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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Coursera Labs

Includes hands on learning projects.

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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. 11 hours to complete

English

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.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

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. 11 hours to complete

English

Instructors

Instructor rating

4.96/5 (806 Ratings)

Andrew Ng
Top Instructor

Instructor

Founder, DeepLearning.AI & Co-founder, Coursera

6,297,979 Learners

38 Courses

Cristian Bartolomé Arámburu
Top Instructor

Curriculum Developer

76,336 Learners

1 Course

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Content Rating98%(6,188 ratings)

Week1

Week 1

5 hours to complete

Week 1: Overview of the ML Lifecycle and Deployment

5 hours to complete

9 videos (Total 81 min), 2 readings, 5 quizzes

Week2

Week 2

3 hours to complete

Week 2: Select and Train a Model

3 hours to complete

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

16 videos

Modeling overview2mKey challenges5mWhy low average error isn't good enough10mEstablish a baseline7mTips for getting started6mError analysis example8mPrioritizing what to work on5mSkewed datasets12mPerformance auditing7mData-centric AI development2mA useful picture of data augmentation5mData augmentation8mCan adding data hurt?6mAdding features8mExperiment tracking4mFrom big data to good data3m

1 reading

Week 2 Optional References3m

2 practice exercises

Selecting and Training a Model10mModeling challenges10m

Week3

Week 3

4 hours to complete

Week 3: Data Definition and Baseline

4 hours to complete

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

16 videos

Why is data definition hard?4mMore label ambiguity examples9mMajor types of data problems11mSmall data and label consistency8mImproving label consistency9mHuman level performance (HLP)10mRaising HLP9mObtaining data12mData pipeline5mMeta-data, data provenance and lineage9mBalanced train/dev/test splits4mWhat is scoping?2mScoping process6mDiligence on feasibility and value14mDiligence on value7mMilestones and resourcing2m

3 readings

Week 3 Optional References3mReferences5mAcknowledgments3m

2 practice exercises

Data Stage of the ML Production Lifecycle20mScoping (optional)10m

Reviews

4.8

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TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING IN PRODUCTION

by DCMay 20, 2021

Practical and well-structured advices throughout the lifecycle of ML. Examples from real world problems & experiences make the advices more tangible and helps to reflect on own problems.

by GDMar 4, 2023

Good refresher if you already work in ML. A bit longish and could have been shortened.

I found the code provided useful to remind the use of Keras

In short, solid but not super mandatory

by EEMay 19, 2021

Excellent course, as always! Many thanks!

Great combination of theory + notebooks with practical examples.

Everything is perfectly structured. I will recommend this course to everyone!

by JFJun 12, 2021

The course helped both validate what I knew about the topic and update me about many new trends/tools via high quality references + first hand experences from the instructor.

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.
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

About this Course

What you will learn

Skills you will gain