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.
本課程是 Machine Learning Engineering for Production (MLOps) 專項課程 專項課程的一部分
提供方


課程信息
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
您將學到的內容有
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.
您將獲得的技能
- 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)
提供方

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
授課大綱 - 您將從這門課程中學到什麼
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.
審閱
- 5 stars62.52%
- 4 stars20.04%
- 3 stars10.02%
- 2 stars5.21%
- 1 star2.20%
來自MACHINE LEARNING DATA LIFECYCLE IN PRODUCTION 的熱門評論
Very good training about data lifecycle for ML projects
Excellent course. Theory and practice well combined, to fit diverse curiositiy levels.
excellent course. Nice to see how we can detect data drift and skew drift
instruction on debugging jupyter and submission issue is important for learners
關於 Machine Learning Engineering for Production (MLOps) 專項課程
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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