Container management tools such as Kubernetes streamlines the development of applications; therefore, since its release ten years ago, it has been orchestrating containerised applications. Various organisations have embraced Kubernetes to train, evaluate, and deploy containerised machine learning models. 

However, delivering a machine learning model is cumbersome as it usually gets bulky. To address these challenges, AWS has introduced SageMaker for Kubernetes users to work with containerised machine learning models effortlessly.

Kubernetes’ Unique Challenges

Kubernetes facilitates a lot of features to the table such as portability, management, among others, but at the same time, it brings unique challenges associated with it. For one, the infrastructure requires additional controls such as optimising for utilisation, cost and performance, and more. 

Such practices divest time and resources, and in turn, slackens the process of developing for bringing products to market. However, the dearth of a solution that can handle such tasks and expedite the development has come to an end with the AWS announcement.

Integrating Amazon SageMaker and Kubernetes Workflows

While Amazon SageMaker Operators for Kubernetes is designed to bridge infrastructure gaps, as of now all the heavy lifting is done by AWS. The firm with this new offering integrates the Amazon SageMaker and Kubernetes workflows. 

AWS, with this solution, has now empowered developers who are using Kubernetes to call SageMaker quickly — a modular and fully-managed service that helps developers and data scientists to easier build, train, and deploy machine learning (ML) models at scale.

This will allow administrators to bring further flexibility while developing and delivering data science projects. Besides, it helps in extending the abilities of SageMaker, resulting in better management of the workflow through pre-configured and optimised computer resources.

Amazon SageMaker Operators for Kubernetes with TensorFlow:

1. Install Amazon SageMaker Operators for Kubernetes
2. Create a YAML config for training
3. Train the model using SageMaker Operator

Kubernetes and SageMaker Together: What It Means To Organisations

Machine learning is no more only for research and finding insights into data to make informed decisions; it is highly being deployed to in production for delivering superior solutions. And with by bringing Kubernetes and SageMaker together, data scientists will be able to use a fully managed services. 

Developers can extend the Kubernetes API by creating a custom resource that contains domain-specific logic and components. Besides, users can invoke these bespoke resources and automate workflows. Further, one can add specific resources in Kubernetes by installing SageMaker Operators. 

These are the following advantages:

1. Train: Developers can save 90% of the training cost using SageMaker, distribute training to reduce training time with multiple GPU nodes.
2. Tune: Automatically tune to optimise the hyperparameter by searching for the best range for accurate models, resulting in saving time while improving the model performance.
3. Interference: Get high performance and availability for real-time or batch processing for prediction.

Further, Developers and data scientists can use Kubernetes and interact with SageMaker training, tuning, and inference jobs natively. Even analysing logs becomes more accessible as the data is sent back to Kubernetes, allowing you to evaluate records in your command line.

Such flexibility and interoperability will enable firms to focus on development rather than managing the resources and other dependencies.

Outlook

In the microservice development landscape, Kubernetes won the container orchestration race from docker. Thus, it was essential to build services for assisting businesses in their development initiatives.

As Amazon is the leading cloud provider, it will be helpful for a plethora of organisations to leverage the service and allow them to gain operational resilience. With new advancements in machine learning, the development using microservices was getting increasingly tedious, but with Amazon SageMaker Operators firms can manage and develop products quickly.