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.
提供:


このコースについて
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
学習内容
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.
習得するスキル
- ML Metadata
- Convolutional Neural Network
- TensorFlow Extended (TFX)
- Data Validation
- Data transformation
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
提供:

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
シラバス - 本コースの学習内容
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.
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.
レビュー
- 5 stars63.19%
- 4 stars20%
- 3 stars9.57%
- 2 stars5.31%
- 1 star1.91%
MACHINE LEARNING DATA LIFECYCLE IN PRODUCTION からの人気レビュー
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.
The course is exciting. Lab and exercises are informative, but the answer to the quizzes are a little ambiguous.
This was my first course with Robert, which was a very pleasant experience.
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. :)
Machine Learning Engineering for Production (MLOps)専門講座について
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.

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