Notes about various data science, machine-learning, deep-learning APIs
Notebooks
Reference, Guides, Tutorials:
- TensorFlow Extended (TFX)
- TFRecord, tf.train.Example
- tf.Transform API
- TensorFlow Serving
- TensorFlow Probability
Book Material:
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
- Excellent foundation for both Scikit-Learn and Deep Learning, probably the best single resource there is.
- Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow
- Good introduction to TFX, though a bit behind the latest API versions.
- Bayesian Methods for Hackers
- Great introduction to probabilistic programming, especially the Bayesian approach.
Useful papers
Machine Learning Operations (MLOps): Overview, Definition, and Architecture
Challenges in Deploying Machine Learning: a Survey of Case Studies, v3 (2022)
TFX: A TensorFlow-Based Production-Scale Machine Learning Platform (2017)
Data Validation for Machine Learning (2019)
Hidden Technical Debt in Machine Learning Systems (2015)
Hidden technical debt in machine learning systems (日本語資料)
Machine Learning: The High Interest Credit Card of Technical Debt (2014)
AutoGraph: Imperative-style Coding with Graph-based Performance (2019)
TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines (2020)
Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX) (2021)
tf.data: A Machine Learning Data Processing Framework (2021)
TensorFlow: A system for large-scale machine learning (2016)