Optimizing your Machine Learning Project Lifecycle using MLOps
- Machine Learning
Data Science and Machine Learning models are increasingly being used to drive businesses forward; the real success is in operationalizing these models to increase availability and speed up time to value.
MLOps – a combination of Machine Learning, Data Engineering and DevOps – is an approach to automate and streamline the deployment and maintenance of ML models, enable greater collaboration between data scientists and operations teams and reliably deliver quality outputs to the business.
In this recorded webcast Thorogood Consultant Archana Krishna navigates you through the Machine Learning lifecycle, identifying key stages, exploring the value in developing MLOps processes and the common challenges encountered along the way. We also discuss approaches and tools that can be used to implement MLOps within AWS: model development using SageMaker, orchestration and deployment via the newly released SageMaker Pipelines, CloudWatch-based monitoring, integration with the AWS DevOps tools and others.
Optimizing Your Machine Learning Project Lifecycle Using MLOPS (24 minutes)
In this webcast we:
- Introduce MLOps and key building blocks
- Explore common challenges and considerations in the successful implementation of MLOps
- Discuss a framework to implement MLOps with AWS
Is it for you?
- Are you building Machine Learning models and evaluating the best way to productionize your solutions?
- Are you looking at increasing collaboration between your data teams, increasing effectiveness and developing a robust lifecycle for the deployment of Machine Learning models?
- Are you looking at developing an MLOps framework within the AWS data platform?