Leverage AI & ML in Snowflake Securely

Introduction to AI and ML in Snowflake

Artificial intelligence (AI) and machine learning (ML) are transforming how organizations extract insights from data. Snowflake is at the forefront of making AI and ML accessible to everyone—not just data scientists or engineers. Its goal: democratize data and AI, empower business users, and provide enterprise-grade security, all while eliminating infrastructure management headaches. This guide explores Snowflake's AI/ML capabilities, practical use cases, and how you can get started—fast.

Snowflake's Security-First Approach to AI

Security is Snowflake's top priority. Any AI or ML tool you use within Snowflake inherits the platform's robust, enterprise-grade security standards. Whether you’re extracting data from PDFs, building AI-driven apps, or running custom ML models, your data remains protected by the same governance and access controls as your core analytics workloads. There's no compromise—security is embedded from the ground up.

Empowering Business Users: No-Code and Low-Code AI

• Natural Language Queries: With tools like Snowflake Copilot, users can ask business questions in plain English and get answers from their data without writing SQL. For example, a user might ask: "Show me all summer camps in 2024 by number of available spots." Snowflake translates this into the necessary queries and returns actionable results.
• Universal Search: Find datasets across your Snowflake environment and marketplace using intuitive search—no technical expertise required.
• Document AI: Extract attributes from PDFs and Word documents automatically, making unstructured data instantly usable for analytics and reporting.

Zero Infrastructure Management

Snowflake eliminates infrastructure concerns. Users don’t need to worry about provisioning servers, scaling resources, or managing underlying hardware. AI and ML workloads run securely within Snowflake’s platform, freeing you to focus on outcomes—not operations. Whether you’re executing Python, SQL, or deploying Docker containers, everything happens under unified governance and billing.

Key Snowflake AI & ML Features

• Snowflake Cortex: Offers a suite of AI services that are easy to use, flexible (supporting SQL and Python), and cost-effective. Cortex includes forecasting, anomaly detection, translation, and summarization functions—callable directly from SQL or Python.
• Document AI: Automatically extract structured data from documents—perfect for automating workflows like payroll processing or contract analysis.
• Copilot: Enables English-language querying of your data, making analytics accessible to everyone in your organization.
• Universal Search: Allows users to discover data both within internal tables and external marketplace sources, increasing data accessibility.
• Streamlit Integration: Build interactive analytics dashboards and apps with minimal Python code, natively within Snowflake. Streamlit apps empower rapid prototyping and instant data visualization without complex setup.

Snowpark: Advanced Data Engineering & ML in Python

Snowpark is Snowflake’s developer framework for building powerful data transformations and machine learning models. It supports:

• DataFrame API: Similar to Apache Spark, allowing seamless migration of existing workloads and familiarity for Python developers.
• ML API: Simplifies building, training, and deploying machine learning models directly within Snowflake, leveraging its scalability and security.

Unlike traditional approaches—where data must be moved out of the warehouse for processing—Snowpark pushes computation to Snowflake. This keeps your data secure, reduces latency, and eliminates the need for external infrastructure.

Container Services: Deploy Any App, Even With GPUs

With Snowflake Container Services, you can:

• Dockerize any application and run it directly inside Snowflake, including those requiring GPUs for intensive AI/ML tasks.
• Host custom inference models, applications written in C, C++, Rust, or any language that supports containerization.
• Benefit from unified security and governance—no need to move data or manage external orchestration tools.

Examples include deploying large language models (LLMs), custom analytics engines, or even unconventional apps (as a showcase, some users have run games like Doom in containers—demonstrating flexibility, though business value may vary!).

Practical Use Cases

• Automated Document Processing: Use Document AI to extract payroll data from employee documents, accelerating HR and finance workflows.
• Business Intelligence for All: Empower non-technical users to ask questions and generate reports with Copilot, reducing reliance on IT and data teams.
• Data Discovery: Universal Search helps teams find, evaluate, and integrate new data sources—internally and from Snowflake Marketplace—boosting innovation.
• Rapid App Prototyping: Streamlit lets analysts and business users build and share interactive dashboards in minutes, driving faster decision-making.
• Custom ML & AI: With Snowpark and Container Services, data scientists can develop, deploy, and scale advanced analytics models without leaving the secure Snowflake environment.

Customer Success Stories

• EDF Energy: Achieved 3-4x increase in ML model output speed by porting Spark pipelines to Snowpark, accelerating time-to-market and enabling faster insights.
• Global Adoption: Over 35% of Snowflake customers use Snowpark weekly, spanning industries from energy to fintech and retail.

Seamless Integration & Extensibility

Snowflake’s AI and ML capabilities are accessible through multiple interfaces:

• Direct SQL or Python (via Snowpark)
• Embedded within user-defined functions (UDFs), stored procedures, and analytic workflows
• Through Streamlit apps and dashboards

This flexibility means you can choose the right tool for each team: business users, data engineers, and data scientists all get what they need—without silos or duplicate tooling.

How to Get Started With AI & ML in Snowflake

1. Assess Your Use Case: Identify opportunities for automation, insight generation, or workflow optimization using Snowflake’s built-in AI/ML tools.
2. Explore Quickstarts: Access sample applications and step-by-step guides to deploy Dockerized apps, train ML models, or build interactive dashboards quickly.
3. Leverage Prebuilt Functions: Use forecasting, anomaly detection, summarize, and translate functions for rapid deployment—no modeling expertise required.
4. Build Advanced Solutions: For custom ML or AI, utilize Snowpark and Container Services to bring your own code and libraries securely into Snowflake.
5. Engage With Keboola: Keboola offers integration, automation, and consulting to help you unlock maximum value from Snowflake’s AI/ML stack.

Best Practices and Considerations

• Security & Governance: Always leverage Snowflake’s built-in security controls for data, apps, and containers.
• Performance Optimization: Choose the right service (built-in functions, Snowpark, or containers) based on workload complexity and resource needs.
• Cost Management: Snowflake’s pay-as-you-go model and efficient compute usage help control costs—monitor usage and optimize as needed.
• Evaluate Business Value: With great flexibility comes responsibility. Before deploying custom apps or containers (e.g., running Kafka, PostgreSQL, or non-standard workloads), consider if it’s the optimal approach for your goals.

Why Choose Snowflake for AI & ML?

• Unified Platform: Analytics, data science, and application development—all in one secure environment.
• Fast Time-to-Value: Deploy AI and ML solutions in minutes—not weeks or months.
• Enterprise Security: Enjoy peace of mind with robust, consistent security across data, models, and applications.
• No Infrastructure Overhead: Focus on driving business results, not managing servers or scaling clusters.
• Broad Ecosystem: Extend functionality with marketplace data, partner solutions, and a vibrant developer community.

Next Steps: Accelerate Your AI Journey with Keboola

Keboola’s expertise helps you harness the full potential of Snowflake’s AI and ML platform. Whether you’re just starting or scaling enterprise deployments, Keboola provides integration, automation, and strategic guidance so you can innovate faster and smarter.

Conclusion

Snowflake has reimagined AI and ML for the modern data-driven enterprise: secure, accessible, and without infrastructure headaches. From point-and-click document extraction to deploying advanced models and applications, every business user and data scientist can unlock new value—today. Ready to get started? Contact Keboola for a personalized strategy session or try our integration solutions to accelerate your AI journey.