Content
The course includes presentations, demonstrations, and hands-on labs.
Module 1: Machine Learning on Google Cloud Platform
- Effective ML.
- Fully managed ML.
Module 2: Explore the Data
- Exploring the dataset.
- BigQuery.
- BigQuery and AI Platform Notebooks.
Module 3: Creating the dataset
- Creating a dataset.
Module 4: Build the Model
- Build the model.
Module 5: Operationalize the model
- Operationalizing the model.
- Cloud AI Platform.
- Train and deploy with Cloud AI Platform.
- BigQuery ML.
- Deploying and Predicting with Cloud AI Platform.
Module 6: Architecting Production ML Systems
- The Components of an ML System.
- The Components of an ML System: Data Analysis and Validation.
- The Components of an ML System: Data Transformation + Trainer.
- The Components of an ML System: Tuner + Model Evaluation and Validation.
- The Components of an ML System: Serving.
- The Components of an ML System: Orchestration + Workflow.
- The Components of an ML System: Integrated Frontend + Storage.
- Training Design Decisions.
- Serving Design Decisions.
- Designing from Scratch.
Module 7: Ingesting data for Cloud-based analytics and ML
- Data On-Premise.
- Large Datasets.
- Data on Other Clouds.
- Existing Databases.
Module 8: Designing Adaptable ML systems
- Adapting to Data.
- Changing Distributions.
- Right and Wrong Decisions.
- System Failure.
- Mitigating Training-Serving Skew through Design.
- Debugging a Production Model.
Module 9: Designing High-performance ML systems
- Training.
- Predictions.
- Why distributed training?
- Distributed training architectures.
- Faster input pipelines.
- Native TensorFlow Operations.
- TensorFlow Records.
- Parallel pipelines.
- Data parallelism with All Reduce.
- Parameter Server Approach.
- Inference.
Module 10: Hybrid ML systems
- Machine Learning on Hybrid Cloud.
- KubeFlow.
- Embedded Models.
- TensorFlow Lite.
- Optimizing for Mobile.
Module 11: Welcome to Image Understanding with TensorFlow on GCP
- Images as Visual Data.
- Structured vs Unstructured Data.
Module 12: Linear and DNN Models
- Linear Models.
- DNN Models Review.
- Review: What is Dropout?
Module 13: Convolutional Neural Networks (CNNs)
- Understanding Convolutions.
- CNN Model Parameters.
- Working with Pooling Layers.
- Implementing CNNs with TensorFlow.
Module 14: Dealing with Data Scarcity
- The Data Scarcity Problem.
- Data Augmentation.
- Transfer Learning.
- No Data, No Problem.
Module 15: Going Deeper Faster
- Batch Normalization.
- Residual Networks.
- Accelerators (CPU vs GPU, TPU).
- TPU Estimator.
- Neural Architecture Search.
Module 16: Pre-built ML Models for Image Classification
- Pre-built ML Models.
- Cloud Vision API.
- AutoML Vision.
- AutoML Architecture.
Module 17: Working with Sequences
- Sequence data and models.
- From sequences to inputs,
- Modeling sequences with linear models.
- Modeling sequences with DNNs.
- Modeling sequences with CNNs.
- The variable-length problem4m.
Module 18: Recurrent Neural Networks
- Introducing Recurrent Neural Networks.
- How RNNs represent the past.
- The limits of what RNNs can represent.
- The vanishing gradient problem.
Module 19: Dealing with Longer Sequences
- LSTMs and GRUs.
- RNNs in TensorFlow.
- Deep RNNs.
- Improving our Loss Function.
- Working with Real Data.
Module 20: Text Classification
- Working with Text.
- Text Classification.
- Selecting a Model.
- Python vs Native TensorFlow.
Module 21: Reusable Embeddings
- Historical methods of making word embeddings.
- Modern methods of making word embeddings.
- Introducing TensorFlow Hub.
- Using TensorFlow Hub within an estimator.
Module 22: Recurrent Neural NetworksEncoder-Decoder Models
- Introducing Encoder-Decoder Networks.
- Attention Networks.
- Training Encoder-Decoder Models with TensorFlow.
- Introducing Tensor2Tensor.
- AutoML Translation.
- Dialogflow.
Module 23: Recommendation Systems Overview
- Types of Recommendation Systems.
- Content-Based or Collaborative.
- Recommendation System Pitfalls.
Module 24:Content-Based Recommendation Systems
- Content-Based Recommendation Systems.
- Similarity Measures.
- Building a User Vector.
- Making Recommendations Using a User Vector.
- Making Recommendations for Many Users.
- Using Neural Networks for Content-Based Recommendation Systems.
Module 25:Collaborative Filtering Recommendation Systems
- Types of User Feedback Data.
- Embedding Users and Items.
- Factorization Approaches.
- The ALS Algorithm.
- Preparing Input Data for ALS.
- Creating Sparse Tensors For Efficient WALS Input.
- Instantiating a WALS Estimator: From Input to Estimator.
- Instantiating a WAL Estimator: Decoding TFRecords.
- Instantiating a WALS Estimator: Recovering Keys.
- Instantiating a WALS Estimator: Training and Prediction.
- Issues with Collaborative Filtering.
- Cold Starts.
Module 26:Neural Networks for Recommendation Systems
- Hybrid Recommendation System.
- Context-Aware Recommendation Systems.
- Context-Aware Algorithms.
- Contextual Postfiltering.
- Modeling Using Context-Aware Algorithms.
Module 27:Building an End-to-End Recommendation System
- Architecture Overview.
- Cloud Composer Overview.
- Cloud Composer: DAGs.
- Cloud Composer: Operators for ML9.
- Cloud Composer: Scheduling.
- Cloud Composer: Triggering Workflows with Cloud Functions.
- Cloud Composer: Monitoring and Logging