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

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403 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 second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.
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: Collecting, Labeling, and Validating data
Week 2: Feature Engineering, Transformation, and Selection
Week 3: Data Journey and Data Storage
Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Course 2 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. 22 hours to complete

English

Subtitles: English, French

What you will learn

Identify responsible data collection for building a fair ML production system.

Implement feature engineering, transformation, and selection with TensorFlow Extended

Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data.

Skills you will gain

ML Metadata
Convolutional Neural Network
TensorFlow Extended (TFX)
Data Validation
Data transformation

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 2 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. 22 hours to complete

English

Subtitles: English, French

Instructor

Instructor rating

4.17/5 (131 Ratings)

Robert Crowe

Instructor

TensorFlow Developer Engineer, Google

24,208 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%(2,210 ratings)

Week

Week 1

7 hours to complete

Week 1: Collecting, Labeling and Validating Data

This week covers a quick introduction to machine learning production systems. More concretely you will learn about leveraging the TensorFlow Extended (TFX) library to collect, label and validate data to make it production ready.

7 hours to complete

12 videos (Total 95 min), 3 readings, 6 quizzes

12 videos

Specialization overview6m

Course Overview2m

Overview11m

ML Pipelines6m

Importance of Data8m

Example Application: Suggesting Runs8m

Responsible Data: Security, Privacy & Fairness11m

Case Study: Degraded Model Performance9m

Data and Concept Change in Production ML5m

Process Feedback and Human Labeling11m

Detecting Data Issues7m

TensorFlow Data Validation6m

3 readings

Week 1 Optional References3m

How to Download your Notebook10m

Partial Grading for Assignments2m

4 practice exercises

Intro to MLEP12m

Data Collection15m

Data Labeling5m

Issues in Training Data10m

Week

Week 2

7 hours to complete

Week 2: Feature Engineering, Transformation and Selection

Implement feature engineering, transformation, and selection with TensorFlow Extended by encoding structured and unstructured data types and addressing class imbalances

7 hours to complete

12 videos (Total 86 min), 1 reading, 4 quizzes

12 videos

Introduction to Preprocessing5m

Preprocessing Operations6m

Feature Engineering Techniques10m

Feature Crosses3m

Preprocessing Data at Scale12m

TensorFlow Transform14m

Hello World with tf.Transform7m

Feature Spaces5m

Feature Selection4m

Filter Methods6m

Wrapper Methods5m

Embedded Methods5m

1 reading

Week 2 Optional References3m

3 practice exercises

Feature Engineering and Preprocessing15m

Feature Transformation15m

Feature Selection8m

Week

Week 3

5 hours to complete

Week 3: Data Journey and Data Storage

Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data.

5 hours to complete

8 videos (Total 42 min), 1 reading, 4 quizzes

Week

Week 4

3 hours to complete

Week 4 (Optional): Advanced Labeling, Augmentation and Data Preprocessing

Combine labeled and unlabeled data to improve ML model accuracy and augment data to diversify your training set.

3 hours to complete

6 videos (Total 31 min), 5 readings, 3 quizzes

Reviews

4.4

74 reviews

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3 stars
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2 stars
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TOP REVIEWS FROM MACHINE LEARNING DATA LIFECYCLE IN PRODUCTION

by BPMay 28, 2021

excellent course. Nice to see how we can detect data drift and skew drift

by PPJul 8, 2021

Very good training about data lifecycle for ML projects

by SSOct 12, 2021

The course is exciting. Lab and exercises are informative, but the answer to the quizzes are a little ambiguous.

by WRJul 26, 2021

It's a new course so sometimes there are mistakes in the translations or there is something off in the assignment's grading, but the content is great. :)

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 Data Lifecycle in Production