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DataNova Academy Learning Path

Machine Learning & MLOps

Move beyond experiments and learn how machine learning becomes reliable in production.

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Coming Soon

This learning experience is currently in development.

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What You'll Learn

  • Machine learning development workflows
  • Model packaging and deployment patterns
  • Monitoring, drift, and operational health
  • MLOps pipelines and automation
  • Scaling models responsibly in enterprise settings
  • Collaboration between data science and engineering

Who It's For

  • Data scientists moving toward production
  • ML and AI engineers
  • Platform teams supporting model operations
  • Technical leaders operationalizing ML

Skills You'll Gain

  • Production ML thinking
  • MLOps pipeline design
  • Model monitoring practices
  • Deployment and lifecycle management

Prerequisites

  • Foundational ML or applied AI familiarity
  • Comfort with Python recommended
  • Basic cloud awareness helpful

Expected Learning Outcomes

  • Operationalize machine learning systems
  • Design monitoring and lifecycle workflows
  • Reduce demo-to-production gaps
  • Communicate MLOps value to stakeholders

FAQ

Frequently Asked Questions

  • Is this the same as AI Engineering?

    They overlap, but this path focuses more on training, deploying, and operating machine learning models with MLOps discipline.

  • Do I need deep statistics knowledge?

    A working foundation helps. The emphasis is practical operationalization and reliable delivery.

  • When will this launch?

    This experience is coming soon. Join the waiting list for launch updates.

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