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學生對 提供的 Machine Learning Data Lifecycle in Production 的評價和反饋

423 個評分
76 條評論


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...




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 is a very informative course. I learned a lot about data, metadata, schema and feature engineering, Also, Robert Crowe sir is a very good teacher.


76 - Machine Learning Data Lifecycle in Production 的 82 個評論(共 82 個)

創建者 Max A


The course is informative and well made, but the bugs in the grading algorithm are super annoying!

創建者 Merlin S


I​ts incomprehensible to me why this course has such a good average score.

創建者 Nithiwat S


The course is poorly prepared and presented. The instructor basically talks through slides with no concrete technical content, simply babling from one bullet point to another, from one slide to the next, unorganized. Lectures were horrible -- broad, technical content barely scratches the surface, uninteresting way to deliver and speak. This is a practical course. The intructor should have structured the lecture around a practical implementation through a real-life example. It's not there at all. Very difficult to continue listening and it's very frustrating. Lab and Assignments in Jupyter Notebook are good. Overall, a huge disappointment considering the first course in the Specialization taught by Andrew Ng was so good.

創建者 Chandramouli B


This course was nothing short of painful for someone who has had some industry experience, as well as som experience teaching. The video instructions were disjointed, unclear and did precious little to prepare one for the graded lab assignments. There was significant lack of cohesion between the ungraded and graded labs. Finally, the TFX ecosystem is esoteric, unnecessarily complex and a nightmare to use. As someone looking to adopt a data pipeline for their production ML model, all this course has done is convince me not to use TFX.

創建者 Panagiotis S


Very poor content. Also it was not engaging at all. The instructor was just reading the slides and gave only a slight explanation on more advanced concepts. Also the graded assignments were too easy and only focused on Tensorflow products which not everybody out there uses. Personally I was dissapointed that using ONLY tensorflow components that cannot be used alongside with other libraries like Pytorch or MXNet. Very dissapointing..

創建者 Germán G


Traté en varios navegadores de enviar mi trabajo para ser sometido a evaluación, sin éxito. No obtuve respuesta ni soporte.

El contenido es interesante pero el soporte y habilitación no está al nivel de lo requerido: es lamentable que no reembolsen.

創建者 Longlong F


The assignment grading system is not working. I submit the right expected answer but got 0/120. This is pretty annoying and discouraging.