A Practical Guide to Building, Automating, and Scaling Machine Learning Pipelines with Modern Tools and Best Practices
Sub Category
- Data Science
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Objectives
- Understand the core concepts, benefits, and evolution of MLOps.
- Learn the differences between MLOps and DevOps practices.
- Set up a version-controlled MLOps project using Git and Docker.
- Build end-to-end ML pipelines from data preprocessing to deployment.
- Transition ML models from experimentation to production environments.
- Deploy and monitor ML models for performance and data drift.
- Gain hands-on experience with Docker for ML model containerization.
- Learn Kubernetes basics and orchestrate ML workloads effectively.
- Set up local and cloud-based MLOps infrastructure (AWS, GCP, Azure).Troubleshoot common challenges in scalability, reproducibility, and reliability.
Pre Requisites
- Basic Python Programming Skills: Familiarity with Python syntax and scripting.
- Fundamentals of Machine Learning: Understanding of ML concepts like training, testing, and evaluation.
- Basic Knowledge of Data Science Tools: Exposure to Jupyter Notebooks or similar tools.
- Understanding of Version Control: Familiarity with Git for tracking code changes.
- Willingness to Learn Docker and Kubernetes: No prior experience needed, but a readiness to learn these tools is essential.
- Basic Command-Line Skills: Ability to navigate and execute commands in a terminal.
- Access to a Computer with Internet Connection: Suitable for running Docker and cloud services.
- Curiosity and Problem-Solving Mindset: Enthusiasm to troubleshoot and optimize workflows.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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