The AWS MLOps Framework is a comprehensive solution designed to facilitate the implementation of Machine Learning Operations. This framework offers a standardized interface for managing ML pipelines across various AWS services and third-party platforms. Leveraging AWS, users can effortlessly incorporate their own models, configure pipeline orchestration, and efficiently monitor pipeline operations.
Developers have the flexibility to create machine learning pipelines using open-source toolsets like Apache Beam, which was initially developed as a library for writing machine learning processes. Once the pipeline development is complete, it can be deployed on the AWS CloudFormation management console, utilizing the infrastructure-as-code approach. This approach empowers developers to generate and publish applications or appliances seamlessly.
When it comes to building and testing models, AWS provides the option to use AWS Lambda. Alternatively, for production-ready models, AWS SageMaker can be utilized to train and deploy models on Amazon S3. Trained models can also be deployed on Amazon S3 and validated using Elastic MapReduce for data loading and verification.
One of the key advantages of deploying MLOps on AWS is the seamless integration with the wider AWS stack. Models can be easily integrated with other AWS services such as Amazon ECS for scaling, Amazon EMR for object storage, Amazon DynamoDB for analytics, and AWS Lambda and LambdaArray for serverless compute.
Furthermore, the combination of MLOps in Amazon EMR and Machine Learning Manager allows for convenient monitoring of MLOps pipelines through an intuitive schedule. To update the cluster with a new version of the ML model without restarting the entire cluster, Machine Learning Manager and MLOps can be combined into an API Gateway. This approach ensures a consistent method for training ML models and deploying them to the AWS Cloud.
By utilizing the MLOps APIs, users can build automated, self-service ML pipelines for common machine learning operations like classification, feature engineering, and regression testing. Additionally, AWS offers pre-built pipelines curated for easy use at a lower level.
AWS also simplifies the development of custom scripts for MLOps by providing access to resources such as Elastic GPUs, CloudWatch events, DynamoDB tables, AWS Lambda functions, and AWS S3 buckets. Users can write scripts to trigger notifications, initiate projects, or run Lambda functions.
While AWS's ML capabilities are largely available for free, including a 30-day trial period, data must still be provided. AWS also offers commercial plans with multiple price tiers based on the number of compute hours used.
