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Working with AWS ML Ops Services

Working with AWS ML Ops Services

Working with AWS ML Ops Services

 

The AWS MLOps Framework solution helps you operationalize Machine Learning Operations. The AWS tools provide a standard interface for managing ML pipelines for AWS services and third-party services. You can bring your own model, configure the orchestration of the pipeline, and monitor the pipeline’s operations.

Developers can create machine learning pipelines using an open-source toolset such as Apache Beam (originally a library for writing machine learning processes). Once your pipeline is complete, you can deploy it in the cloud in the AWS CloudFormation management console. The infrastructure-as-code approach allows developers to generate an appliance and publish it to the AWS Cloud.

AWS has optimized MLOps for Serverless Computing to make it easier to deploy and orchestrate machine learning models on AWS. You can use AWS Lambda for building and testing your models, or you can use AWS SageMaker for producing production-ready models from your models and deploying them in Amazon S3. You can also deploy the trained models in Amazon S3 and use Elastic MapReduce to load and validate your data.

Another advantage of deploying MLOps on AWS is that you can easily integrate models into the rest of the AWS stack, such as Amazon ECS for scaling, Amazon EMR for object storage, Amazon DynamoDB for analytics, and AWS Lambda and LambdaArray for serverless compute.

Additionally, you can combine your MLOps in Amazon EMR with Machine Learning Manager, enabling you to monitor your MLOps pipelines on a schedule easily. If you want to be able to update the cluster to use a new version of the ML model without restarting your entire cluster, you can combine Machine Learning Manager and MLOps into an API Gateway. This technique provides you with a consistent way to train your ML models and deploy them to the AWS Cloud.

Using the MLOps APIs, you can build automated, self-service ML pipelines for common machine learning operations such as classification, feature engineering, and regression testing.

At the lowest level, you can use pre-built pipelines that AWS has curated.

AWS has also made it easy to develop custom scripts for MLOps for AWS resources such as Elastic GPUs, Cloudwatch events, DynamoDB tables, AWS Lambda functions, and AWS S3 buckets. You can write a script that triggers a notification or a project or one that runs a Lambda function.

Like most of AWS’s ML capabilities, this solution is an entirely-free option. While AWS does offer a 30-day free trial, you still have to provide data. It also has commercial plans with several price tiers depending on the number of compute hours used.





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