Google Cloud -MLOps

MLOps

Machine learning operations (MLOps) is the practice of applying DevOps strategies to machine learning (ML) systems.

MLOps provides a set of standardized processes and technology capabilities for building, deploying, and operationalizing ML systems rapidly and reliably.

The MLOps lifecycle

MLOps LIfecycle

The processes can consist of the following:

• ML development: concerns experimenting and developing a robust and reproducible model training procedure (training pipeline code), which consists of multiple tasks from data preparation and transformation to model training and evaluation.
• Training operationalization: concerns automating the process of packaging, testing, and deploying repeatable and reliable training pipelines.
• Continuous training: concerns repeatedly executing the training pipeline in response to new data or to code changes, or on a schedule, potentially with new training settings.
• Model deployment: concerns packaging, testing, and deploying a model to a serving environment for online experimentation and production serving.
• Prediction serving: this is about serving the model that is deployed in production for inference.
• Continuous monitoring: is about monitoring the effectiveness and efficiency of a deployed model.
• Data and model management: is a central, cross-cutting function for governing ML artifacts to support audit-ability, traceability, and compliance. Data and model management can also promote the shareability, reusability, and discoverability of ML assets.

Benefits of MLOps

• Shorter development cycles, and as a result, shorter time to market.
• Better collaboration between teams.
• Increased reliability, performance, scalability, and security of ML systems.
• Streamlined operational and governance processes.
• Increased return on investment of ML projects.

If you want to know more, you can refer to the below docs

MLOps: Continuous delivery and automation pipelines in machine learning | Cloud Architecture Center…

Thank you :)