Description
Machine Learning in Production is a Machine Learning in Production course published by Coursera Academy. In this Machine Learning in Production course, you will develop an understanding of designing an end-to-end production ML system: project scope, data requirements, modeling strategies, and deployment patterns and technologies. You will learn strategies for addressing common challenges in production, such as establishing a model baseline, addressing conceptual drift, and performing error analysis. You will follow a framework for developing, deploying, and continuously improving a production ML application. Understanding machine learning and deep learning concepts is essential, but if you are looking to build an effective AI career, you will also need experience preparing your projects for deployment. Machine Learning Engineering for Production combines fundamental machine learning concepts with the skills and best practices of modern software development necessary to successfully deploy and maintain ML systems in real-world environments.
What you will learn
- Identify key components of the ML project lifecycle, pipeline, and select the best deployment and monitoring patterns for different production scenarios.
- Optimize model performance and metrics by prioritizing important outliers that represent key parts of a dataset.
- Solve production challenges around structured, unstructured, small, and big data, how label consistency is essential, and how you can improve it.
Who is this course suitable for?
- The Machine Learning in Production course is for machine learning professionals or software engineers who are looking to gain practical knowledge on how to formulate a repeatable, traceable, and verifiable machine learning project for production.
Machine Learning in Production Course Specifications
- Publisher: Coursera
- Instructor: Andrew Ng
- Language: English
- Education level: Intermediate
- Number of lessons: 3
- Training duration: about 11 hours
Machine Learning in Production Course Topics
Course prerequisites
- Some knowledge of AI / deep learning
- Intermediate Python skills
- Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Pictures
Sample video
Installation Guide
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Subtitles: English
Quality: 720p
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File size
607 MB
