English | Size: 939 MB
Genre: eLearning
Machine Learning Engineer, Step into the cutting-edge field of machine learning engineering with this comprehensive track designed for aspiring professionals. This program teaches you everything you need to know about model deployment, operations, monitoring, and maintenance. In this track, you will learn the fundamentals of MLOps. You will work interactively with key technologies like Python, Docker, and MLflow. You will learn in detail about concepts such as CI/CD, deployment strategies, or concept drift. The track includes interactive courses and real-world projects that help you facilitate the skills learned. Upon completing this track, you’ll emerge as a well-rounded machine learning engineer with all the skills required for a junior machine learning engineer role. Note: Prior knowledge of concepts, including data manipulation, training, and evaluating machine learning models using Python, is expected from learners who enroll in this track.
What you’ll learn
Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.
Who this course is for
This track teaches fundamentals of MLOps, key technologies like Python, Docker, MLflow, deployment strategies, and monitoring which are all important skills for machine learning engineering careers.
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