Course Description

Course Overview

The Machine Learning with TensorFlow on Google Cloud Platform (MLTF) course is designed to provide individuals with the knowledge and skills necessary to build and deploy machine learning models using TensorFlow on the Google Cloud Platform (GCP). This course focuses on the key machine learning concepts, tools, and best practices for developing and operationalizing machine learning solutions on GCP.

Prerequisites

To enroll in the MLTF course, participants should have a strong understanding of machine learning concepts, experience with Python programming language, and familiarity with basic statistics. Knowledge of TensorFlow and GCP fundamentals will be beneficial. Participants should have access to a GCP project or demo environment to practice the concepts covered in the course.

Methodology

The MLTF course follows a blended learning approach, combining theoretical instruction, demonstrations, discussions, and hands-on labs. Participants will engage in instructor-led sessions where machine learning concepts, best practices, and TensorFlow on GCP features are explained. They will also have access to resources and tools to gain practical experience in developing and deploying machine learning models. The course encourages active participation, discussions, and collaborative problem-solving to reinforce learning.

Course Outline

Introduction to Machine Learning on GCP

Overview of machine learning concepts and challenges

Understanding the benefits of machine learning on GCP

Exploring GCP machine learning services and tools

Data Preprocessing and Feature Engineering

Preparing data for machine learning models

Handling missing data, outliers, and categorical variables

Implementing feature engineering techniques for model performance improvement

Building and Training Machine Learning Models with TensorFlow

Understanding TensorFlow and its ecosystem

Designing and building machine learning models using TensorFlow

Training and evaluating models with different optimization algorithms and techniques

Scaling Machine Learning with Google Cloud ML Engine

Deploying machine learning models on Google Cloud ML Engine

Utilizing distributed training for large-scale models

Monitoring and managing model training jobs on GCP

Model Serving and Deployment with TensorFlow Serving and Cloud Functions

Serving machine learning models using TensorFlow Serving

Deploying models as serverless functions with Cloud Functions

Implementing versioning, scaling, and monitoring for model deployment

Advanced Topics in Machine Learning on GCP

Implementing transfer learning and model fine-tuning

Utilizing AutoML for automated model development

Leveraging GCP’s AI Platform for advanced machine learning tasks

Outcome

By the end of the MLTF course, participants will have:

• Developed a comprehensive understanding of machine learning concepts and best practices on GCP
• Acquired practical knowledge in developing and deploying machine learning models using TensorFlow on GCP
• Gained expertise in data preprocessing, feature engineering, and model training with TensorFlow
• Learned techniques for scaling machine learning models and managing training jobs on GCP
• Gained hands-on experience through practical labs and exercises
• Prepared to develop and operationalize machine learning solutions using TensorFlow on GCP

Labs

The MLTF course includes hands-on labs that provide participants with practical experience in developing and deploying machine learning models using TensorFlow on GCP. Some examples of lab exercises include:

• Preprocessing and feature engineering of machine learning datasets
• Building and training machine learning models using TensorFlow
• Deploying and scaling machine learning models on Google Cloud ML Engine
• Serving machine learning models with TensorFlow Serving
• Deploying machine learning models as serverless functions with Cloud Functions
• Implementing transfer learning and model fine-tuning with pre-trained models

These labs enable participants to apply the concepts learned in the course and gain hands-on experience in developing and deploying machine learning models using TensorFlow on GCP, allowing them to develop practical skills in machine learning on Google Cloud Platform.