Machine Learning in Production

Instructor: Andrew Ng

What you'll learn

  •   Identify key components of the ML project lifecycle, pipeline & select the best deployment & monitoring patterns for different production scenarios.
  •   Optimize model performance and metrics by prioritizing  disproportionately important examples that represent key slices of a dataset.
  •   Solve production challenges regarding structured, unstructured, small, and big data, how label consistency is essential, and how you can improve it.
  • Skills you'll gain

  •   Applied Machine Learning
  •   Artificial Intelligence and Machine Learning (AI/ML)
  •   Machine Learning
  •   Debugging
  •   Data Validation
  •   Feature Engineering
  •   Data Pipelines
  •   Continuous Monitoring
  •   Application Deployment
  •   MLOps (Machine Learning Operations)
  •   Data Quality
  •   Software Development Life Cycle
  •   Continuous Deployment
  • There are 3 modules in this course

    Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need experience preparing your projects for deployment as well. Machine learning engineering for production combines the foundational concepts of machine learning with the skills and best practices of modern software development necessary to successfully deploy and maintain ML systems in real-world environments. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Modeling Challenges and Strategies Week 3: Data Definition and Baseline

    Week 2: Modeling Challenges and Strategies

    Week 3: Data Definition and Baseline

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