Course Description

Course Overview

The Machine Learning on Google Cloud (MLGC) course is designed to provide individuals with the knowledge and skills necessary to leverage the machine learning capabilities of the Google Cloud Platform (GCP). This course focuses on the key concepts, tools, and best practices for building and deploying machine learning models on GCP using various machine learning services.

Prerequisites

To enroll in the MLGC course, participants should have a strong understanding of machine learning concepts, experience with Python programming language, and familiarity with basic statistics. Knowledge of 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 MLGC 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 GCP machine learning services are explained. They will also have access to resources and tools to gain practical experience in building 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 on GCP

Utilizing GCP’s AutoML for automated model development

Building custom machine learning models using TensorFlow on GCP

Training and evaluating models with different optimization algorithms and techniques

Deploying and Serving Machine Learning Models on GCP

Deploying machine learning models on GCP’s AI Platform

Utilizing Cloud Functions and Cloud Run for model serving

Implementing versioning, scaling, and monitoring for model deployment

Advanced Topics in Machine Learning on GCP

Implementing transfer learning and model fine-tuning

Leveraging GCP’s pre-trained models and APIs

Exploring advanced techniques for anomaly detection and recommendation systems

MLOps and Model Management

Managing machine learning models using Kubeflow and AI Platform Pipelines

Implementing continuous integration and deployment (CI/CD) for machine learning

Monitoring and managing deployed models for performance and scalability

Outcome

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

• Developed a comprehensive understanding of machine learning concepts and best practices on GCP
• Acquired practical knowledge in building and deploying machine learning models using various GCP services
• Gained expertise in data preprocessing, feature engineering, and model training on GCP
• Learned techniques for deploying and serving machine learning models on GCP
• Gained hands-on experience through practical labs and exercises
• Prepared to develop and deploy machine learning solutions using GCP’s machine learning capabilities

Labs

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

• Preprocessing and feature engineering of machine learning datasets on GCP
• Building and training machine learning models using GCP’s AutoML
• Developing custom machine learning models using TensorFlow on GCP
• Deploying and serving machine learning models on GCP’s AI Platform
• Implementing transfer learning and fine-tuning of pre-trained models on GCP
• Managing machine learning models and deploying CI/CD pipelines with Kubeflow

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