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

4.6
stars
242 ratings

Laurence Moroney +1 more instructor

21,039 already enrolled

Offered By

Machine Learning Engineering for Production (MLOps) SpecializationDeepLearning.AI

About this Course

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In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally,  you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times.  
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: Model Serving Introduction
Week 2: Model Serving Patterns and Infrastructures
Week 3: Model Management and Delivery
Week 4: Model Monitoring and Logging

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 4 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. 31 hours to complete

English

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Skills you will gain

TensorFlow Serving
Model Monitoring
Model Registries
Machine Learning Operations (MLOps)
Generate Data Protection Regulation (GDPR)

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 4 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. 31 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.5/5 (66 Ratings)

Laurence Moroney

Instructor

Lead AI Advocate, Google

417,268 Learners

15 Courses

Robert Crowe

Instructor

TensorFlow Developer Engineer, Google

39,017 Learners

3 Courses

Offered by

DeepLearning.AI

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Syllabus - What you will learn from this course

Content Rating85%(1,082 ratings)

Week1

Week 1

4 hours to complete

Week 1: Model Serving: Introduction

4 hours to complete

7 videos (Total 34 min), 5 readings, 5 quizzes

7 videos

Course Overview4mIntroduction to Model Serving6mIntroduction to Model Serving Infrastructure5mDeployment Options3mImproving Prediction Latency and Reducing Resource Costs5mCreating and deploying models to AI Prediction Platform2mInstalling TensorFlow Serving6m

5 readings

Ungraded Labs - Best Practices5mUngraded Lab - Introduction to Docker20mOptional: Build, train, and deploy an XGBoost model on Cloud AI Platform45mUngraded Lab - Tensorflow Serving with Docker20mUngraded Lab - Serve a model with TensorFlow Serving30m

3 practice exercises

Introduction to Model Serving30mIntroduction to Model Serving Infrastructure30mTensorFlow Serving30m

Week2

Week 2

10 hours to complete

Week 2: Model Serving: Patterns and Infrastructure

10 hours to complete

8 videos (Total 44 min), 8 readings, 8 quizzes

8 videos

Model Serving Architecture4mModel Servers: TensorFlow Serving3mModel Servers: Other Providers5mScaling Infrastructure10mOnline Inference6mData Preprocessing4mBatch Inference Scenarios5mBatch Processing with ETL3m

8 readings

Documentation on model servers10mUngraded Lab - Deploy a ML model with FastAPI and Docker1hLearn about scaling with boy bands10mExplore Kubernetes and KubeFlow10m(Optional) Ungraded Lab: Intro to Kubernetes1h 10mUngraded Lab - Latency testing with Docker Compose and Locust45mData preprocessing10mUngraded Lab (Optional): Machine Learning with Apache Beam and TensorFlow45m

6 practice exercises

Model serving architecture30mScaling Infrastructure30mOnline Inference30mData Preprocessing30mBatch inference scenarios30mBatch Processing with ETL30m

Week3

Week 3

11 hours to complete

Week 3: Model Management and Delivery

11 hours to complete

9 videos (Total 82 min), 10 readings, 6 quizzes

9 videos

Experiment Tracking8mTools for Experiment Tracking7mIntroduction to MLOps11mMLOps Level 0 5mMLOps Levels 1&213mDeveloping Components for an Orchestrated Workflow13mManaging Model Versions7mContinuous Delivery7mProgressive Delivery7m

10 readings

Experiment Tracking10mMLOps Resources10mUngraded Lab: Intro to Kubeflow Pipelines2hArchitecture for MLOps using TFX, Kubeflow Pipelines, and Cloud Build10mUngraded Lab: Developing TFX Custom Components45mUngraded Lab - Model Versioning with TF Serving40mML Model Management10mUngraded Lab - CI/CD pipelines with GitHub Actions1hContinuous Delivery10mProgressive Delivery10m

3 practice exercises

ML Experiments Management and Workflow Automation30mMLOps Methodology30mModel Management and Deployment Infrastructure30m

Week4

Week 4

6 hours to complete

Week 4: Model Monitoring and Logging

6 hours to complete

13 videos (Total 66 min), 7 readings, 5 quizzes

13 videos

Why Monitoring Matters6mObservability in ML4mMonitoring Targets in ML4mLogging for ML Monitoring7mTracing for ML Systems3mWhat is Model Decay?3mModel Decay Detection2mWays to Mitigate Model Decay5mResponsible AI5mLegal Requirements for Secure and Private AI9mAnonymization and Pseudonymisation 4mRight to be Forgotten8mSpecialization recap and farewell1m

7 readings

Monitoring Machine Learning Models in Production10mAddressing Model Decay10mResponsible AI10mGDPR and CCPA10mCourse 4 Optional References10mAcknowledgements10m(Optional) Opportunity to Mentor Other Learners10m

3 practice exercises

Model Monitoring and Logging30mModel Decay30mGDPR and Privacy30m

Reviews

4.6

35 reviews

5 stars
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4 stars
20.90%
3 stars
3.27%
2 stars
2.45%
1 star
2.04%

TOP REVIEWS FROM DEPLOYING MACHINE LEARNING MODELS IN PRODUCTION

by WHSep 10, 2021

The most practical course for junior MLOPs engineers looking for the best productionization methodologie, and the tools that implement them.

by VLMay 16, 2022

It's intense, applied, concrete and to the point. A very good course.

by THDec 18, 2021

Very insightful, with a good high-level explanation of challenges surrounding model usage and deployments in a production environment.

by EBFeb 5, 2022

Really enjoyed it however to get he most out of it, the time commitment is large

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.

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Deploying Machine Learning Models in Production

About this Course

Skills you will gain