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

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stars
221 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

100% online

Start instantly and learn at your own schedule.

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. 26 hours to complete

English

Subtitles: English, French

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.

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. 26 hours to complete

English

Subtitles: English, French

Instructor

Instructor rating

4.47/5 (61 Ratings)

Robert Crowe

Instructor

TensorFlow Developer Engineer, Google

26,426 Learners

3 Courses

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 Rating85%(1,269 ratings)

Week

Week 1

7 hours to complete

Week 1: Neural Architecture Search

Learn how to effectively search for the best model that will scale for various serving needs while constraining model complexity and hardware requirements.

7 hours to complete

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

10 videos

Course Overview2m

Hyperparameter Tuning3m

Keras Autotuner Demo6m

Intro to AutoML5m

Understanding Search Spaces2m

Search Strategies4m

Measuring AutoML Efficacy3m

AutoML on the Cloud8m

Assignment Setup1m

Week 1 Wrap Up19s

4 readings

Ungraded Lab - Intro to Keras Tuner1h

Ungraded Lab - Hyperparameter Tuning and Model Training with TFX1h

Neural Architecture Search3m

AutoML3m

2 practice exercises

Hyperparameter Tuning and Neural Architecture Search10m

AutoML12m

Week

Week 2

5 hours to complete

Week 2: Model Resource Management Techniques

Learn how to optimize and manage the compute, storage, and I/O resources your model needs in production environments during its entire lifecycle.

5 hours to complete

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

13 videos

Dimensionality Effect on Performance8m

Curse of Dimensionality9m

Curse of Dimensionality: an Example4m

Manual Dimensionality Reduction5m

Manual Dimensionality Reduction: Case Study7m

Algorithmic Dimensionality Reduction 2m

Principal Components Analysis7m

Other Techniques7m

Mobile, IoT, and Similar Use Cases 7m

Benefits and Process of Quantization8m

Post Training Quantization4m

Quantization Aware Training4m

Pruning12m

6 readings

Ungraded Lab - Manual Feature Engineering30m

Dimensionality Reduction Techniques3m

Ungraded Lab - Algorithmic Dimensionality Reduction40m

Quantization3m

Pruning3m

Ungraded Lab - Quantization and Pruning1h

2 practice exercises

Dimensionality Reduction20m

Quantization and Pruning20m

Week

Week 3

5 hours to complete

Week 3: High-Performance Modeling

Implement distributed processing and parallelism techniques to make the most of your computational resources for training your models efficiently.

5 hours to complete

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

6 videos

Distributed Training10m

High-Performance Ingestion11m

Training Large Models - The Rise of Giant Neural Nets and Parallelism13m

Teacher and Student Networks3m

Knowledge Distillation Techniques9m

Case Study - How to Distill Knowledge for a Q&A Task8m

4 readings

Ungraded Lab - Distributed Strategies with TF and Keras1h

High-Performance Modeling5m

Ungraded Lab - Knowledge Distillation1h

Knowledge Distillation 2m

2 practice exercises

High-Performance Modeling20m

Knowledge Distillation 20m

Week

Week 4

6 hours to complete

Week 4: Model Analysis

Use model performance analysis to debug and remediate your model and measure robustness, fairness, and stability.

6 hours to complete

12 videos (Total 69 min), 8 readings, 5 quizzes

12 videos

Model Performance Analysis7m

Introduction to TensorFlow Model Analysis6m

TFMA in Practice3m

Model Debugging Overview3m

Benchmark Models1m

Sensitivity Analysis and Adversarial Attacks9m

Adversarial Attack Demo4m

Residual Analysis2m

Model Remediation4m

Fairness4m

Measuring Fairness 5m

Continuous Evaluation and Monitoring14m

8 readings

TensorBoard3m

TensorFlow Model Analysis3m

Ungraded Lab - Tensorflow Model Analysis1h

Ungraded Lab - Model Analysis with TFX Evaluator1h

Sensitivity Analysis and Adversarial Attacks3m

Ungraded Lab - Fairness Indicators1h

Model Remediation and Fairness5m

Continuous Evaluation and Monitoring5m

3 practice exercises

Model Analysis8m

Model Analysis and Debugging18m

Continuous Evaluation and Monitoring8m

Reviews

4.5

42 reviews

5 stars
69.74%
4 stars
17.64%
3 stars
6.72%
2 stars
3.36%
1 star
2.52%

TOP REVIEWS FROM MACHINE LEARNING MODELING PIPELINES IN PRODUCTION

by IAJun 14, 2022

There were a lot of useful information and practical insights about the subject of the course. The material on Tensorflow-specific modules felt a bit unorganized and cumbersome to go through.

by MBOct 20, 2021

I enjoyed this course a lot. It gave me a lot of ideas on how I can improve my models and make my workflow more efficient. Thank you.

by SMJun 7, 2022

The assignments are just quizes, and no practical programming exercise

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.

Machine Learning Modeling Pipelines in Production