Unleashing the Power of Machine Learning with Snowflake

Machine Learning (ML) has revolutionized data analysis and interpretation for businesses. However, the operationalization of ML models presents ongoing challenges. Continuous Integration/Continuous Deployment (CI/CD) is becoming increasingly essential as datasets expand and environments change. This article explores how to construct an end-to-end ML training workflow using Snowflake, highlighting the automation of the ML model development process using GitHub Actions and Snowflake Tasks.

Who Should Read This?

This article is tailored for data enthusiasts, ML practitioners, and technical business leaders who are looking to optimize their Machine Learning operations within Snowflake.

Discover how to:

Master Continuous Integration and Continuous Deployment for ML within Snowflake.
Explore the role of Snowpark and Snowpark ML in enhancing your ML processes.
Automate your ML workflow using Snowflake Tasks and Streams.

Snowflake's Strategy: Democratizing Data and ML

Snowflake's core mission is to ensure data is accessible, usable, and valuable to everyone. A key pillar of Snowflake's industry leadership is its commitment to ease of use and turnkey functionality. The platform simplifies complex tasks, reduces the need for specialized skills, and abstracts away the underlying platform complexities, offering total cost of ownership (TCO) advantages. Snowflake continues to innovate and streamline complex tasks with a user-friendly approach.

Looking ahead, Snowflake is shaping the future of:

Containers: Providing isolated environments for applications.
Low Management: Reducing operational overhead.
Unstructured Data: Simplifying the management of diverse data types.
DocumentAI: Enabling advanced document processing and insights.
Machine Learning: Providing user-friendly ML tools and functionalities.
ML SQL Functions: Embedding ML capabilities directly into SQL.
AIAI with NVDA: Collaborating for cutting-edge AI tools.
Microsoft: Partnering to bring Microsoft AI directly to the Data Cloud.
LLMs over Company Data: Expanding data reach and utility.
Data Applications: Making data-centric applications more accessible.
Streamlit Native Application Framework: Ensuring seamless integration for application development.

Technologies Used in the ML Workflow

This section delves into the specific technologies employed to create the automated ML workflow within Snowflake.

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Snowflake: The Cloud Data Platform

Snowflake is a cloud data platform that provides data warehousing, data lakes, data engineering, data science, and more. Its ability to seamlessly integrate data, natively support diverse data formats, and provide elastic computing makes it an ideal choice for an ML CI/CD pipeline.

Snowpark: Bridging the Gap Between Data and Code

Snowpark is a library that offers an API to query and process data within Snowflake. It operates on lazy execution principles, similar to Spark. Snowpark allows the use of local IDEs like Jupyter or VSCode to write and debug programs. For data science workloads, it enables preprocessing and feature engineering using the Snowpark API. The code is written on a local machine, but all execution occurs within Snowflake virtual warehouses using pushdown compute, eliminating the need for additional compute resources, configuration, or maintenance.

Simplify "Pushdown Compute":

Pushdown compute can be thought of as delegating tasks to a more powerful assistant. Instead of performing all the processing locally, the code is sent to Snowflake, where robust computers handle the complex distributed and scalable computations. This reduces the workload on the local machine and accelerates results.

Snowpark ML: Streamlining the ML Lifecycle

Snowpark ML provides a suite of APIs designed for the entire machine learning lifecycle within Snowflake. It facilitates seamless data pre-processing, model training, and deployment, all within the Snowflake environment. Its strength lies in its ability to perform tasks using push-down compute, ensuring optimized performance, scalability, and governance. Snowflake-ml integrates familiar libraries like scikit-learn and xgboost, making ML workflows more intuitive for Python users.

Stored Procedures (SPROC) for Feature Engineering

Stored Procedures in Snowflake are essential for feature engineering. They allow writing Pandas DataFrame/Snowpark DataFrame code directly within a Stored Procedure that runs within Snowflake. These stored procedures can be scheduled using tasks.

User-Defined Functions (UDF) for Inference

Python UDFs are Snowflake User Defined Functions that allow users to write Python code and call it inside Snowflake as if it were a normal SQL function. Python UDFs can contain new code and calls to existing packages, allowing flexibility and code reuse. Snowpark-ML can be used to train an ML model and deploy the model in Snowflake using UDF. Snowflake’s partnership with Anaconda allows users to use 3rd party packages in Python UDFs, and Anaconda handles everything automatically, like performing dependency management of packages.

Snowflake Tasks: Automating ML Workflows

Snowflake Tasks are automated, scheduled units of work within Snowflake. They are crucial for orchestrating complex workflows, particularly in ML, where tasks such as data ingestion, transformation, training, and validation need to be sequenced. Tasks can be set to run at specific intervals or be triggered by specific events, ensuring that ML pipelines remain agile and responsive.

Snowflake Streams: Tracking Data Changes

Snowflake Streams provide a mechanism to track data changes in tables over time. This is particularly important in ML, as streams can track new data as it is ingested into Snowflake, enabling seamless batch-based processing of this new data. This ensures that model inference is performed on the most recent data incremental to the last inference timestamp.

Github Actions: Automating CI/CD

GitHub Actions, a CI/CD platform integrated within GitHub, automates workflows triggered by repository events. For ML projects on GitHub, Actions facilitate automated tasks such as model training and deployment. In this demo, GitHub Actions interacts with Snowflake to deploy the Feature Engineering pipeline, model inference tasks, and data streams for continuous data processing and inferencing.

Leveraging Snowflake-ML for Efficiency

Snowpark ML's pre-processing functions are essential for feature engineering. Powered by Snowflake’s SQL engine, they excel at distributed multi-node execution, eliminating concerns about memory constraints or computational burdens. By integrating algorithms from open-source libraries like Scikit-Learn and XGBoost, model training can be integrated directly into Snowflake. The streamlined, efficient, and secure experience makes Snowflake-ML an ideal solution for businesses looking to merge data management with ML.

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Step-by-Step Guide to ML CI/CD within Snowflake

This section outlines the detailed steps to implement ML CI/CD within Snowflake.

Step 1: Setting Up the Snowflake Environment

What: Establish a stable Snowflake data warehouse connection and grant access to necessary databases and schemas.
Why: A robust environment ensures seamless interaction between components and provides a solid foundation for subsequent operations.

Step 2: Loading Data into Snowflake

What: Populate the Snowflake environment by importing data from a designated folder.
Why: The quality of a model depends on its data. Loading pertinent datasets into Snowflake ensures secure and scalable data processing.

Step 3: Data Preparation for Model Training using Snowflake-ML

What: Utilize Snowpark’s computational capabilities to preprocess the data, preparing it for ML processes. Snowpark ensures all compute is pushed down, leveraging Snowflake’s scalable distributed computing infrastructure.
Why: Snowpark enables data cleaning and feature engineering within Snowflake, using pushdown compute. The code is executed in Snowflake’s distributed environment, maximizing efficiency and scalability.

Step 4: Training and Deploying the ML Model using Snowflake-ML

What: Train the ML model after data preparation, followed by its deployment based on requirements. The Snowflake-ML library is used to train the model directly on Tables in Snowflake, and a UDF is created for future inferring of the model.
Why: Training ML models with Snowflake-ML is convenient and scalable. The library trains models on data residing in Snowflake tables without moving the data, ensuring security and enabling training on extensive datasets. Once the model is trained, it is deployed by creating a UDF.

Step 5: Creating a Stored Procedure in Snowflake

What: Establish a stored procedure in Snowflake tailored to process incoming data.
Why: Stored procedures excel in managing batch processing, handling periodic data operations efficiently. As new data streams in, the procedure ensures systematic processing, guaranteeing the data’s preparedness for subsequent predictive analysis.

Step 6: Orchestrating the ML Workflow

What: Integrate Snowflake’s Tasks and Streams to automate the entire ML sequence.
Why: Automation ensures real-time responsiveness to incoming data. Tasks manage scheduled operations, ensuring tasks like data ingestion and model inference take place at predetermined intervals. Streams track data changes, ensuring that new incremental data is utilized for inference as it arrives. This synergy ensures agility and maintains the ML process in a continuous state of update.

Snowflake's Machine Learning SQL Functions

Snowflake's ML SQL Functions transform the way SQL and ML are viewed. The primary functions include:

Forecasting: Predict future values based on past data, ideal for sales predictions and stock trends.
Anomaly Detection: Identify unusual patterns in data that don't conform to expected behaviors, useful in fraud detection and system health monitoring.
Contribution Explorer: Understand contributing factors to a particular outcome, providing insights into 'why' something happened.

Requirements & Limitations

As with any tool in development, there are requirements and limitations. Here are the current constraints for these functions:

Maximum of 500,000 rows for model training.
Minimum of 12 rows for model training.
1-second minimum granularity.
Seasonal components have a 1-minute minimum granularity.
Timestamps must have FIXED intervals.
Season length of autoregressive features tied to input frequency.
Existing models cannot be updated; a new one must be trained.
Outliers can influence the algorithm; users may need to remove if undesired.
Model cloning across accounts is not possible.

Getting Started with ML SQL Functions

Diving into these functions involves a systematic process:

Prepare Data: Organize and clean your data to ensure its readiness.
Create Model: Set up the foundation for your machine learning model.
Train Model: Use your data to train and refine the model.
Harvest Data: Extract insights and results.

Example: Predictive Analysis with Stock Data

Consider a dataset with the closing price data for all the stocks in the Nasdaq & DOW. To run predictive analysis over the dataset for the next 2 months, training the model on data beginning on 1/1/2019, the following steps are taken:

Prepare Data: Views are used to further prepare the data for ML, meeting requirements.
- Exclude tickers with less than 12 rows (new IPO or stock that came off the market within 12 days of the beginning) through a view.
- Change the date column datatype to a timestamp in a view.
- Meet the FIXED intervals requirement by mocking up data for weekends and holidays.
Create Model: Create a time series forecasting model using the prepared data.
Train Model: Train the model using historical stock data.
Harvest Data: Extract the predicted closing prices for the next two months.

Snowflake's Capabilities for AI

Snowflake's cloud-native architecture and robust features provide a powerful platform for AI applications. Its capabilities enable businesses to harness the full potential of their data, create more accurate Machine Learning models, and integrate real-time insights into their operations.

Scalability

Snowflake’s architecture separates storage and compute, allowing users to scale each component independently based on their needs. AI algorithms and models thrive on vast amounts of data. Snowflake enables businesses to store data in a centralized, secure, and scalable environment, ensuring that data is easily accessible for AI applications. Snowflake’s ability to instantly scale up and down means that organizations only pay for the compute and storage resources they use.

Data Sharing

Data sharing is critical for AI development, especially in industries where real-time data collaboration is essential. Snowflake’s built-in data sharing feature enables organizations to seamlessly integrate external datasets into their AI models, enhancing the accuracy and diversity of the insights generated. Snowflake accelerates the development of AI models by removing data silos and providing a unified view of data, ensuring that all relevant data is readily available.

Machine Learning Integration

Snowflake’s integration with machine learning (ML) platforms is one of its most powerful features for AI. Snowflake’s Data Science Workbench allows data scientists to run experiments and prototypes within Snowflake’s secure and scalable environment. The integration with external ML and AI tools provides businesses with a seamless workflow for building, training, and deploying AI models.

Experimentation

Experimentation is at the heart of AI development. Snowflake’s zero-copy cloning feature allows data scientists to create full copies of data without duplicating it physically. Zero-copy cloning is an essential tool for AI workflows as it enables teams to experiment with different datasets, modify data, and run multiple AI models simultaneously, all without incurring additional costs for storage.

Security and Governance

Data security and governance are crucial when working with AI models, particularly in regulated industries such as healthcare, finance, and manufacturing. Snowflake provides robust security features, including encryption, access controls, and compliance certifications. These features ensure that sensitive data is protected, even when used in AI models.

Real-Time Processing

Real-time data processing is becoming a key requirement for AI applications that need up-to-date information for predictive analytics and decision-making. With Snowflake’s support for real-time data ingestion and processing, organizations can feed streaming data directly into their AI models, enabling them to make real-time predictions, detect anomalies, and adjust strategies dynamically.

Snowpark

Snowpark, Snowflake’s developer framework, allows data engineers, data scientists, and developers to build sophisticated AI workflows within Snowflake’s environment. By using Snowpark, teams can take advantage of the full range of Snowflake’s data capabilities while incorporating custom AI models and advanced analytics.

Integration with Third-Party Tools

Snowflake’s ability to integrate seamlessly with a wide range of third-party tools, including popular AI platforms like Databricks and DataRobot, makes it an end-to-end solution for AI development. By enabling a seamless flow of data from ingestion through transformation, analysis, and AI model deployment, Snowflake provides businesses with a unified environment for managing the entire AI lifecycle.

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