Unlocking the Power of Custom Containers on Amazon SageMaker for Streamlined ML Application Deployment

Unlocking the Power of Custom Containers on Amazon SageMaker for Streamlined ML Application Deployment

Unlocking the Power of Custom Containers on Amazon SageMaker for Streamlined ML Application Deployment

As Seen On

In the past few years, machine learning (ML) has marked its roots into an array of industries ranging from healthcare to retail, significantly solving complex business challenges of managing vast data and effective decision-making. The power enabling ML’s widespread adoption is the availability and versatility of platforms like Amazon SageMaker that aid in building, tuning, and deploying ML applications. One of its most potent features is the use of custom containers that allows a nuanced deployment of ML applications.

Amazon SageMaker is a fully-managed machine learning service equipped with multiple features, built-in algorithms, and pre-built docker images for swift and effective model deployment. But there are cases when the built-in features cannot suffice the customized needs of ML operations. This magnifies the role of the ‘Bring Your Own Containers’ (BYOC) concept in Amazon SageMaker, particularly to empower data scientists, developers, and ML experts who want to incorporate their unique ML frameworks, proprietary algorithms, or complex inference pipelines.

There are a number of scenarios where BYOC benefits a user. When an ML framework or library is not supported by Amazon SageMaker’s built-in algorithms or pre-built containers, a custom container comes to the rescue. Specific industries or domains frequently require specialized model architectures, or distinct pre-processing steps that might be lacking in the built-in Amazon SageMaker toolkit. Also, for deploying proprietary algorithms developed in-house, and when there’s a need to execute a series of complicated steps in a specific order under a complex business logic, BYOC steps in as a desirable approach.

An ideal illustration of leveraging this power of custom containers in Amazon SageMaker is the hosting of a serial inference ML application with real-time endpoints. Let’s consider an application where the first custom container is built using the latest Scikit-learn package, and the second deploys a pre-trained Xgboost model. The Scikit-learn container serves to transform raw data into featurized columns, while the Xgboost container hosts a model that makes predictions on the transformed data. Together they form a serial-inference pipeline, deployed on a real-time endpoint on Amazon SageMaker.

But why separate containers for the same inference application? The reasons are more practical than you might think. By using separate containers, you are essentially decoupling the two tasks – data transformation and data prediction. This brings about clarity of purpose and easier handling of underlying dependencies that each task requires. It also allows the use of specific fit-for-purpose frameworks, which may necessitate separate containers due to different dependencies involved. Another perk of using separate containers is resource isolation, providing flexibility and greater control. Lastly, functional isolation simplifies the process of maintenance and upgrades, ensuring that change in one area doesn’t inadvertently impact another.

Figuring out how to utilize Amazon SageMaker and its BYOC capabilities can unlock new opportunities and efficiencies. Building and hosting ML applications with custom containers not only streamline ML application deployment, but it also presents control back into the hands of developers and data scientists who aspire to build path-breaking ML solutions.

 
 
 
 
 
 
 
Casey Jones Avatar
Casey Jones
7 months ago

Why Us?

  • Award-Winning Results

  • Team of 11+ Experts

  • 10,000+ Page #1 Rankings on Google

  • Dedicated to SMBs

  • $175,000,000 in Reported Client
    Revenue

Contact Us

Up until working with Casey, we had only had poor to mediocre experiences outsourcing work to agencies. Casey & the team at CJ&CO are the exception to the rule.

Communication was beyond great, his understanding of our vision was phenomenal, and instead of needing babysitting like the other agencies we worked with, he was not only completely dependable but also gave us sound suggestions on how to get better results, at the risk of us not needing him for the initial job we requested (absolute gem).

This has truly been the first time we worked with someone outside of our business that quickly grasped our vision, and that I could completely forget about and would still deliver above expectations.

I honestly can't wait to work in many more projects together!

Contact Us

Disclaimer

*The information this blog provides is for general informational purposes only and is not intended as financial or professional advice. The information may not reflect current developments and may be changed or updated without notice. Any opinions expressed on this blog are the author’s own and do not necessarily reflect the views of the author’s employer or any other organization. You should not act or rely on any information contained in this blog without first seeking the advice of a professional. No representation or warranty, express or implied, is made as to the accuracy or completeness of the information contained in this blog. The author and affiliated parties assume no liability for any errors or omissions.