AWS MLOps is a game-changer for machine learning workflows. It allows you to automate the deployment, monitoring, and maintenance of your ML models.
By using AWS MLOps, you can reduce the time it takes to move a model from development to production by up to 90%. This is because AWS MLOps automates many of the manual processes involved in ML workflows.
AWS MLOps also provides a centralized platform for managing your ML models, making it easier to track their performance and make updates as needed.
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What is AWS MLOps
AWS MLOps is a concept that helps businesses get machine learning models to production faster and with higher success rates. It considers every stage of the ML lifecycle, from building to serving and monitoring ML models.
The right platform, processes, and people are essential for executing AWS MLOps well. This helps manage the additional complexity introduced when managing ML systems.
Executed well, AWS MLOps capabilities combine to reduce model performance degradation. This decreases overhead and operating costs for the business.
By using AWS MLOps, businesses can enable the use of advanced analytics and ML-powered decisioning. They can also unlock new revenue streams.
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AWS Services
AWS Services are the backbone of MLOps on Amazon Web Services. S3 is the object storage service that provides durability, scalability, and easy integration with other AWS services, making it an essential component for data management in ML workflows.
S3 can serve as a central repository for storing training data, model artifacts, and other large datasets. This makes it a great choice for storing and managing large amounts of data in your MLOps workflow.
ZenML integrates various AWS services, including S3, EC2, EKS, Sagemaker, and ECR, as stack components that you can compose into ZenML Stacks. This allows you to build a stack by choosing the services that best fit your needs, making it easy to switch services without having to change your code.
Elastic Container Registry (ECR)
ECR is AWS's fully-managed Docker container registry.
It can be used to store, manage, and deploy Docker images containing your pipeline code and associated dependencies, ensuring consistency across development, testing, and production environments.
You can choose ECR for the container registry stack component of your stack.
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AWS S3
AWS S3 is a central repository for storing training data, model artifacts, and other large datasets in MLOps.
It provides durability, scalability, and easy integration with other AWS services, making it an essential component for data management in ML workflows.
S3 can serve as a central repository for storing training data, model artifacts, and other large datasets in MLOps.
You can read more about S3 on the official User Guide.
S3 is an artifact store component in a ZenML Stack, which can be composed with other components like an orchestrator and a container registry.
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Elastic Compute Cloud
Elastic Compute Cloud is a game-changer for MLOps. EC2 offers resizable compute capacity in the cloud, making it easy to scale up or down as needed.
You can use EC2 instances for various tasks such as data preprocessing, model training, and inference. Its flexibility is a major plus, allowing you to choose the right instance type based on your computational needs.
EC2 instances can be CPU-optimized or GPU-accelerated, giving you the power to tackle complex tasks. You can even use an EC2 instance as an orchestrator stack component, where you'd run your pipelines and its steps.
Hosting other services on the VM is possible, but it would require some setup on your end.
Engineering Training Outline
To become proficient in MLOps Engineering on AWS, you'll need to master the skills outlined in the training outline.
The training outline includes four modules: Introduction to MLOps, Initial MLOps, Repeatable MLOps, and Model Monitoring and Operations.
Here's a breakdown of the modules:
- Module 1: Introduction to MLOps covers the basics of MLOps, including processes, people, technology, security and governance, and the MLOps maturity model.
- Module 2: Initial MLOps focuses on setting up experimentation environments in SageMaker Studio, including creating and updating a lifecycle configuration and provisioning a SageMaker Studio environment with the AWS Service Catalog.
- Module 3: Repeatable MLOps covers managing data for MLOps, version control of ML models, and code repositories in ML.
- Module 4: Model Monitoring and Operations includes hands-on labs on troubleshooting pipelines, monitoring ML models, and using Amazon SageMaker Model Monitor.
To complete the training, you'll need to have completed the AWS Technical Essentials course (Learning Tree course 1226) and have equivalent experience in DevOps Engineering on AWS or practical Data Science with Amazon SageMaker.
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Frequently Asked Questions
Is AWS SageMaker MLOps?
Yes, AWS SageMaker MLOps is a set of purpose-built tools for automating and standardizing machine learning operations. It helps streamline the ML lifecycle with automated processes.
What is the difference between MLOps and MLflow?
MLflow focuses on tracking hyperparameter tuning runs, while Azure MLOps provides a broader environment for managing the entire machine learning lifecycle. In essence, MLflow is a tool for logging and tracking, whereas Azure MLOps is a platform for end-to-end machine learning management.
Is MLOps the same as DevOps?
No, MLOps and DevOps are not the same, as MLOps focuses on machine learning while DevOps focuses on application development. While they share some similarities, MLOps requires specialized collaboration and processes for machine learning model development and deployment.
What is the difference between SageMaker and MLflow?
Amazon SageMaker is a fully managed service for scaling ML inference containers, while MLflow simplifies deployment with easy-to-use commands, streamlining the process without container definitions. This difference in focus enables SageMaker to handle large-scale container deployment, whereas MLflow excels in making deployment more accessible and user-friendly.
Does SageMaker use Kubeflow?
SageMaker integrates with Kubeflow, allowing you to leverage Kubeflow Pipelines to build and run SageMaker jobs and deployments. This integration simplifies the process of building and deploying machine learning models with SageMaker.
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