Machine Learning Data Lifecycle in Production
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
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.
Syllabus
Syllabus - What you will learn from this course
Week 1
Week 1: Collecting, Labeling and Validating Data
Week 2
Week 2: Feature Engineering, Transformation and Selection
Week 3
Week 3: Data Journey and Data Storage
Week 4
Week 4 (Optional): Advanced Labeling, Augmentation and Data Preprocessing
FAQ
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:
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.
Reviews
The course is exciting. Lab and exercises are informative, but the answer to the quizzes are a little ambiguous.
Interesting material. There are quite a lot of typos and many code snippets are directly from the tfx manual pages however the instructions provided and logic of the course is clear.
It will be more interesting if unstructured data such as image, audio, ... is used more in the course.
instruction on debugging jupyter and submission issue is important for learners
