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

4.4
stars
316 ratings

Robert Crowe

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Offered By

Machine Learning Engineering for Production (MLOps) SpecializationDeepLearning.AI

About this Course

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In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.
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: Neural Architecture Search
Week 2: Model Resource Management Techniques
Week 3: High-Performance Modeling
Week 4: Model Analysis
Week 5: Interpretability

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

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Course 3 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. 23 hours to complete

English

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What you will learn

Apply techniques to manage modeling resources and best serve batch and real-time inference requests.
Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.

Skills you will gain

Explainable AI
Fairness Indicators
automl
Model Performance Analysis
Precomputing Predictions

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 3 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. 23 hours to complete

English

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

Try Coursera for Business

Instructor

Instructor rating

4.31/5 (85 Ratings)

Robert Crowe

Instructor

TensorFlow Developer Engineer, Google

38,986 Learners

3 Courses

Offered by

DeepLearning.AI

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Syllabus - What you will learn from this course

Content Rating85%(1,870 ratings)

Week1

Week 1

6 hours to complete

Week 1: Neural Architecture Search

6 hours to complete

10 videos (Total 40 min), 4 readings, 6 quizzes

10 videos

Course Overview2mHyperparameter Tuning3mKeras Autotuner Demo6mIntro to AutoML5mUnderstanding Search Spaces2mSearch Strategies4mMeasuring AutoML Efficacy3mAutoML on the Cloud8mAssignment Setup1mWeek 1 Wrap Up19s

4 readings

Ungraded Lab - Intro to Keras Tuner45mUngraded Lab - Hyperparameter Tuning and Model Training with TFX1hNeural Architecture Search3mAutoML3m

2 practice exercises

Hyperparameter Tuning and Neural Architecture Search10mAutoML12m

Week2

Week 2

4 hours to complete

Week 2: Model Resource Management Techniques

4 hours to complete

13 videos (Total 91 min), 6 readings, 3 quizzes

13 videos

Dimensionality Effect on Performance8mCurse of Dimensionality9mCurse of Dimensionality: an Example4mManual Dimensionality Reduction5mManual Dimensionality Reduction: Case Study7mAlgorithmic Dimensionality Reduction 2mPrincipal Components Analysis7mOther Techniques7mMobile, IoT, and Similar Use Cases 7mBenefits and Process of Quantization8mPost Training Quantization4mQuantization Aware Training4mPruning12m

6 readings

Ungraded Lab - Manual Feature Engineering30mDimensionality Reduction Techniques3mUngraded Lab - Algorithmic Dimensionality Reduction40mQuantization3mPruning3mUngraded Lab - Quantization and Pruning45m

2 practice exercises

Dimensionality Reduction20mQuantization and Pruning20m

Week3

Week 3

5 hours to complete

Week 3: High-Performance Modeling

5 hours to complete

6 videos (Total 57 min), 4 readings, 4 quizzes

6 videos

Distributed Training10mHigh-Performance Ingestion11mTraining Large Models - The Rise of Giant Neural Nets and Parallelism13mTeacher and Student Networks3mKnowledge Distillation Techniques9mCase Study - How to Distill Knowledge for a Q&A Task8m

4 readings

Ungraded Lab - Distributed Strategies with TF and Keras45mHigh-Performance Modeling5mUngraded Lab - Knowledge Distillation45mKnowledge Distillation 2m

2 practice exercises

High-Performance Modeling20mKnowledge Distillation 20m

Week4

Week 4

4 hours to complete

Week 4: Model Analysis

4 hours to complete

12 videos (Total 69 min), 8 readings, 4 quizzes

12 videos

Model Performance Analysis7mIntroduction to TensorFlow Model Analysis6mTFMA in Practice3mModel Debugging Overview3mBenchmark Models1mSensitivity Analysis and Adversarial Attacks9mAdversarial Attack Demo4mResidual Analysis2mModel Remediation4mFairness4mMeasuring Fairness 5mContinuous Evaluation and Monitoring14m

8 readings

TensorBoard3mTensorFlow Model Analysis3mUngraded Lab - Tensorflow Model Analysis45mUngraded Lab - Model Analysis with TFX Evaluator45mSensitivity Analysis and Adversarial Attacks3mUngraded Lab - Fairness Indicators40mModel Remediation and Fairness5mContinuous Evaluation and Monitoring5m

3 practice exercises

Model Analysis8mModel Analysis and Debugging18mContinuous Evaluation and Monitoring8m

Reviews

4.4

59 reviews

5 stars
63.83%
4 stars
20.12%
3 stars
7.54%
2 stars
5.66%
1 star
2.83%

TOP REVIEWS FROM MACHINE LEARNING MODELING PIPELINES IN PRODUCTION

by JZApr 3, 2022

Lots of hands-on exercises accompanying knowledge learned in this course 3, but could be difficult for someone without prior working knowledge on Google Cloud platform/services.

by ADAug 7, 2021

Excellent!! Ver, Very Very Good. Learn a lot. Thank you for sharing.

by SDJan 5, 2023

Excellent introduction to pipelines for production ML.

by DWJun 8, 2022

Great course, probably the best in the specialisation.

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.

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Machine Learning Modeling Pipelines in Production

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