights and checkpoint to be used to serve inference using the Neuron SDK and Trainium instances. By following the solution in this post, you should now know how to configure a pipeline for continuous pre-training of an LLM using SageMaker and Trainium accelerator chips.

For more information on how to use Trainium for your workloads, refer to the Neuron SDK documentation or reach out directly to the team. We value customer feedback and are always looking to engage with ML practitioners and builders. Feel free to leave comments or questions in the comments section.

About the authors

Marco Punio is a Solutions Architect focused on generative AI strategy, applied AI solutions and conducting research to help customers hyperscale on AWS. He is a qualified technologist with a passion for machine learning, artificial intelligence, and mergers & acquisitions. Marco is based in Seattle, WA and enjoys writing, reading, exercising, and building applications in his free time.

Armando Diaz is a Solutions Architect at AWS. He focuses on generative AI, AI/ML, and Data Analytics. At AWS, Armando helps customers integrating cutting-edge generative AI capabilities into their systems, fostering innovation and competitive advantage. When he’s not at work, he enjoys spending time with his wife and family, hiking, and traveling the world.

Arun Kumar Lokanatha is a Senior ML Solutions Architect with the Amazon SageMaker Service team. He focuses on helping customers build, train, and migrate ML production workloads to SageMaker at scale. He specializes in deep learning, especially in the area of NLP and CV. Outside of work, he enjoys running and hiking.

Robert Van Dusen is a Senior Product Manager with Amazon SageMaker. He leads frameworks, compilers, and optimization techniques for deep learning training.

Niithiyn Vijeaswaran is a Solutions Architect at AWS. His area of focus is generative AI and AWS AI Accelerators. He holds a Bachelor’s degree in Computer Science and Bioinformatics. Niithiyn works closely with the Generative AI GTM team to enable AWS customers on multiple fronts and accelerate their adoption of generative AI. He’s an avid fan of the Dallas Mavericks and enjoys collecting sneakers.

Rohit Talluri is a Generative AI GTM Specialist (Tech BD) at Amazon Web Services (AWS). He is partnering with top generative AI model builders, strategic customers, key AI/ML partners, and AWS Service Teams to enable the next generation of artificial intelligence, machine learning, and accelerated computing on AWS. He was previously an Enterprise Solutions Architect, and the Global Solutions Lead for AWS Mergers & Acquisitions Advisory.

Sebastian Bustillo is a Solutions Architect at AWS. He focuses on AI/ML technologies with a profound passion for generative AI and compute accelerators. At AWS, he helps customers unlock business value through generative AI. When he’s not at work, he enjoys brewing a perfect cup of specialty coffee and exploring the world with his wife.

One group commonly misunderstood by voice technology are individuals who speak African American English, or AAE. Researchers designed an experiment to test how AAE speakers adapt their speech when imagining talking to a voice assistant, compared to talking to a friend, family member, or stranger. The study tested familiar human, unfamiliar human, and voice assistant-directed speech conditions by comparing speech rate and pitch variation. Analysis of the recordings showed that the speakers exhibited two consistent adjustments when they were talking to voice technology compared to talking to another person: a slower rate of speech with less pitch variation.

Numerous customers face challenges in managing diverse data sources and seek a chatbot solution capable of orchestrating these sources to offer comprehensive answers. This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment.

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. For documentation retrieval, Retrieval Augmented Generation (RAG) stands out as a key tool. It allows you to retrieve data from sources beyond the foundation model, enhancing prompts by integrating contextually relevant retrieved data. You can use prompt engineering to prevent hallucination and make sure that the answer is grounded in the source documentations. To retrieve data from database, you can use foundation models (FMs) offered by Amazon Bedrock, converting text into SQL queries with specified constraints. This process empowers the extraction of data from Amazon Athena tables, effectively addressing inquiries related to data.

For handling more intricate queries, achieving comprehensive answers demands information sourced from both documentation and databases. Agents for Amazon Bedrock is a generative AI tool offered through Amazon Bedrock that enables generative AI applications to execute multistep tasks across company systems and data sources. This integration allows for the synthesis of combined information, resulting in detailed and exhaustive answers.

This post demonstrates how to build a chatbot using Amazon Bedrock including Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock, within an automated solution. The code used in this solution is available in the GitHub repo.

Solution overview

In this post, we use publicly available data, encompassing both unstructured and structured formats, to showcase our entirely automated chatbot system. Our unstructured data comes from the Amazon EC2 User Guide for Linux Instances and Amazon EC2 Instance Types documentation, and the structured data is derived from the EC2 Instance On-Demand Pricing for the US East (N. Virginia) AWS Region.

The following diagram illustrates the solution architecture.

The diagram details a comprehensive AWS Cloud-based setup within a specific Region, using multiple AWS services. The primary interface for the chatbot is a Streamlit application hosted on an Amazon Elastic Container Service (Amazon ECS) cluster, with accessibility managed by an Application Load Balancer. Queries made through this interface activate the AWS Lambda Invocation function, which interfaces with an agent. This agent responds to user inquiries by either consulting the knowledge base or by invoking an Agent Executor Lambda function. This function invokes a set of actions associated with the agent, following a predefined API schema. The knowledge base uses a serverless Amazon OpenSearch Service index as its vector database foundation. Additionally, the Agent Executor function generates SQL queries that are run against the AWS Glue database through Athena.

Deploy the solution with the AWS CDK

The AWS Cloud Development Kit (AWS CDK) is an open source software development framework for defining cloud infrastructure in code and provisioning it through AWS CloudFormation. Our AWS CDK stack deploys resources from the following AWS services:

AWS Key Management Service (AWS KMS)
Application Load Balancer
Amazon Bedrock
Amazon Elastic Container Registry (Amazon ECR)
Amazon Elastic Container Service (Amazon ECS)
AWS Fargate
AWS Glue Data Catalog (for the AWS Glue database component)
AWS Identity and Access Management (IAM)
AWS Lambda
Amazon OpenSearch Service
Amazon Simple Storage Service (Amazon S3)
Amazon Virtual Private Cloud (Amazon VPC)

Refer to the instructions provided in the README.md file for deploying the solution using the AWS CDK. After you have completed all the necessary setup, you can deploy the stack with the following command:

Amazon Bedrock features

Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

In this section, we discuss some of the key Amazon Bedrock features and resources that we use in our solution.

Agents for Amazon Bedrock

Agents for Amazon Bedrock enables you to build and configure autonomous agents in your application. An agent helps your end-users complete actions based on organization data and user input. Agents orchestrate interactions between FMs, data sources, software applications, and user conversations.

For example, you can ask a question such as, “Which EC2 instance is recommended for machine learning model inference and how much does it cost?” The agent can help break down complex tasks into smaller tasks:

“To answer this question, I will:

1. Call the GET::ChatBotBedrockAgentActionGroup::getUc2Response function to get the latest EC2 instance type and pricing details for machine learning inference.

2. Call the GET::x_amz_knowledgebase_WSGY5QH4ZD::Search function to get recommendations on which EC2 instance type is best for machine learning inference.

3. Return the recommended EC2 instance type and the pricing details to the user.

I have checked that I have access to the GET::ChatBotBedrockAgentActionGroup::getUc2Response and GET::x_amz_knowledgebase_WSGY5QH4ZD::Search functions.”

Then the agent will gather information through these three steps and integrate them into a final answer:

“The AWS Inferentia and Trainium instances are well-suited for machine learning model inference workloads. The Inf1 and Trn1 instances deliver high performance inference using dedicated ML chips like Inferentia and Trainium at lower costs compared to GPU-based instances.According to the latest information, AWS Inferentia and Trainium instance prices range from $0.228 per hour for an inf1.xlarge instance to $24.78 per hour for a trn1n.32xlarge instance. The cheapest Inferentia instance is the inf1.xlarge at $0.228 per hour, while the most expensive is the inf2.48xlarge at $12.9813 per hour. The cheapest Trainium instance is the trn1.2xlarge at $1.3438 per hour, while the most expensive is the trn1n.32xlarge at $24.78 per hour.”

Before you create your agent, you should set up action groups and knowledge bases that you want to add to your agent:

Action groups define the tasks that you want your agent to help customers carry out.
Knowledge bases provide a repository of information that the agent can query to answer customer queries and improve its generated responses. For more information, see Knowledge bases for Amazon Bedrock.

After you complete the AWS CDK deployment, you can verify your agent along with its corresponding knowledge base and action group by completing the following steps:

On the Amazon Bedrock console, choose Agents in the navigation pane.
Choose the name of your agent.

Choose the working draft.

You can review the action group and knowledge base in the working draft.

Knowledge Bases for Amazon Bedrock

Knowledge Bases for Amazon Bedrock is a fully managed capability that helps you implement the entire RAG workflow (managed RAG), from ingestion to retrieval and prompt augmentation, without having to build custom integrations to data sources and manage data flows. For this post, we created a knowledge base for Amazon Bedrock using the AWS CDK; it’s based on the database of EC2 instance documentation stored in an S3 bucket.

Action groups

An action group consists of the following components that you set up:

An OpenAPI schema that defines the APIs that your action group should call. Your agent uses the API schema to determine the fields it needs to elicit from the customer to populate for the API request.
A Lambda function that defines the business logic for the action that your agent will carry out.

For each action group in an agent, you define a Lambda function to program the business logic for carrying out an action group and customize how you want the API response to be returned. You use the variables from the input event to define your functions and return a response to the agent. In our use case, we used Amazon Bedrock FMs, converting text into SQL queries with specified constraints. This process empowers the extraction of data from Athena tables, effectively addressing inquiries related to data.

The following screenshot shows an Athena table and sample query.

Sample questions and answers

After the AWS CDK deployment is complete, you can either test the agent on the Amazon Bedrock console or through the Streamlit app URL listed in the outputs of the chatbot stack on the AWS CloudFormation console, as shown in the following screenshot.

In the UI of the chatbot, you can view the source of the response. If the response comes from the knowledge base, you will see a link related to the documentation. If the response is sourced from the Amazon EC2 pricing table, you will see the SQL query text converted from the relevant table. The chatbot is also capable of answering questions that require information from both data sources. The following screenshots show some sample questions and answers with different data sources.

Each response from an Amazon Bedrock agent is accompanied by a trace that details the steps being orchestrated by the agent. The trace helps you follow the agent’s reasoning process that leads it to the response it gives at that point in the conversation.

When you show the trace in the test window in the console, a window appears showing a trace for each step in the reasoning process. You can view each step of the trace in real time as your agent performs orchestration. Each step can be one of the following traces:

PreProcessingTrace – Traces the input and output of the preprocessing step, in which the agent contextualizes and categorizes user input and determines if it is valid
OrchestrationTrace – Traces the input and output of the orchestration step, in which the agent interprets the input, invokes APIs and queries knowledge bases, and returns output to either continue orchestration or respond to the user
PostProcessingTrace – Traces the input and output of the postprocessing step, in which the agent handles the final output of the orchestration and determines how to return the response to the user
FailureTrace – Traces the reason that a step failed

Customizations for your own dataset

To integrate your custom data into the solution, follow the structured guidelines in this section and tailor them to your requirements. These steps are designed to provide a seamless and efficient integration process, enabling you to deploy the solution effectively with your own data.

Integrate knowledge base data

To prepare your data for integration, locate the assets/knowledgebase_data_source/ directory and place your dataset within this folder.

To make configuration adjustments, access the cdk.json file. Navigate to the context/config/paths/knowledgebase_file_name field and update it accordingly. Furthermore, modify the context/config/bedrock_instructions/knowledgebase_instruction field in the cdk.json file to accurately reflect the nuances and context of your new dataset.

Integrate structural data

To organize your structural data, within the assets/data_query_data_source/ directory, create a subdirectory (for example, tabular_data). Deposit your structured dataset (acceptable formats include CSV, JSON, ORC, and Parquet) into this newly created subfolder.

For configuration and code updates, make the following changes:

Update the cdk.json file’s context/config/paths/athena_table_data_prefix field to align with the new data path
Revise code/action-lambda/dynamic_examples.csv by incorporating new text to SQL examples that correspond with your dataset
Revise code/action-lambda/prompt_templates.py to mirror the attributes of your new tabular data
Modify the cdk.json file’s context/config/bedrock_instructions/action_group_description field to elucidate the purpose and functionality of the action Lambda function tailored for your dataset
Reflect the new functionalities of your action Lambda function in the assets/agent_api_schema/artifacts_schema.json file

General updates

In the cdk.json file, under the context/config/bedrock_instructions/agent_instruction section, provide a comprehensive description of the intended functionality and design purpose for your agents, taking into account the newly integrated data.

Clean up

To delete your resources when you’re finished using the solution and to avoid future costs, you can either delete the stack on the AWS CloudFormation console or run the following command in the terminal:

Conclusion

In this post, we illustrated the process of using the AWS CDK to establish and oversee a set of AWS resources designed to construct a chatbot on Amazon Bedrock. If you’re interested in connecting to your data source and developing your own chatbot, you can begin exploring with Amazon Bedrock.

About the Authors

Jundong Qiao is a Machine Learning Engineer at AWS Professional Service, where he specializes in implementing and enhancing AI/ML capabilities across various sectors. His expertise encompasses building next-generation AI solutions, including chatbots and predictive models that drive efficiency and innovation. Prior to AWS, Jundong was an Engineering Manager in Machine Learning at ACV Auctions, where he led initiatives that leveraged AI/ML to address intricate issues within the automotive industry.

Kara Yang is a data scientist at AWS Professional Services, adept at leveraging cloud computing, machine learning, and Generative AI to tackle diverse industry challenges. Passionately dedicated to innovation, she consistently pursues new technologies, refines solutions, and delights in sharing her expertise through writing and presentations.

Kiowa Jackson is a Machine Learning Engineer at AWS ProServe, dedicated to helping customers leverage Generative AI for creating and deploying novel applications. He is passionate about placing the benefits of GenAI in the hands of users through real-world use cases.

Praveen Kumar Jeyarajan is a Principal DevOps Consultant at AWS, supporting Enterprise customers and their journey to the cloud. He has 13+ years of DevOps experience and is skilled in solving myriad technical challenges using the latest technologies. He holds a Masters degree in Software Engineering. Outside of work, he enjoys watching movies and playing tennis.

Shuai Cao is a Senior Data Science Manager focused on Generative AI at Amazon Web Services. He leads teams of data scientists, machine learning engineers, and application architects to deliver AI/ML solutions for customers. Outside of work, he enjoys composing and arranging music.

Imagine harnessing the power of advanced language models to understand and respond to your customers’ inquiries. Amazon Bedrock, a fully managed service providing access to such models, makes this possible. Fine-tuning large language models (LLMs) on domain-specific data supercharges tasks like answering product questions or generating relevant content.

In this post, we show how Amazon Bedrock and Amazon SageMaker Canvas, a no-code AI suite, allow business users without deep technical expertise to fine-tune and deploy LLMs. You can transform customer interaction using datasets like product Q&As with just a few clicks using Amazon Bedrock and Amazon SageMaker JumpStart models.

Solution overview

The following diagram illustrates this architecture.

In the following sections, we show you how to fine-tune a model by preparing your dataset, creating a new model, importing the dataset, and selecting a foundation model. We also demonstrate how to analyze and test the model, and then deploy the model via Amazon Bedrock.

Prerequisites

First-time users need an AWS account and AWS Identity and Access Management (IAM) role with SageMaker, Amazon Bedrock, and Amazon Simple Storage Service (Amazon S3) access.

To follow along with this post, complete the prerequisite steps to create a domain and enable access to Amazon Bedrock models:

Create a SageMaker domain.
On the domain details page, view the user profiles.
Choose Launch by your profile, and choose Canvas.
Confirm that your SageMaker IAM role and domain roles have the necessary permissions and trust relationships.
On the Amazon Bedrock console, choose Model access in the navigation pane.
Choose Manage model access.
Select Amazon to enable the Amazon Titan model.

Prepare your dataset

Complete the following steps to prepare your dataset:

Download the following CSV dataset of question-answer pairs.
Confirm that your dataset is free from formatting issues.
Copy the data to a new sheet and delete the original.

Create a new model

SageMaker Canvas allows simultaneous fine-tuning of multiple models, enabling you to compare and choose the best one from a leaderboard after fine-tuning. However, this post focuses on the Amazon Titan Text G1-Express LLM. Complete the following steps to create your model:

In SageMaker canvas, choose My models in the navigation pane.
Choose New model.
For Model name, enter a name (for example, MyModel).
For Problem type¸ select Fine-tune foundation model.
Choose Create.

The next step is to import your dataset into SageMaker Canvas:

Create a dataset named QA-Pairs.
Upload the prepared CSV file or select it from an S3 bucket.
Choose the dataset, then choose Select dataset.

Select a foundation model

After you upload your dataset, select a foundation model and fine-tune it with your dataset. Complete the following steps:

On the Fine-tune tab, on the Select base models menu¸ select Titan Express.
For Select input column, choose question.
For Select output column, choose answer.
Choose Fine-tune.

Wait 2–5 hours for SageMaker to finish fine-tuning your models.

Analyze the model

When the fine-tuning is complete, you can view the stats about your new model, including:

Training loss – The penalty for each mistake in next-word prediction during training. Lower values indicate better performance.
Training perplexity – A measure of the model’s surprise when encountering text during training. Lower perplexity suggests higher model confidence.
Validation loss and validation perplexity – Similar to the training metrics, but measured during the validation stage.

To get a detailed report on your custom model’s performance across various dimensions, such as toxicity and accuracy, choose Generate evaluation report. Then select Download report.

Canvas offers a Python Jupyter notebook detailing your fine-tuning job, alleviating concerns about vendor lock-in associated with no-code tools and enabling detail sharing with data science teams for further validation and deployment.

If you selected multiple foundation models to create custom models from your dataset, check out the Model leaderboard to compare them on dimensions like loss and perplexity.

Test the models

You now have access to custom models that can be tested in SageMaker Canvas. Complete the following steps to test the models:

Choose Test in Ready-to-Use Models and wait 15–30 minutes for your test endpoint to be deployed.

This test endpoint will only stay up for 2 hours to avoid unintended costs.

When the deployment is complete, you’ll be redirected to the SageMaker Canvas playground, with your model pre-selected.

Choose Compare and select the foundation model used for your custom model.
Enter a phrase directly from your training dataset, to make sure the custom model at least does better at such a question.

For this example, we enter the question, “Who developed the lie-detecting algorithm Fraudoscope?”

The fine-tuned model responded correctly:

“The lie-detecting algorithm Fraudoscope was developed by Tselina Data Lab.”

Amazon Titan responded incorrectly and verbosely. However, to its credit, the model produced important ethical concerns and limitations of facial recognition technologies in general:

Let’s ask a question about an NVIDIA chip, which powers Amazon Elastic Compute Cloud (Amazon EC2) P4d instances: “How much memory in an A100?”

Again, the custom model not only gets the answer more correct, but it also answers with the brevity you would want from a question-answer bot:

“An A100 GPU provides up to 40 GB of high-speed HBM2 memory.”

The Amazon Titan answer is incorrect:

Deploy the model via Amazon Bedrock

For production use, especially if you’re considering providing access to dozens or even thousands of employees by embedding the model into an application, you can deploy the models as API endpoints. Complete the following steps to deploy your model:

On the Amazon Bedrock console, choose Foundation models in the navigation pane, then choose Custom models.
Locate the model with the prefix Canvas- with Amazon Titan as the source.

Alternatively, you can use the AWS Command Line Interface (AWS CLI): aws bedrock list-custom-models

Make note of the modelArn, which you’ll use in the next step, and the modelName, or save them directly as variables:

To start using your model, you must provision throughput.

On the Amazon Bedrock console, choose Purchase Provisioned Throughput.
Name it, set 1 model unit, no commitment term.
Confirm the purchase.

Alternatively, you can use the AWS CLI:

Or, if you saved the values as variables in the previous step, use the following code:

After about five minutes, the model status changes from Creating to InService.

If you’re using the AWS CLI, you can see the status via aws bedrock list-provisioned-model-throughputs.

Use the model

You can access your fine-tuned LLM through the Amazon Bedrock console, API, CLI, or SDKs.

In the Chat Playground, choose the category of fine-tuned models, select your Canvas- prefixed model, and the provisioned throughput.

Enrich your existing software as a service (SaaS), software platforms, web portals, or mobile apps with your fine-tuned LLM using the API or SDKs. These let you send prompts to the Amazon Bedrock endpoint using your preferred programming language.

The response demonstrates the model’s tailored ability to answer these types of questions:

“The lie-detecting algorithm Fraudoscope was developed by Tselina Data Lab.”

This improves the response from Amazon Titan before fine-tuning:

“Marston Morse developed the lie-detecting algorithm Fraudoscope.”

For a full example of invoking models on Amazon Bedrock, refer to the following GitHub repository. This repository provides a ready-to-use code base that lets you experiment with various LLMs and deploy a versatile chatbot architecture within your AWS account. You now have the skills to use this with your custom model.

Another repository that may spark your imagination is Amazon Bedrock Samples, which can help you get started on a number of other use cases.

Conclusion

In this post, we showed you how to fine-tune an LLM to better fit your business needs, deploy your custom model as an Amazon Bedrock API endpoint, and use that endpoint in application code. This unlocked the custom language model’s power to a broader set of people within your business.

Although we used examples based on a sample dataset, this post showcased these tools’ capabilities and potential applications in real-world scenarios. The process is straightforward and applicable to various datasets, such as your organization’s FAQs, provided they are in CSV format.

Take what you learned and start brainstorming ways to use custom AI models in your organization. For further inspiration, see Overcoming common contact center challenges with generative AI and Amazon SageMaker Canvas and AWS re:Invent 2023 – New LLM capabilities in Amazon SageMaker Canvas, with Bain & Company (AIM363).

About the Authors

Yann Stoneman is a Solutions Architect at AWS focused on machine learning and serverless application development. With a background in software engineering and a blend of arts and tech education from Juilliard and Columbia, Yann brings a creative approach to AI challenges. He actively shares his expertise through his YouTube channel, blog posts, and presentations.

Davide Gallitelli is a Specialist Solutions Architect for AI/ML in the EMEA region. He is based in Brussels and works closely with customer throughout Benelux. He has been a developer since very young, starting to code at the age of 7. He started learning AI/ML in his later years of university, and has fallen in love with it since then.

Organizations often offer support in multiple languages, saying “contact us for translations.” However, customers who don’t speak the predominant language often don’t know that translations are available or how to request them. This can lead to poor customer experience and lost business. A better approach is proactively providing information in multiple languages so customers can access it directly. This leads to more informed, satisfied, and included customers.

In this post, we share how we identified these challenges and overcame them through our work with Swindon Borough Council. We developed the Document Translation app, which uses Amazon Translate, to address these issues. The app is a business user app for self-serve translations. The app is created in partnership with Swindon Council and released as open source code freely available for your organization to use.

Translation challenges

We identified three key challenges:

Accuracy and quality
Cost to translate
Time to translate

Accuracy and quality

Translation accuracy and quality are critical, because the results must be accurate and understood. As quoted in the Swindon Borough Council case study:

“The council ran small-scale trials with the main digital translation providers that can support the different languages spoken by Swindon’s citizens. It recruited local bilingual volunteers to assess the quality of the machine translations against their first languages, and Amazon Translate came out on top.”

The Document Translation app uses Amazon Translate for performing translations. Amazon Translate provides high-quality document translations for contextual, accurate, and fluent translations. It supports many languages and dialects, providing broad coverage for customers worldwide. Custom terminology, a feature of Amazon Translate,is dynamically utilized by the app workflow when a language has matching custom terminology available.

Cost to translate

High costs of manual translation can prohibit organizations from supporting multiple languages, straining already tight budgets. Balancing language inclusivity and budget limitations poses a significant challenge when relying solely on traditional translation methods.

Swindon Borough Council paid around £159.81 ($194.32 USD) per single-page document, limiting them to providing translation only where legally required. As discussed in the case study, Swindon Borough Council slashed 99.96% of translation costs using Amazon Translate:

“Such dramatic savings mean that it’s no longer limited to translating only documents it is legally required to provide—it can offer citizens wider access to content for minimal extra cost.”

Customers report third-party translation services fees as a major cost. The neural machine translation technology of Amazon Translate dramatically lowers these costs.

Following the Cost Optimization pillar of the AWS Well-Architected Framework further led to implementing an AWS Graviton architecture using AWS Lambda and an infrequently accessed Amazon DynamoDB table class. With no server management overhead or continually running systems, this helps keep costs low.

Time to translate

Manual translation delays that lower customer satisfaction also include internal processes, approvals, and logistics arrangements in place to control costs and protect sensitive and private content. Swindon Borough Council stated that turnaround times could take up to 17 days:

“First, it was slow. The internal process required manual inputs from many different people. On average, that process took up to 12 days, and the time required by the translation agency was 3–5 days. That meant total translation time for a document was up to 17 days.”

This app offers a business user self-serve portal for document translations. Users can upload documents and download translations for sharing without slow manual intervention. Amazon Translate can perform translations in about 10 minutes.

Solution overview

The app’s business user portal is a browser-based UI that has been translated into all languages and dialects supported by Amazon Translate. The dynamic React UI doesn’t require server software. To accelerate development, UI components such as buttons and input boxes come from the AWS Cloudscape Design library. For interacting with AWS services, the AWS Amplify JS library for React simplifies the authentication, security, and API requests.

Fig.1 – Translating a document.

Fig.2 – Localized user interface.

Fig.3 – Client architecture overview.

The backend uses several serverless and event-driven AWS services, including AWS Step Functions for low-code workflows, AWS AppSync for a GraphQL API, and Amazon Translate. This architecture enables fast development and reduces ongoing management overhead, as shown in the following diagram.

Fig.4 – Translation architecture overview.

The app is built with Infrastructure as Code (IaC) using the AWS Cloud Development Kit (AWS CDK). The AWS CDK is an open source software development framework used to model and provision cloud applications. Using the Typescript CDK provides a reliable, repeatable, and extensible foundation for deployments. Paired with a consistent continuous integration and delivery (CI/CD) pipeline, deployments are predictable. Reusable components are extracted into constructs and imported where needed, providing consistency and best practices such as AWS Identity and Access Management (IAM) roles, Amazon CloudWatch logging, and AWS X-Ray tracing for all Lambda functions.

Fig.5 – Continuous integration and continuous delivery pipeline overview.

App deployment

The app is effortless to deploy using the AWS CDK. The AWS CDK allows modeling of the entire stack, including frontend React code, backend functions and workflows, and cloud infrastructure definitions packaged together.

Before deployment, review any prerequisites you may want to use, such as connecting this to your organization’s single sign-on with the SAML provider.

The installation wizard provides the necessary commands. AWS CloudShell allows you to run these commands without installing anything locally. The app documentation covers all advanced options available. Installation takes 30–60 minutes and is monitored from AWS CodePipeline.

Fig.6 – Installation wizard.

A self-paced Immersion Day is available for your technical teams to get hands-on experience with the services and build core components. Alternatively, your AWS account team can provide personalized guidance through the workshop.

Additional feature: Simply Readable

This app is designed with multiple features (as of this writing, Document Translation and Simply Readable). Simply Readable enables you to create Easy Read documents with generative artificial intelligence (AI) using Amazon Bedrock. The app can be installed with or without this feature.

Conclusion

The Document Translation app provides translations in your customers’ native languages. Amazon Translate enables accurate translation at scale. Communicating in customers’ languages shows respect, improves understanding, and builds trust.

Translation capabilities should be core to any growth strategy, building loyalty and revenue through superior localized experiences.

Business leaders should evaluate solutions like Amazon Translate to overcome language barriers and share their brand. Enabling multilingual communication conveys “We value you, we hear you, and we want your experience with us to be positive.”

To learn more about the app, see the FAQ.

About the Author

Philip Whiteside is a Solutions Architect (SA) at Amazon Web Services. Philip is passionate about overcoming barriers by utilizing technology.