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.
Machine Learning in Production
Gain insight into a topic and learn the fundamentals.
4.8
(3,077 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 11 hours
Learn at your own pace
98%
Most learners liked this course
What you'll learn
Identify key components of the ML project lifecycle, pipeline & select the best deployment & monitoring patterns for different production scenarios.
Optimize model performance and metrics by prioritizing disproportionately important examples that represent key slices of a dataset.
Solve production challenges regarding structured, unstructured, small, and big data, how label consistency is essential, and how you can improve it.
Skills you'll gain
Details to know
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Assessments
6 assignments
Taught in English
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There are 3 modules in this course
This week covers a quick introduction to machine learning production systems focusing on their requirements and challenges. Next, the week focuses on deploying production systems and what is needed to do so robustly while facing constantly changing data.
What's included
8 videos3 readings2 assignments1 app item2 ungraded labs
This week is about model strategies and key challenges in model development. It covers error analysis and strategies to work with different data types. It also addresses how to cope with class imbalance and highly skewed data sets.
What's included
16 videos2 readings2 assignments1 ungraded lab
This week is all about working with different data types and ensuring label consistency for classification problems. This leads to establishing a performance baseline for your model and discussing strategies to improve it given your time and resources constraints. This week also includes the final end-to-end project.
What's included
17 videos5 readings2 assignments2 ungraded labs
Instructor
Instructor ratings
4.9 (994 ratings)
Offered by
Recommended if you're interested in Machine Learning
Google Cloud
Duke University
Duke University
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Frequently asked questions
Machine learning engineering for production refers to the tools, techniques, and practical experiences that transform theoretical ML knowledge into a production-ready skillset.
Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles.
Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. With machine learning engineering for production, you can turn your knowledge of machine learning into production-ready skills.
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. In striking contrast with standard machine learning modeling, production systems need to handle evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance.
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.
By the end, you will be ready to:
Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
Build data pipelines by gathering, cleaning, and validating datasets.
Implement feature engineering, transformation, and selection with TensorFlow Extended.
Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.
Apply techniques to manage modeling resources and best serve offline/online inference requests.
Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.
Deliver deployment pipelines for model serving that require different infrastructures.
Apply best practices and progressive delivery techniques to maintain a continuously operating production system.