Description

Introduction

The SnowPro® Advanced Data Scientist Training is designed for professionals who want to leverage Snowflake’s advanced analytics and machine learning (ML) capabilities for building scalable AI-driven solutions. This course covers Snowpark, Python-based ML models, feature engineering, and real-time predictive analytics using Snowflake’s native ML functions and integrations with external ML frameworks. Participants will gain hands-on experience in data preparation, model training, deployment, and optimization within Snowflake’s cloud ecosystem.

Prerequisites of SnowPro®

• SnowPro Core Certification (recommended but not mandatory)
• Strong understanding of SQL, Python, and data science concepts
• Familiarity with machine learning frameworks (Scikit-learn, TensorFlow, PyTorch)
• Experience with data engineering, ETL pipelines, and statistical modeling

Table of Contents

1. Snowflake for Data Science and ML

• 1.1 Snowflake’s Architecture for Data Science Workflows
• 1.2 Snowpark and Native ML Capabilities
• 1.3 Integration with External ML and AI Frameworks

2. Data Preparation and Feature Engineering

• 2.1 Handling Structured and Semi-Structured Data in Snowflake
• 2.2 Feature Extraction, Transformation, and Selection
• 2.3 Automating Data Cleansing and Normalization

3. Machine Learning Model Development in Snowflake

• 3.1 Implementing ML Models Using Snowpark and Python
• 3.2 Supervised vs. Unsupervised Learning in Snowflake
• 3.3 Hyperparameter Tuning and Model Selection

4. Model Deployment and Inference

• 4.1 Deploying ML Models Directly in Snowflake(Ref: SnowPro® Advanced Data Scientist Recertification Exam Certification Training)
• 4.2 Real-Time Predictions and Batch Inference Pipelines
• 4.3 Using UDFs for ML Model Serving and Scalability

5. Advanced Predictive Analytics

• 5.1 Time-Series Forecasting with Snowflake ML Functions
• 5.2 Anomaly Detection and Outlier Analysis
• 5.3 Customer Segmentation and Recommendation Systems

6. AI-Driven Data Pipelines and Automation

• 6.1 Automating Model Training with Snowflake Tasks and Streams
• 6.2 CI/CD Pipelines for ML Model Updates
• 6.3 Using Snowflake’s REST API for ML Workflow Orchestration

7. Deep Learning and NLP with Snowflake

• 7.1 Implementing Neural Networks and Deep Learning Models
• 7.2 Natural Language Processing (NLP) and Text Analytics in Snowflake
• 7.3 Sentiment Analysis and Chatbot AI Use Cases

8. Data Science at Scale: Performance Optimization

• 8.1 Optimizing Query Performance for ML Workloads
• 8.2 Managing Compute Costs in Large-Scale ML Projects
• 8.3 Scaling ML Models with Snowflake’s Multi-Cluster Warehouses

9. Security, Compliance, and Ethical AI

• 9.1 Data Privacy and Secure ML Model Deployments
• 9.2 Role-Based Access Control (RBAC) for ML Pipelines
• 9.3 Ethical AI and Bias Mitigation in Snowflake ML Models

10. Case Studies and Real-World Applications

• 10.1 Enterprise ML Use Cases with Snowflake
• 10.2 Troubleshooting Common ML Issues in Snowflake
• 10.3 Best Practices for Data Science and AI in Snowflake

Conclusion

The SnowPro® Advanced Data Scientist Training provides a comprehensive, hands-on approach to building, deploying, and optimizing machine learning models within Snowflake. By mastering Snowpark, ML model deployment, and AI-driven analytics, participants will be able to drive data science innovation, enhance decision-making, and scale AI-powered solutions in enterprise environments.

Reference