Architecting Production-ready ML Models Using Google Cloud ML Engine
This course covers Cloud ML Engine, a powerful service that supports distributed training and evaluation for models built in TensorFlow, Scikit-learn and XGBoost.
What you'll learn
Building machine learning models using Python and a machine learning framework is the first step towards building an enterprise-grade ML architecture, but two key challenges remain: training the model with enough computing firepower to get the best possible model and then making that model available to users who are not data scientists or even Python users. In this course, Architecting Production-ready ML Models Using Google Cloud ML Engine, you will gain the ability to perform on-cloud distributed training and hyperparameter tuning, as well as learn to make your ML models available for use in prediction via simple HTTP requests. First, you will learn to use the ML Engine for models built in XGBoost. XGBoost is an ML framework that utilizes a technique known as Ensemble Learning to construct a single, strong model by combining several weak learners, as they are known. Next, you will discover how easy it is to port serialized models from on-premise to the GCP. You will build a simple model in scikit-learn, which is the most popular classic ML framework, and then serialized that model and port it over for use on the GCP using ML Engine. Finally, you will explore how to tap the full power of distributed training, hyperparameter tuning, and prediction in TensorFlow, which is one of the most popular libraries for deep learning applications. You will see how a JSON environment variable called TF_CONFIG is used to share state information and optimize the training and hyperparameter tuning process. When you’re finished with this course, you will have the skills and knowledge of the Google Cloud ML Engine needed to get the full benefits of distributed training and make both batch and online prediction available to your client apps via simple HTTP requests.
Table of contents
- Module Overview 1m
- Implementing Models in XGBoost 6m
- Enabling ML Engine APIs, Creating Service Account Keys and Storage Buckets 7m
- Implementing a Simple XGBoost Model in Python 7m
- XGBoost Model: Train Locally 3m
- XGBoost Model: Train on the Cloud 5m
- XGBoost Model: Examine Results 3m
- XGBoost Model: Deploy Using the Web Console 3m
- XGBoost Model: Access Using Python Libraries 5m
- Module Overview 2m
- Implementing a Simple Regression Model Using Scikit-learn in Python 6m
- Scikit-learn Model: Train on the Cloud 4m
- Scikit-learn Model: Deploy Using the gcloud Command Line Utility 4m
- Scikit-learn Model: Share Model with Other Users Using Role Based Access Control 4m
- Build and Train a Scikit-learn Model on Kaggle 3m
- Deploy On-premise Model Using ML Engine 2m
- Summary 1m
- Module Overview 1m
- ML Engine and Tensor Flow 4m
- Hyperparameter Tuning 3m
- Implementing task.py for Distributed Training 6m
- Implementing model.py for Classification Using TensorFlow Estimators 5m
- TensorFlow Model: Train Locally and on the Cloud 6m
- TensorFlow Model: Simulate Distributed Training, Run Distributed Training with Multiple Workers 4m
- TensorFlow Model: Hyperparameter Tuning 4m
- TensorFlow Model: Online and Batch Predictions 4m
- Summary and Further Study 1m