Machine Learning with TensorFlow on Google Cloud Platform

Agenda

1 - How Google Does Machine Learning

• Develop a data strategy around machine learning
• Examine use cases that are then reimagined through an ML lens
• Recognize biases that ML can amplify
• Leverage Google Cloud Platform tools and environment to do ML
• Learn from Google’s experience to avoid common pitfalls
• Carry out data science tasks in online collaborative notebooks
• Invoke pre-trained ML models from Cloud Datalab

2 - Launching into Machine Learning

• Identify why deep learning is currently popular
• Optimize and evaluate models using loss functions and performance metrics
• Mitigate common problems that arise in machine learning
• Create repeatable and scalable training, evaluation, and test datasets

3 - Intro to TensorFlow

• Create machine learning models in TensorFlow
• Use the TensorFlow libraries to solve numerical problems
• Troubleshoot and debug common TensorFlow code pitfalls
• Use tf_estimator to create, train, and evaluate an ML model
• Train, deploy, and productionalize ML models at scale with Cloud ML Engine

4 - Feature Engineering

• Turn raw data into feature vectors
• Preprocess and create new feature pipelines with Cloud Dataflow
• Create and implement feature crosses and assess their impact
• Write TensorFlow Transform code for feature engineering

5 - The Art and Science of ML

• Optimize model performance with hyperparameter tuning
• Experiment with neural networks and fine-tune performance
• Enhance ML model features with embedding layers
• Create reusable custom model code with the Custom Estimator