trainocate-gcp-training-b

GCPAMLTF - Advanced Machine Learning with TensorFlow on Google Cloud Platform

Overview

Duration: 5 days
This course will give you hands-on experience optimizing, deploying, and scaling a variety of production ML models. You'll learn how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text, along with recommendation systems.

Objectives

  • Implement the various flavors of production ML systems—static, dynamic, and continuous training; static and dynamic inference; and batch and online processing.
  • Solve an ML problem by building an end-to-end pipeline, going from data exploration, preprocessing, feature engineering, model building, hyperparameter tuning, deployment, and serving.
  • Develop a range of image classification models from simple linear models to high-performing convolutional neural networks (CNNs) with batch normalization, augmentation, and transfer learning.
  • Forecast time-series values using CNNs, recurrent neural networks (RNNs), and LSTMs.
  • Apply ML to natural language text using CNNs, RNNs, LSTMs, reusable word embeddings, and encoder-decoder generative models.
  • Implement content-based, collaborative, hybrid, and neural recommendation models in TensorFlow.

Audience

  • Data Engineers and programmers interested in learning how to apply machine learning in practice.
  • Anyone interested in learning how to leverage machine learning in their enterprise.

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

Prerequisites

To get the most out of this training, participants should have:
  • Knowledge of machine learning and TensorFlow to the level covered in Machine Learning on Google Cloud coursework.
  • Experience coding in Python.
  • Knowledge of basic statistics.
  • Knowledge of SQL and cloud computing (helpful).

Certification

This course is not associated with any certification.

Schedule

Show Schedule for:




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