Snowflake began as a cloud-based data warehouse platform and has changed how large organizations store and manage large data sets. Its robust design improved data storage and operational flexibility, security, and efficiency.
As the demand for advanced data analytics especially using AI increased, Snowflake has been coming up with robust AI and ML features to its platform. Now it has become an integrated data platform from end to end with AI and ML features and functions. This enables every member of the data team such as researchers, engineers, and scientists to use this advanced technology.
Overview of Snowflake AI And ML Features
Snowflake’s AI and ML capabilities are of two categories based on How and what kind of scenarios it can be used in.
1. Snowflake Cortex AI
Snowflake Cortex is a set of AI-based features that use large language models (LLMs) to process unstructured data and respond to natural language queries.
This includes:
- Cortex LLM Functions: These functions allow for text generation, information extraction, sentiment analysis, summarization, and translation, making it easier to work with diverse datasets and use cases.
- Snowflake Copilot: This feature accelerates SQL productivity by generating queries and providing SQL assistance.
- Document AI: It facilitates the extraction of specific content from documents, such as contract terms or invoice amounts, using advanced AI models.
- Cortex Analyst and Cortex Search: These upcoming features will empower businesses to build tools that can interact with both structured and unstructured data.
2. Snowflake ML
Snowflake ML is designed in a way to provide us with very accessible and relatively easier-to-use ML functionalities.
- ML Functions: These include tools for time-series forecasting, anomaly detection, classification, and contribution analysis. These functions help us detect patterns, predict outcomes, and classify data.
- AI & ML Studio: An easy UI-based and no-code interface that allows users to develop, test, and deploy AI models without requiring deep technical expertise. This tool is designed to democratize AI development across the enterprise.
- MLOps Capabilities: Snowflake ML provides tools for model management, feature store integration, and governance to support the entire ML lifecycle, ensuring that models remain robust and effective over time.
Snowflake’s strong data security and governance along with these new optimized and accessible features, makes it a powerful platform for organizations looking to integrate AI and ML into their data workloads. Whether it’s through ready-made AI functions or custom-built models, Snowflake’s tools are designed to meet the diverse needs of modern data teams.
3. Anomaly Detection
Anomaly detection is one of the data analysis functions provided by Snowflake. This specific function provides users with an opportunity to uncover unusual data or trends from time-series data.
Scenario – detecting unusual sales patterns
Imagine that you are a data analyst responsible for monitoring all the incoming sales data daily. Your usual routine would include going through all the data however big it might be to weed out any unusual sales trends. This will mean that you’ll be spending most of your time doing this while you can provide that time and effort on some other equally important tasks. In such situations, you can use this ML feature to spot anomalies in minutes making your job more easier and productive.
Steps to follow
Set up your data:
- Create and load the tables with your historical sales data that will be used to train your model and new sales data which will be the one on which this model will be used.
Create and train your model:
- To create a Snowflake Anomaly Detection model you’ll need to specify which columns to use for training, such as the timestamp and the target variable (e.g.sales).
- CREATE SNOWFLAKE.ML.ANOMALY_DETECTION detector(…);
Detect anomalies:
- After training your model, apply it to your new data to detect anomalies. This involves specifying which columns to use for analysis.
View and interpret results:
- Review the results, which will include predictions on whether each data point is considered an anomaly, along with associated metrics like forecast values and confidence intervals.
By leveraging Snowflake’s anomaly detection feature, you can simplify your organization’s monitoring tasks, making them more efficient and less resource-intensive, all within minutes.
4. Forecasting
Snowflake’s forecasting ML feature allows users to predict future trends and outcomes based on historical data.
Scenario – Predicting Future Inventory Needs
Imagine you’re managing inventory data and are required to predict future product demands based on this data. Snowflake’s forecasting feature allows you to use the historical Inventory data to detect the various products that’ll be in more demand or even not that popular through ML algorithms. This ensures optimal stock management, reduces the risk of stockouts or overstocking, and improves overall supply chain efficiency.
Steps to follow
Set up your data:
- Create and load the tables with your historical inventory data which will be used for training a model and new inventory data which will be the one on which this model will be used.
Create and train your model:
- Use Snowflake’s tools or commands to set up a forecasting model. Specify your timestamp column and the column with the values you want to predict.
-
CREATE SNOWFLAKE.ML.FORECAST model1(
INPUT_DATA => TABLE(v1),
TIMESTAMP_COLNAME => ‘date’,
TARGET_COLNAME => ‘inventory’
);
Generate forecasts:
- Apply your trained model to forecast future values. This will give you predictions for the periods you want to forecast, along with estimated confidence intervals.
Review and interpret results:
- Check the forecast results, including predicted values and confidence ranges. This helps you understand how accurate your forecasts might be.
Snowflake’s Forecasting feature delivers valuable insights to your data team, streamlining the process for effective and efficient predictions.
5. Chabot
Snowflake’s LLM functions, such as the COMPLETE function, allow developers to create chatbots. Using this along with Streamlit and a little Python can create a well-made chatbot in a short period.
Scenario – Data-related insights with a Chatbot
Imagine a retail company with a sales team that frequently needs insights on sales, inventory, or some other kind of data. For this, they will be required to reach out to the relevant departments and get the necessary insights. In such scenarios, a chatbot built on top of your data in Snowflake will come a long way as it’ll take care of the menial questions posed and allow the sales team to act more factly and independently.
Snowflake provides a diverse selection of LLM models, including popular options like Llama3-8b and Mistral-Large, as well as its proprietary Snowflake-Arctic. These models are designed to power advanced chatbot setups, enabling seamless interactions with your data warehouse. By leveraging these models, you can create a chatbot that can effortlessly interpret and respond to natural language queries about your data.
Conclusion
Snowflake’s AI and ML capabilities are reshaping how organizations leverage their data by offering powerful features such as Forecasting, Anomaly Detection, and large language model (LLM) functions. These tools are integrated directly into the platform, simplifying the process of gaining meaningful insights from even the most complex datasets. Users can seamlessly perform their tasks without the need to transfer data to other platforms. With our expertise in Snowflake’s advanced AI and ML features, we provide scalable Snowflake implementation solutions & consulting tailored to diverse business needs, helping organizations drive innovation and make more strategic, data-driven decisions.
