Ever wondered how organizations build, deploy, maintain, adapt, retrain & redeploy large-scale AI-powered applications?
In today's fast-paced industry, maintaining & deploying scalable applications while being able to adapt quickly to the changing consumer requirements is of utmost importance.
Through this course, you will be introduced to some of the core ideas behind combining the long-established practices of DevOps with the emerging field of Machine Learning. You will be exposed to the various stages of the ML model lifecycle, including data versioning, experimentation, evaluation & monitoring. To consolidate these principles, you will also get an opportunity to build & deploy end-to-end ML pipelines by leveraging various ML Operations Management tools.
Pre-Requisites: Reasonable understanding of Python, Traditional ML Algorithms & Performance Metrics
Course Duration: 4 weeks
Time Commitment: 8-10 hrs per week (1-2 hrs more for optional material)
Week 1: Overview & Motivation for MLOps, ML Workflow Lifecycle, Real-world MLOps Case-Studies
Week 2: Dataset & Model Management using Data Version Control (DVC)
Week 3: ML Experimentation using PyCaret
Week 4: Deployment & Monitoring using PyCaret & MLFlow
Material (Tutorials, References & Documentation) to be released weekly, along with weekly tasks for students to implement all that they have learned through the week's material
Each of the 4 weeks will have certain assignments which build on top of the previous week's assignment. Through this series of 4 assignments, the audience can gain hands-on experience in creating, monitoring & versioning end-to-end ML pipelines using the libraries introduced to them during the respective weeks. Submission of all 4 weekly assignments is mandatory for obtaining a certificate since all these are integral to the learning intended from this course
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Created with ❤️ by WnCC