DeepLearning.AI
The Machine Learning in Production course covers how to conceptualize integrated systems that continuously operate in production as well as solve common challenges unique to the production environment. By the end of this program, you will be ready to design an ML production system end-to-end, establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
In this Machine Learning in Production course, you will build intuition about designing a production ML system end-to-end: project scoping, data needs, modeling strategies, and deployment patterns and technologies. You will learn strategies for addressing common challenges in production like establishing a model baseline, addressing concept drift, and performing error analysis. You’ll follow a framework for developing, deploying, and continuously improving a productionized ML application.
Data Scientists
Professionals looking to transition their machine learning models from research to production.
Machine Learning Engineers
Engineers aiming to enhance their skills in deploying and maintaining ML systems in real-world environments.
AI Enthusiasts
Individuals interested in understanding the end-to-end process of machine learning production systems.
This course will help you design an ML production system end-to-end, establish a model baseline, and continuously improve a productionized ML application. It is ideal for data scientists, machine learning engineers, and AI enthusiasts looking to transition their models from research to production.
1 / 3
Basic understanding of machine learning concepts
Familiarity with Python programming
Experience with data handling and preprocessing
Andrew Ng
Founder, DeepLearning.AI
Andrew Ng is best known as the founder of DeepLearning.AI, a company offering deep learning courses. He previously led the development of Google Brain, an artificial intelligence project at Google.
Cost
Free
Duration
Dates
Location
