Introduction
Vertex AI, Google Cloud’s fully managed machine learning platform, offers powerful tools for data scientists to streamline the end-to-end machine learning lifecycle. This course is designed to guide data scientists through the entire workflow—from data preparation to model deployment and monitoring. By mastering Vertex AI, data scientists can efficiently manage large datasets, build and train models, and deploy them at scale. This course emphasizes practical, hands-on learning with real-world use cases to enhance your ability to create impactful machine learning solutions in a cloud environment.

Prerequisites

• Basic understanding of machine learning concepts and algorithms
• Experience with Python programming
• Familiarity with Google Cloud Platform (preferred)

Table of Contents

1. Introduction to Vertex AI for Data Scientists
   1.1 Overview of Vertex AI and Its Capabilities
   1.2 Key Features for Data Scientists
   1.3 Benefits of Using Vertex AI in Machine Learning Projects
   1.4 Real-World Applications of Vertex AI in Data Science
2. Data Preparation and Exploration
   2.1 Importing Data from Google Cloud Storage and BigQuery
   2.2 Preprocessing and Feature Engineering with Vertex AI
   2.3 Data Exploration and Visualization with Notebooks
   2.4 Data Cleaning Techniques for High-Quality Datasets
3. Model Training with Vertex AI
   3.1 Selecting and Preparing Data for Training
   3.2 Building and Customizing Models in Vertex AI
   3.3 Using AutoML for Custom Model Development
   3.4 Distributed Model Training for Large Datasets
   3.5 Hyperparameter Tuning and Optimization with Vertex AI
4. Using Pre-Trained Models and Transfer Learning
   4.1 Leveraging Pre-Trained Models for Faster Development
   4.2 Applying Transfer Learning to Custom Datasets
   4.3 Fine-Tuning Pre-Trained Models in Vertex AI
   4.4 Integrating Pre-Trained Models for Advanced NLP, CV, and Forecasting
5. Model Evaluation and Validation
   5.1 Evaluating Model Performance with Vertex AI Metrics
   5.2 Model Selection Based on Validation Results
   5.3 Cross-Validation and Testing for Generalization
   5.4 Bias Detection and Fairness Evaluation in Models
6. Model Deployment and Serving
   6.1 Deploying Models as Endpoints in Vertex AI(Ref: Vertex AI Pipelines: Automating and Managing ML Workflows )
   6.2 Setting Up Real-Time Inference with Vertex AI Prediction
   6.3 Batch Predictions and Integrating APIs
   6.4 Scaling Model Deployment for Production Use
7. Model Monitoring and Maintenance
   7.1 Tracking Model Performance Over Time
   7.2 Implementing Continuous Monitoring and Retraining
   7.3 Detecting Model Drift and Ensuring Stability
   7.4 Automating Retraining with Vertex AI Pipelines
8. Vertex AI Pipelines: Automating ML Workflows
   8.1 Introduction to Vertex AI Pipelines for Data Scientists
   8.2 Building End-to-End ML Pipelines for Automation
   8.3 Integrating Vertex AI Pipelines with GitOps and CI/CD
   8.4 Versioning and Reproducibility in Machine Learning Pipelines
9. Collaboration and Workflow Management
   9.1 Using Vertex AI Workbench for Collaborative Development
   9.2 Sharing Notebooks and Projects Across Teams
   9.3 Managing Large-Scale Data Science Projects with Vertex AI
   9.4 Collaboration with MLOps Teams for Streamlined Operations
10. Security, Privacy, and Compliance
    10.1 Managing Data Access and Permissions in Vertex AI
    10.2 Ensuring Model Security and Integrity
    10.3 Complying with Data Privacy Regulations in Machine Learning
    10.4 Secure Model Deployment and Inference
11. Real-World Data Science Projects with Vertex AI
    11.1 Predictive Analytics for Customer Segmentation
    11.2 Building a Recommender System with Vertex AI
    11.3 Natural Language Processing for Sentiment Analysis
    11.4 Computer Vision Models for Image Classification and Object Detection

Conclusion
By mastering Vertex AI, data scientists can effectively manage the entire machine learning lifecycle—from data preparation to model deployment and ongoing monitoring. This course equips you with the necessary skills to utilize Google Cloud’s powerful machine learning platform for building scalable, production-ready models. With hands-on experience in automating workflows, collaborating with cross-functional teams, and ensuring model accuracy and security, you’ll be well-prepared to take on complex data science challenges and contribute to innovative machine learning solutions in your organization. Vertex AI is your key to simplifying and optimizing the machine learning process in the cloud.

Reference