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Robert Crowe
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.
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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.
Read more
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
Register for this course and see more details by visiting:
OpenCourser.com/course/5ictpz/machine
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What's inside
Syllabus
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.
Read more
Syllabus
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.
Read more
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
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.
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches essential principles and best practices for building production-ready machine learning systems
Emphasizes practical aspects of machine learning engineering, including data collection, cleaning, feature engineering, model deployment, and monitoring
Leverages TensorFlow Extended (TFX), a popular open-source library for machine learning pipelines
Provides hands-on experience through interactive labs and exercises
Suitable for learners with a foundational understanding of machine learning and Python programming
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Reviews summary
Ml data lifecycle in production
Learners say this course provides largely positive information about the data lifecycle in production. Instructors Robert Crowe and Andrew Ng cover TensorFlow, TensorFlow Extended, ML Pipelines, debugging Jupyter and submission issues, and more. It's noted by students that the course includes engaging assignments, difficult exams, and helpful labs covering how to implement machine learning data pipelines using TensorFlow Extended, with some students recommending taking the assignments and ungraded labs seriously. Some students report that the course skips over a lot of detail and is overly focused on Tensorflow, with some instructors rated as tedious and dull. However, it's noted that the material is much more interesting in the next course and that the detailed walkthroughs on TensorFlow tools that manage schemas, metadata, and examples are especially appreciated.
Make the new concepts and code clear to the audience. Connect the examples to the previous way of working.
"Make the new concepts and code clear to the audience."
"Connect the examples to the previous way of working."
The detailed walkthroughs on TensorFlow tools that manage schemas, metadata, and examples are especially appreciated.
"The detailed walkthroughs on TensorFlow tools that manage schemas, metadata, and examples are especially appreciated."
The course takes an overall look over the general data life-cycle pipeline in production. The instructor, Robert Crowe (TF engineer), presented a plain domain of the studied subjects and was fully able to explain them understandably. The technologies and libraries presented through the course are modern and applicable to the majority of my current projects.
"The course takes an overall look over the general data life-cycle pipeline in production."
"The instructor, Robert Crowe (TF engineer), presented a plain domain of the studied subjects and was fully able to explain them understandably."
"The technologies and libraries presented through the course are modern and applicable to the majority of my current projects."
It skips over a lot of detail.
"It skips over a lot of detail."
Not engaging at all.
"Not engaging at all."
This course is not up to the usual level of Andrew Ng's specializations on Coursera. In my opinion, it needs a big review to order contents better.
"This course is not up to the usual level of Andrew Ng's specializations on Coursera."
"In my opinion, it needs a big review to order contents better."
I have no doubt in Robert's knowledge on the subject, but delivering clear instruction with just right amount of contexts is an art that takes another few years to master.
"I have no doubt in Robert's knowledge on the subject, but delivering clear instruction with just right amount of contexts is an art that takes another few years to master."
The videos were a bit repeatable and the content should be better organized.
"The videos were a bit repeatable and the content should be better organized."
I would really enjoy if we made it bottom up, and implement more stuff like this in pure python. Tensorflow is deprecated and very tiring. Very non pythonic.
"I would really enjoy if we made it bottom up, and implement more stuff like this in pure python. Tensorflow is deprecated and very tiring. Very non pythonic."
There is far too much focus on TensorFlow. The concepts in this course are important to know, however they are briefly introduced in the videos, which are followed by a TensorFlow coding lab.
"There is far too much focus on TensorFlow."
"The concepts in this course are important to know, however they are briefly introduced in the videos, which are followed by a TensorFlow coding lab."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Machine Learning Data Lifecycle in Production with these
activities:
Python Programming
Show steps
Ensure you have a strong foundation in Python programming before starting the course.
Browse courses on
Python
Show steps
-
Review the basics of Python syntax
-
Practice writing simple Python programs
-
Complete online tutorials or coding challenges
The Data Science Handbook
Show steps
Introduce yourself to critical ideas and concepts in the field of data science to build a strong foundation before the course begins.
View
The Data Science Handbook: Advice and Insights...
on Amazon
Show steps
-
Read chapters 1-3
-
Complete the exercises at the end of each chapter
-
Summarize the key points of each chapter
Data Lineage and Provenance
Show steps
Enhance your understanding of data lineage and provenance techniques to effectively manage data in production systems.
Browse courses on
Data Lineage
Show steps
-
Find online tutorials or articles on data lineage and provenance
-
Explore tools and frameworks for implementing data lineage and provenance
-
Discuss the benefits and challenges of data lineage and provenance
Six other activities
Expand to see all activities and additional details
Show all nine activities
TensorFlow Extended Exercises
Show steps
Sharpen your skills in using TensorFlow Extended for data pipelines and feature engineering.
Browse courses on
TensorFlow Extended
Show steps
-
Find online tutorials or exercises on TensorFlow Extended
-
Practice building data pipelines using TFX
-
Experiment with different feature engineering techniques
Data Cleaning and Transformation Discussion
Show steps
Engage with peers to discuss best practices and techniques for cleaning and transforming data.
Browse courses on
Data Cleaning
Show steps
-
Join or start a study group or online forum
-
Participate in discussions on data cleaning and transformation
-
Share your experiences and learn from others
Data Pipeline Project
Show steps
Apply your understanding of data pipelines by building one for a real-world dataset.
Browse courses on
Data Pipelines
Show steps
-
Choose a dataset and define your project goals
-
Design and implement a data pipeline using TFX
-
Evaluate the performance of your data pipeline
Contribute to TensorFlow Extended
Show steps
Deepen your understanding of TensorFlow Extended and contribute to the open-source community.
Browse courses on
Open Source
Show steps
-
Find a project on GitHub or other platforms where you can contribute
-
Read the documentation and familiarize yourself with the codebase
-
Submit a bug report or propose a feature addition
Advanced Machine Learning Engineering Workshop
Show steps
Attend a workshop to learn and practice advanced techniques in machine learning engineering.
Browse courses on
Machine Learning Engineering
Show steps
-
Research and find an advanced machine learning engineering workshop
-
Register for the workshop and prepare for the sessions
-
Actively participate in the workshop and engage with the instructors
Personal Data Science Project
Show steps
Apply your learnings by initiating a personal data science project to solidify your understanding and build a portfolio.
Browse courses on
Data Science Project
Show steps
-
Define the problem you want to solve or the question you want to answer
-
Gather and prepare the necessary data
-
Build and train a machine learning model
-
Evaluate the model's performance and iterate to improve it
Python Programming
Show steps
Ensure you have a strong foundation in Python programming before starting the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the basics of Python syntax
- Practice writing simple Python programs
- Complete online tutorials or coding challenges
The Data Science Handbook
Show steps
Introduce yourself to critical ideas and concepts in the field of data science to build a strong foundation before the course begins.
View
The Data Science Handbook: Advice and Insights...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3
- Complete the exercises at the end of each chapter
- Summarize the key points of each chapter
Data Lineage and Provenance
Show steps
Enhance your understanding of data lineage and provenance techniques to effectively manage data in production systems.
Browse courses on
Data Lineage
Show steps
Show steps
Show steps
- Find online tutorials or articles on data lineage and provenance
- Explore tools and frameworks for implementing data lineage and provenance
- Discuss the benefits and challenges of data lineage and provenance
Six other activities
Expand to see all activities and additional details
Show all nine activities
TensorFlow Extended Exercises
Show steps
Sharpen your skills in using TensorFlow Extended for data pipelines and feature engineering.
Browse courses on
TensorFlow Extended
Show steps
Show steps
Show steps
- Find online tutorials or exercises on TensorFlow Extended
- Practice building data pipelines using TFX
- Experiment with different feature engineering techniques
Data Cleaning and Transformation Discussion
Show steps
Engage with peers to discuss best practices and techniques for cleaning and transforming data.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Join or start a study group or online forum
- Participate in discussions on data cleaning and transformation
- Share your experiences and learn from others
Data Pipeline Project
Show steps
Apply your understanding of data pipelines by building one for a real-world dataset.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Choose a dataset and define your project goals
- Design and implement a data pipeline using TFX
- Evaluate the performance of your data pipeline
Contribute to TensorFlow Extended
Show steps
Deepen your understanding of TensorFlow Extended and contribute to the open-source community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find a project on GitHub or other platforms where you can contribute
- Read the documentation and familiarize yourself with the codebase
- Submit a bug report or propose a feature addition
Advanced Machine Learning Engineering Workshop
Show steps
Attend a workshop to learn and practice advanced techniques in machine learning engineering.
Browse courses on
Machine Learning Engineering
Show steps
Show steps
Show steps
- Research and find an advanced machine learning engineering workshop
- Register for the workshop and prepare for the sessions
- Actively participate in the workshop and engage with the instructors
Personal Data Science Project
Show steps
Apply your learnings by initiating a personal data science project to solidify your understanding and build a portfolio.
Browse courses on
Data Science Project
Show steps
Show steps
Show steps
- Define the problem you want to solve or the question you want to answer
- Gather and prepare the necessary data
- Build and train a machine learning model
- Evaluate the model's performance and iterate to improve it
Career center
Learners who complete Machine Learning Data Lifecycle in Production will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer applies engineering principles and methodologies to build, deploy, and maintain machine learning systems. Machine Learning Data Lifecycle in Production can help you enter or advance your career by laying out the essential steps to properly implement machine learning to get the most out of your data.
Machine Learning Architect
Machine Learning Architect
A Machine Learning Architect designs, builds, and manages machine learning systems. Machine Learning Data Lifecycle in Production can help you enter or advance your career as a Machine Learning Architect by providing you with the knowledge and skills to build and deploy robust and scalable machine learning systems.
Data Scientist
Data Scientist
A Data Scientist uses data to solve problems and make informed decisions. The skills taught in Machine Learning Data Lifecycle in Production, such as data gathering, cleaning, and validation, can help you excel in this role.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. Machine Learning Data Lifecycle in Production can help you enter or advance your career as a Software Engineer by providing you with the skills to build and deploy machine learning models in production.
Data Architect
Data Architect
A Data Architect designs and manages data systems and infrastructure. Machine Learning Data Lifecycle in Production may be useful to you by providing you with the skills to design and manage data systems that can support machine learning applications.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help organizations make informed decisions. Machine Learning Data Lifecycle in Production may be useful to you by providing you with the skills to handle and analyze large datasets, which is essential for success in this role.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data infrastructure and systems. This role requires a strong foundation in data management and engineering principles. Machine Learning Data Lifecycle in Production may be useful to you by providing insights into the lifecycle of data in production, helping you build more robust and reliable data systems.
Business Analyst
Business Analyst
A Business Analyst helps organizations to improve their performance by analyzing data and identifying opportunities for improvement. Machine Learning Data Lifecycle in Production may be useful to you by providing you with the skills to analyze data and identify trends that can help your organization make better decisions.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products and features. Machine Learning Data Lifecycle in Production may be useful to you by providing you with the skills to understand the technical aspects of machine learning and how it can be used to create successful products.
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. Machine Learning Data Lifecycle in Production may be useful to you by providing you with the skills to manage machine learning projects and ensure their successful delivery.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. Machine Learning Data Lifecycle in Production may be useful to you by providing you with the skills to manage and maintain databases that store machine learning data.
Data Governance Analyst
Data Governance Analyst
A Data Governance Analyst develops and implements policies and procedures for managing data. Machine Learning Data Lifecycle in Production may be useful to you by providing you with the skills to develop and implement data governance policies and procedures for machine learning data.
Data Quality Analyst
Data Quality Analyst
A Data Quality Analyst ensures that data is accurate, complete, and consistent. Machine Learning Data Lifecycle in Production may be useful to you by providing you with the skills to assess and improve the quality of machine learning data.
Data Privacy Analyst
Data Privacy Analyst
A Data Privacy Analyst ensures that data is collected and used in a compliant manner. Machine Learning Data Lifecycle in Production may be useful to you by providing you with the skills to understand and comply with data privacy regulations.
Data Security Analyst
Data Security Analyst
A Data Security Analyst protects data from unauthorized access, use, disclosure, disruption, modification, or destruction. Machine Learning Data Lifecycle in Production may be useful to you by providing you with the skills to secure machine learning data.
For more career information including salaries, visit:
OpenCourser.com/course/5ictpz/machine
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning Data Lifecycle in Production .
This classic textbook provides a comprehensive overview of statistical learning methods, including supervised and unsupervised learning. It offers a solid foundation in the theoretical concepts underlying machine learning, complementing the practical focus of this course.
This classic textbook provides a comprehensive overview of reinforcement learning, a powerful technique for training agents to make decisions in complex environments. It offers a solid foundation for those interested in exploring this advanced topic in machine learning.
Provides a comprehensive overview of machine learning and its applications, making it a great supplementary resource for this course. It covers the entire machine learning lifecycle, from data collection and preparation to model evaluation and deployment, with a focus on practical implementation.
Provides a rigorous foundation in machine learning from a probabilistic perspective. It covers topics such as Bayesian inference, graphical models, and reinforcement learning, offering a deeper understanding of the theoretical underpinnings of machine learning.
Provides a comprehensive overview of speech and language processing techniques. It covers topics such as speech recognition, natural language understanding, and text-to-speech synthesis, offering valuable insights for those interested in exploring this aspect of machine learning.
Save
Provides a comprehensive overview of computer vision algorithms and techniques. It covers topics such as image processing, feature detection, and object recognition, offering valuable insights for those interested in exploring this aspect of machine learning.
Provides a comprehensive overview of feature engineering, covering topics such as data exploration, feature selection, and feature transformation. It valuable resource for anyone looking to improve the performance of their ML models.
Provides a comprehensive overview of natural language processing techniques. It covers topics such as text preprocessing, feature extraction, and language modeling, offering valuable insights for those interested in exploring this aspect of machine learning.
While this course does not explicitly cover deep learning, this book valuable resource for those interested in exploring this topic further. It provides a comprehensive overview of deep learning concepts and techniques, with a focus on Python implementation.
Provides a practical introduction to machine learning with Scikit-Learn, Keras, and TensorFlow, covering topics such as data preprocessing, model training, and model evaluation. It valuable resource for anyone looking to get started with ML and these popular frameworks.
Provides a comprehensive overview of machine learning with R, covering topics such as data preprocessing, model training, and model evaluation. It valuable resource for anyone looking to get started with ML and R.
Provides a practical introduction to machine learning with Go, covering topics such as data preprocessing, model training, and model evaluation. It valuable resource for anyone looking to get started with ML and Go.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5ictpz/machine
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Effort
At the rate of 5 hours a week, it typically takes 4 weeks to complete this course.
Level
Advanced
Via
Coursera
Institution
DeepLearning.AI
Instructor
Robert Crowe
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Machine Learning Engineering for Production (MLOps). It's comprised of four courses:
- Machine Learning Modeling Pipelines in Production
- Deploying Machine Learning Models in Production
- Introduction to Machine Learning in Production
- Machine Learning Data Lifecycle in Production (viewing)
Good to know
Teaches essential principles and best practices for building production-ready machine learning systems
Emphasizes practical aspects of machine learning engineering, including data collection, cleaning, feature engineering, model deployment, and monitoring
Leverages TensorFlow Extended (TFX), a popular open-source library for machine learning pipelines
Provides hands-on experience through interactive labs and exercises
Suitable for learners with a foundational understanding of machine learning and Python programming
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