• Duration 10 weeks
  • Skill level Expert
  • User Avatar

    admin

  • Category:

    MLOps

₹15,000
  • english

Many organizations follow the process of build, test, and train ML models for their data science practice. Machine learning has been, and will continue to be, one of the biggest topics in data for the foreseeable future.

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MLOps

Many organizations follow the process of build, test, and train ML models for their data science practice. Machine learning has been, and will continue to be, one of the biggest topics in data for the foreseeable future. But the real challenge is providing continuous feedback once the models are in production.

Also, data scientists can’t be responsible for the management of an end-to-end machine learning pipeline. It would be best to have a team with the right mix of technical skillsets that manage the automation. Also, Data quality has a deep impact on your ML initiatives. Data quality will be the main concern of MLOps team, we may have to maintain a separate data quality team so this team focuses solely on data quality and is independent from ML, research, and data engineering teams.

MLOps is the framework to operationalize the ML model development process to have a continuous delivery cycle of models. MLOps automates the deployment and management of ML models into production in a scalable way to provide measurable business value.

MLOps infrastructure Representation

Based on an organization’s maturity level, their MLOps infrastructure can be represented by something as simple as this:

  • How models are created, trained and approved
  • Where models are stored
  • How models are deployed
  • How models are evaluated and monitored in production
  • How models are either corrected or removed from the production environment to minimize risks
  • How these processes repeat and intertwine to enable a cyclical machine learning operations process

Benefits of implementing MLOps

  • Delivers business values for data science projects
  • Improves the efficiency of the data science team
  • Allows machine learning models to run more predictably with better results
  • Helps enterprises to improve revenue and operational efficiency
  • Accelerates your digital transformation journey

MLOps deals with the MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

MLOps team should have good understanding of
  • MLOps Fundamentals
  • Why and When to Employ MLOps
  • Understanding the main Kubernetes components (Optional)
  • AI Platform Pipelines that makes MLOps easy, seamless and scalable
  • Training, Tuning and Serving on AI Platform
  • Kubeflow Pipelines on AI Platform
  • CI/CD for Kubeflow Pipelines on AI Platform

Concretely, this means that ML Ops incorporates tasks such as:

  • Version control of any code used for data transformations and model definitions
  • Automated testing of the ingested data and model code before going into production
  • Deployment of the model in production in a stable and scalable environment
  • Monitoring of the model performance and output

MLOps team should have good understanding of

  • 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

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