This course is part of the Machine Learning Engineering for Production (MLOps) Specialization

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Machine Learning Engineering for Production (MLOps) SpecializationDeepLearning.AI

About this Course

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In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.
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. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. 

Week 1: Overview of the ML Lifecycle and Deployment
Week 2: Selecting and Training a Model
Week 3: Data Definition and Baseline

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Course 1 of 4 in the

Machine Learning Engineering for Production (MLOps) Specialization

Advanced Level

• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)

Approx. 12 hours to complete

English

Subtitles: English, French

What you will learn

Identify the key components of the ML lifecycle and pipeline and compare the ML modeling iterative cycle with the ML product deployment cycle.
Understand how performance on a small set of disproportionately important examples may be more crucial than performance on the majority of examples.
Solve problems for structured, unstructured, small, and big data. Understand why label consistency is essential and how you can improve it.

Skills you will gain

Human-level Performance (HLP)
Concept Drift
Model baseline
Project Scoping and Design
ML Deployment Challenges

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 4 in the

Machine Learning Engineering for Production (MLOps) Specialization

Advanced Level

• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)

Approx. 12 hours to complete

English

Subtitles: English, French

Instructors

Instructor rating

4.96/5 (647 Ratings)

Andrew Ng
Top Instructor

Instructor

Founder, DeepLearning.AI & Co-founder, Coursera

5,975,237 Learners

38 Courses

Cristian Bartolomé Arámburu
Top Instructor

Curriculum Developer

54,868 Learners

1 Course

Offered by

DeepLearning.AI

DeepLearning.AI is an education technology company that develops a global community of AI talent.

Syllabus - What you will learn from this course

Content Rating97%(4,425 ratings)

Week

Week 1

5 hours to complete

Week 1: Overview of the ML Lifecycle and Deployment

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.

5 hours to complete

9 videos (Total 81 min), 2 readings, 4 quizzes

Week

Week 2

3 hours to complete

Week 2: Select and Train a Model

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.

3 hours to complete

16 videos (Total 107 min), 1 reading, 3 quizzes

16 videos

Modeling overview2m

Key challenges5m

Why low average error isn't good enough10m

Establish a baseline7m

Tips for getting started6m

Error analysis example8m

Prioritizing what to work on5m

Skewed datasets12m

Performance auditing7m

Data-centric AI development2m

A useful picture of data augmentation5m

Data augmentation8m

Can adding data hurt?6m

Adding features8m

Experiment tracking4m

From big data to good data3m

1 reading

Week 2 Optional References3m

2 practice exercises

Selecting and Training a Model10m

Modeling challenges10m

Week

Week 3

4 hours to complete

Week 3: Data Definition and Baseline

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.

4 hours to complete

16 videos (Total 128 min), 3 readings, 3 quizzes

16 videos

Why is data definition hard?4m

More label ambiguity examples9m

Major types of data problems11m

Small data and label consistency8m

Improving label consistency9m

Human level performance (HLP)10m

Raising HLP9m

Obtaining data12m

Data pipeline5m

Meta-data, data provenance and lineage9m

Balanced train/dev/test splits4m

What is scoping?2m

Scoping process6m

Diligence on feasibility and value14m

Diligence on value7m

Milestones and resourcing2m

3 readings

Week 3 Optional References3m

References5m

Acknowledgments3m

2 practice exercises

Data Stage of the ML Production Lifecycle20m

Scoping (optional)10m

Reviews

4.8

293 reviews

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TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING IN PRODUCTION

by IUDec 5, 2021

I have been involved with deep learning for more than 5 years (in academia), nevertheless learned a lot already. I am very curious about the next courses. Thanks for putting together this course!

by SPFeb 18, 2022

Concise and straight to the point! It is a good and broad introduction to the topic. I'm confident it has prepared me well for the next course of this specialization.

by TFAug 14, 2021

Excellent course, as always. Very well explain for both Data Sicientist, Software engineer and Manager (with some basics undertsanding of ML). One of these courses that Data Sientist should follow.

by DGAug 30, 2021

Excellent course, you learn about the fundamentals of MLOps. A recommended course if you want to understand the life cycle of a Machine Learning algorithm in production.

View all reviews

About the Machine Learning Engineering for Production (MLOps) Specialization

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.

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. The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.

Machine Learning Engineering for Production (MLOps)

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Introduction to Machine Learning in Production