AWS Machine Learning Blog

Boomi uses BYOC on Amazon SageMaker Studio to scale custom Markov chain implementation

This post is co-written with Swagata Ashwani, Senior Data Scientist at Boomi.

Boomi is an enterprise-level software as a service (SaaS) independent software vendor (ISV) that creates developer enablement tooling for software engineers. These tools integrate via API into Boomi’s core service offering.

In this post, we discuss how Boomi used the bring-your-own-container (BYOC) approach to develop a new AI/ML enabled solution for their customers to tackle the “blank canvas” problem. Boomi’s machine learning (ML)-powered solution facilitates the rapid development of integrations on their platform, and enables faster time to market for their customers. Boomi funded this solution using the AWS PE ML FastStart program, a customer enablement program meant to take ML-enabled solutions from idea to production in a matter of weeks. Boomi built this solution using Amazon SageMaker Studio, an end-to-end browser-based IDE for AI/ML workloads, and Amazon Elastic Container Registry (Amazon ECR).

The blank canvas problem describes productivity and creativity issues faced by developers when starting a new task. An experienced developer knows at the onset of a new task what their code base will look like generally, but the process of building this code base is extensive and there’s no clear starting point. As the developer begins making progress on the blank canvas, their productivity is still low. The code written is usually boilerplate code providing the foundation for the business logic that can’t be written until most of the foundation is laid.

Boomi built a novel solution for the blank canvas problem using traditional development techniques. Boomi’s ML and data engineering teams needed the solution to be deployed quickly, in a repeatable and consistent way, at scale. The Boomi team uses the SageMaker BYOC paradigm to support their custom model. The Boomi team then used SageMaker Projects and SageMaker Pipelines to automate the training, testing, monitoring, and deployment of their custom model solution.

Customer use case

Markov chains are specialized structures for making predictive recommendations in a state machine. Markov chains are best known for their applications in web crawling and search engines. Boomi’s data science team implemented a Markov chain model that could be applied to common integration sequences, or steps, on their platform, hence the name Step Suggest.

Markov chains are built using a state machine and a probability matrix describing the likelihood of state transitions. Given a starting state, a Markov chain calculates the probability of a transition to another state allowed by the state machine. The data science team at Boomi applied the Markov Chain approach to the Step Suggest problem by treating integration steps as states in a state machine. Boomi’s Markov chain implementation takes the previous integration step and predicts the next integration step with significant accuracy.

Boomi had significant success with their application of Markov chains. However, the underlying algorithm for Step Suggest is complicated and proprietary. SageMaker has built-in support for several popular ML algorithms, but Boomi already had a working solution. Instead of starting from scratch, Boomi used the BYOC approach to import their existing models to SageMaker. As a result, Boomi’s team was able to use SageMaker for inference, CI/CD, and monitoring, without having to rebuild their Markov chain from scratch.

Solution details

The most important criteria for this solution were the reusability of existing models and the ease of deployment of those models to production. Boomi’s Step Suggest solution needs automated training and inference pipelines. At the time of the migration to SageMaker’s BYOC deployment model, Boomi’s solution was largely built and tested on individual laptops.

Boomi used Amazon ECR to store versions of their Step Suggest model. Amazon ECR stores and versions containerized applications in a container registry. The Boomi team built a Docker container with the model built off individual laptops and uploaded that container to an Amazon ECR domain. When the upload was complete, Boomi mounted the image to their SageMaker domain, where it could be imported and used for additional ML tasks like inference deployments to a hosted endpoint.

The exact steps to replicate this process are outlined Train and deploy deep learning models using JAX with Amazon SageMaker. This post discusses how to bring the JAX framework into your SageMaker domain. JAX is an up-and-coming ML framework for which SageMaker has no built-in support. Boomi implemented a similar workflow for their proprietary framework, extending the capabilities of their SageMaker deployment to satisfy the requirements of the Step Suggest project. There are a few prerequisites; proceed with the next steps before following the guide in the JAX post to practice the BYOC deployment paradigm with SageMaker.

Alternatives to SageMaker

Boomi was already an AWS customer before the AWS PE ML FastStart program. In fact, most of their data science team was using SageMaker notebook instances for model development. Data stored in Amazon Simple Storage Service (Amazon S3) trained models on notebook instances, which came pre-installed with the Jupyter Notebook software. This worked for model development, but Boomi needed a more robust solution to scale this workload to their customers.

The AWS PE ML FastStart program conducted a deep-dive session with Boomi’s data science and engineering teams. We decided SageMaker Studio would be a better solution for Boomi’s team to scale this solution quickly to their customers.

Why SageMaker?

SageMaker Studio brought several key advantages that SageMaker notebooks couldn’t do alone. First and foremost, Studio makes it easier to share notebook assets across a large team of data scientists like the one at Boomi. Boomi’s analysts were free to use SageMaker Data Wrangler for data preparation tasks, while Boomi’s data scientists could continue to use Jupyter notebooks. Most importantly, Studio maintained BYOC functionality. This was absolutely crucial because it meant the team could reuse the model assets they had already built.

Secondly, SageMaker Pipelines made it easy for Boomi’s team to visualize and modify their complex CI/CD requirements. The BYOC deployment paradigm requires additional integrations with Amazon ECR. To that end, the exact training and inference pipelines used by Boomi’s MLOps team necessitated additional steps for automated deployment, rollback, and monitoring. SageMaker Pipelines and the SageMaker StepFunctions SDK addressed this requirement.

Finally, SageMaker Projects presented the team with the ability to create AWS CloudFormation templates that standardized their ML development environments. Infrastructure as code (IaC) solutions like AWS CloudFormation reduce digital waste and standardize resource deployments in an AWS account. When CloudFormation templates are deployed through the AWS Service Catalog, as is done with SageMaker Projects, data science teams can operate freely without fear of violating any organization guardrails or best practices. Boomi’s cloud engineering team agreed this would be an important factor in scaling their data science team.

Feature deep dive

The following diagram illustrates the solution architecture and workflow.

Solution Architecture

The SageMaker BYOC paradigm enabled Boomi’s team to reuse a highly customized implementation of a proprietary ML algorithm. Boomi also had several custom preprocessing and postprocessing steps for their models. These proprietary steps allowed Boomi to bridge the gap between their data science and core product engineering teams. Implementing the processing logic within Studio, although possible, would be better suited for a built-in algorithm. The Studio BYOC paradigm enabled Boomi’s data science team to do what they did best without sacrificing speed and agility in their product’s development.

Because Boomi is a large organization with a strong cloud governance team, and because there are so many teams actively contributing to this project, having robust CI/CD is necessary. The CI/CD enabled by SageMaker Pipelines made it possible for the various contributing parties to collaborate. Boomi’s analysts contributed to preprocessing and postprocessing; the data science team customized, tuned, and built the model within a container; and the MLOps and systems engineering team were able to integrate Step Suggest into their core platform.


By leveraging Amazon SageMaker Studio, Amazon SageMaker Projects, and Amazon SageMaker Pipelines, Boomi made it easier to build MLOps Solutions at scale.

“AWS SageMaker Pipeline based solution has reduced the time needed to build, deploy, and manage our model by ~30%, thanks to its intuitive and user-friendly interface. By using this solution, we were able to deploy our model in just 4 weeks, 2x faster than if we had used traditional infrastructure.”

Boomi has an active relationship with their AWS account team. AWS account teams connect customers like Boomi with programs designed to address their business and technology needs. Connect with your AWS account team to learn more about programs like AWS PE ML FastStart to improve your time to market for new, innovative products built on or with AWS.

About the Authors

Dan Ferguson is an AI/ML Specialist Solutions Architect (SA) on the Private Equity Solutions Architecture at Amazon Web Services. Dan helps Private Equity backed portfolio companies leverage AI/ML technologies to achieve their business objectives.

Swagata Ashwani is a Senior Data Scientist at Boomi with over 6+ years experience in Data Science. Her interests include MLOps, natural language processing, and data visualization. She also actively engages herself in volunteering for Women in Data/AI and spreading more awareness and outreach within the AI community.
In her spare time she can be found plucking strums of her guitar, sipping masala chai and enjoying spicy Indian street food.