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This blog post is co-written with Qaish Kanchwala from The Weather Company.

As industries begin adopting processes dependent on machine learning (ML) technologies, it is critical to establish machine learning operations (MLOps) that scale to support growth and utilization of this technology. MLOps practitioners have many options to establish an MLOps platform; one among them is cloud-based integrated platforms that scale with data science teams. AWS provides a full-stack of services to establish an MLOps platform in the cloud that is customizable to your needs while reaping all the benefits of doing ML in the cloud.

In this post, we share the story of how The Weather Company (TWCo) enhanced its MLOps platform using services such as Amazon SageMaker, AWS CloudFormation, and Amazon CloudWatch. TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. TWCo reduced infrastructure management time by 90% while also reducing model deployment time by 20%.

TWCo strives to help consumers and businesses make informed, more confident decisions based on weather. Although the organization has used ML in its weather forecasting process for decades to help translate billions of weather data points into actionable forecasts and insights, it continuously strives to innovate and incorporate leading-edge technology in other ways as well. TWCos data science team was looking to create predictive, privacy-friendly ML models that show how weather conditions affect certain health symptoms and create user segments for improved user experience.

TWCo was looking to scale its ML operations with more transparency and less complexity to allow for more manageable ML workflows as their data science team grew. There were noticeable challenges when running ML workflows in the cloud. TWCos existing Cloud environment lacked transparency for ML jobs, monitoring, and a feature store, which made it hard for users to collaborate. Managers lacked the visibility needed for ongoing monitoring of ML workflows. To address these pain points, TWCo worked with the AWS Machine Learning Solutions Lab (MLSL) to migrate these ML workflows to Amazon SageMaker and the AWS Cloud. The MLSL team collaborated with TWCo to design an MLOps platform to meet the needs of its data science team, factoring present and future growth.

Examples of business objectives set by TWCo for this collaboration are:

Functional objectives were set to measure the impact of MLOps platform users, including:

The solution uses the following AWS services:

The following diagram illustrates the solution architecture.

This architecture consists of two primary pipelines:

The proposed MLOps architecture includes flexibility to support different use cases, as well as collaboration between various team personas like data scientists and ML engineers. The architecture reduces the friction between cross-functional teams moving models to production.

ML model experimentation is one of the sub-components of the MLOps architecture. It improves data scientists productivity and model development processes. Examples of model experimentation on MLOps-related SageMaker services require features like Amazon SageMaker Pipelines, Amazon SageMaker Feature Store, and SageMaker Model Registry using the SageMaker SDK and AWS Boto3 libraries.

When setting up pipelines, resources are created that are required throughout the lifecycle of the pipeline. Additionally, each pipeline may generate its own resources.

The pipeline setup resources are:

The pipeline run resources are:

You should delete these resources when the pipelines expire or are no longer needed.

In this section, we discuss the manual provisioning of pipelines through an example notebook and automatic provisioning of SageMaker pipelines through the use of a Service Catalog product and SageMaker project.

By using Amazon SageMaker Projects and its powerful template-based approach, organizations establish a standardized and scalable infrastructure for ML development, allowing teams to focus on building and iterating ML models, reducing time wasted on complex setup and management.

The following diagram shows the required components of a SageMaker project template. Use Service Catalog to register a SageMaker project CloudFormation template in your organizations Service Catalog portfolio.

To start the ML workflow, the project template serves as the foundation by defining a continuous integration and delivery (CI/CD) pipeline. It begins by retrieving the ML seed code from a CodeCommit repository. Then the BuildProject component takes over and orchestrates the provisioning of SageMaker training and inference pipelines. This automation delivers a seamless and efficient run of the ML pipeline, reducing manual intervention and speeding up the deployment process.

The solution has the following dependencies:

In this post, we showed how TWCo uses SageMaker, CloudWatch, CodePipeline, and CodeBuild for their MLOps platform. With these services, TWCo extended the capabilities of its data science team while also improving how data scientists manage ML workflows. These ML models ultimately helped TWCo create predictive, privacy-friendly experiences that improved user experience and explains how weather conditions impact consumers daily planning or business operations. We also reviewed the architecture design that helps maintain responsibilities between different users modularized. Typically data scientists are only concerned with the science aspect of ML workflows, whereas DevOps and ML engineers focus on the production environments. TWCo reduced infrastructure management time by 90% while also reducing model deployment time by 20%.

This is just one of many ways AWS enables builders to deliver great solutions. We encourage to you to get started with Amazon SageMaker today.

Qaish Kanchwala is a ML Engineering Manager and ML Architect at The Weather Company. He has worked on every step of the machine learning lifecycle and designs systems to enable AI use cases. In his spare time, Qaish likes to cook new food and watch movies.

Chezsal Kamaray is a Senior Solutions Architect within the High-Tech Vertical at Amazon Web Services. She works with enterprise customers, helping to accelerate and optimize their workload migration to the AWS Cloud. She is passionate about management and governance in the cloud and helping customers set up a landing zone that is aimed at long-term success. In her spare time, she does woodworking and tries out new recipes while listening to music.

Anila Joshi has more than a decade of experience building AI solutions. As an Applied Science Manager at the AWS Generative AI Innovation Center, Anila pioneers innovative applications of AI that push the boundaries of possibility and guides customers to strategically chart a course into the future of AI.

Kamran Razi is a Machine Learning Engineer at the Amazon Generative AI Innovation Center. With a passion for creating use case-driven solutions, Kamran helps customers harness the full potential of AWS AI/ML services to address real-world business challenges. With a decade of experience as a software developer, he has honed his expertise in diverse areas like embedded systems, cybersecurity solutions, and industrial control systems. Kamran holds a PhD in Electrical Engineering from Queens University.

Shuja Sohrawardy is a Senior Manager at AWSs Generative AI Innovation Center. For over 20 years, Shuja has utilized his technology and financial services acumen to transform financial services enterprises to meet the challenges of a highly competitive and regulated industry. Over the past 4 years at AWS, Shuja has used his deep knowledge in machine learning, resiliency, and cloud adoption strategies, which has resulted in numerous customer success journeys. Shuja holds a BS in Computer Science and Economics from New York University and an MS in Executive Technology Management from Columbia University.

Francisco Calderon is a Data Scientist at the Generative AI Innovation Center (GAIIC). As a member of the GAIIC, he helps discover the art of the possible with AWS customers using generative AI technologies. In his spare time, Francisco likes playing music and guitar, playing soccer with his daughters, and enjoying time with his family.

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The Weather Company enhances MLOps with Amazon SageMaker - AWS Blog