Machine Learning Engineer

Machine Learning Engineer

Become a production-ready Machine Learning Engineer by mastering ML engineering foundations, system design, feature pipelines, model training & evaluation, deployment strategies, scalable inference, and feature stores. Includes a hands-on capstone building a production ML system.

  • 4 Phases
  • 13 Lessons
  • 3.8 Hrs
  • Beginner Advanced
Category Cloud computing
Machine Learning Engineer
Learning Path 13 Lessons 3.8 Hrs

Start Phase
  • ML Engineering Overview 09:18
  • ML vs Data Science 10:55
  • ML System Design 51:47

Start Phase
  • Model Development 08:19
  • Python for ML Model Serving 20:04
  • Feature Engineering Pipelines 29:11
  • Model Evaluation & Validation 12.00

Start Phase
  • MLOps Foundations for Deployment & Scaling 50:10
  • Model Deployment Strategies 07:52
  • APIs for ML Models 19:26
  • Batch & Real-Time Inference 02.25
  • Feature Stores 02.11

Start Phase
  • Production ML System (Vertex AI Case Study) 09.10

Start your Machine Learning Engineer journey

Learn at your own pace. Total estimated time 3.8 hours

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