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As machine learning becomes increasingly integral to solving real-life problems, adopting an end-to-end, agile, and automated lifecycle is essential. We introduce MLOps (Machine Learning Operations), a tool designed to provide a structured approach to machine learning projects. This course could be a pivotal skill in your career, especially if you are a:
- Data Scientist responsible for developing and optimizing models;
- Data Engineer responsible for harvesting, managing, and storing data;
- Software Engineer responsible for developing services, APIs, and user interfaces;
- ML Engineer responsible for model production, scaling, packaging, and integration.
To ensure a comprehensive understanding and effective participation, here are some prerequisites:
- Understanding of ML Activity Division: ML activities should be categorized into two main phases: Build and Run. The Build phase involves building and testing the model, while the Run phase encompasses deploying, executing, and monitoring the model.
- Basic Knowledge of Core ML Activities: It’s important to have a fundamental understanding of Feature Engineering, training, and testing in the Build phase, as well as model deployment and inference in the Run phase. As illustrated, we will encapsulate these activities within the basics of MLOps, which will be covered in this course.
The course will dissect each activity in detail, covering its purpose, applicable techniques/tools, and best practices. Here’s what you’ll learn:
- Introduction to MLOps
- Requirements and Design
- Data Processing and Management
- Continuous Training
- Model Management
- Model Integration
So, without further ado, let’s embark on our journey from zero to hero in MLOps.