Description

Introduction of ML & AI with GCP

This course provides a comprehensive understanding of how to build, deploy, and manage machine learning (ML) and artificial intelligence (AI) models using Google Cloud Platform (GCP). Participants will explore the GCP tools and services for ML and AI, with a focus on practical applications such as data preparation, model training, deployment, and optimization. This course is designed for developers, data scientists, and ML engineers looking to leverage GCP’s powerful ML services to create scalable, high-performance AI solutions.

Prerequisites

Participants should have:

• A basic understanding of machine learning and AI concepts.
• Familiarity with Python and its ML libraries (e.g., TensorFlow, scikit-learn).
• Experience with Google Cloud Platform services.
• Knowledge of data engineering concepts and data preparation techniques.
• A basic understanding of cloud computing and big data concepts.

Table of Contents

1. Introduction to Machine Learning and AI on GCP
   1.1 Overview of GCP for Machine Learning and AI
   1.2 Core AI and ML Services on GCP
   1.3 The Machine Learning Workflow: Data to Deployment
2. Data Preparation for Machine Learning
   2.1 Data Collection and Cleaning with GCP Tools
   2.2 Data Preprocessing with BigQuery and Cloud Dataprep
   2.3 Feature Engineering and Transformation
   2.4 Storing and Managing Data with Cloud Storage and BigQuery
3. Building Machine Learning Models with GCP
   3.1 Introduction to TensorFlow on GCP(Ref: )
   3.2 Training Models with AI Platform
   3.3 Building Custom ML Models with AutoML
   3.4 Using BigQuery ML for In-Database Model Training
4. Deploying and Serving Machine Learning Models
   4.1 Deploying Models with AI Platform Predictions
   4.2 Serving Models with TensorFlow Serving
   4.3 Model Monitoring and Retraining Strategies
   4.4 Model Optimization and Scaling(Ref: Google Cloud Platform(GCP) for Developers: Building and Deploying Applications )
5. Automated Machine Learning (AutoML) on GCP
   5.1 Introduction to AutoML Tools on GCP
   5.2 Using AutoML for Image, Text, and Video Classification
   5.3 AutoML for Natural Language Processing
   5.4 Integrating AutoML with BigQuery ML
6. Natural Language Processing with GCP
   6.1 Introduction to NLP Services on GCP
   6.2 Using Cloud Natural Language API for Text Analysis
   6.3 Sentiment Analysis, Entity Recognition, and Syntax Analysis
   6.4 Building Custom NLP Models with AI Platform
7. Computer Vision with Google Cloud
   7.1 Overview of Google Cloud Vision API
   7.2 Image Recognition and Object Detection with Vision API
   7.3 Using AutoML Vision for Custom Image Models
   7.4 Building End-to-End Image Classification Pipelines
8. AI for Speech and Audio
   8.1 Speech-to-Text with Google Cloud Speech API
   8.2 Text-to-Speech with Cloud Text-to-Speech API
   8.3 Building Custom Speech Recognition Models
   8.4 Using AI for Audio Classification and Analysis
9. Machine Learning at Scale with GCP
   9.1 Distributed Machine Learning with TensorFlow on GCP
   9.2 Using Kubernetes for ML Workloads
   9.3 Leveraging TPUs for Accelerated Training
   9.4 BigQuery ML for Scalable Model Training
10. AI and ML Ethics on GCP
    10.1 Understanding Bias and Fairness in AI Models
    10.2 Ethical Considerations in Machine Learning
    10.3 Privacy and Security in AI Models on GCP
    10.4 Ensuring Compliance with Regulations
11. Machine Learning Monitoring and Optimization
    11.1 Monitoring ML Models in Production with AI Platform
    11.2 Optimizing Model Performance with Hyperparameter Tuning
    11.3 Continuous Model Evaluation and Improvement
    11.4 Managing Versioning and Rollback for ML Models
12. Advanced Machine Learning on GCP
    12.1 Deep Learning with TensorFlow on GCP
    12.2 Reinforcement Learning on GCP
    12.3 Advanced Natural Language Processing and GPT Models
    12.4 Building Custom End-to-End AI Solutions
13. Case Studies and Applications of AI on GCP
    13.1 Machine Learning for Retail Analytics
    13.2 AI for Predictive Maintenance in Manufacturing
    13.3 Healthcare Applications of AI and ML
    13.4 AI for Autonomous Vehicles and Robotics
14. Hands-On Labs and Projects
    14.1 Lab: Building a Predictive Model with BigQuery ML
    14.2 Lab: Deploying a TensorFlow Model on AI Platform
    14.3 Lab: Creating a Custom NLP Model with AutoML
    14.4 Lab: Image Classification with Cloud Vision API

Conclusion

Machine Learning and AI with Google Cloud Platform provides participants with the tools, knowledge, and hands-on experience necessary to develop and deploy AI models at scale. By leveraging GCP’s comprehensive suite of AI tools like TensorFlow, AutoML, and BigQuery ML, this course enables learners to build robust machine learning solutions across various industries, from predictive analytics to computer vision and natural language processing. With a focus on real-world applications, participants will leave with a deep understanding of how to effectively implement AI and machine learning models on Google Cloud Platform.

Reference