Snowflake is a cloud-based data warehousing platform that is primarily used for data storage and SQL-based data analysis. While Snowflake itself isn’t designed to be a machine learning platform, it is increasingly being used in machine learning workflows for data preparation, feature engineering, and as a source/destination for machine learning data.

How Snowflake Facilitates Machine Learning:

1. Data Centralization: Snowflake allows organizations to store all their data in a single, unified data warehouse. This is beneficial for machine learning because it centralizes all the data you might need for training models in one place.
2. Scalability: Snowflake can handle very large datasets, which is crucial for training complex machine learning models that require a lot of data.
3. Data Sharing: Snowflake’s data sharing capabilities make it easy to share datasets or specific data sets with different departments or even outside the organization. This helps in collaborative model development.
4. Real-time Data: Snowflake can work with real-time data which is important for machine learning models that rely on up-to-date information for effective predictions or classifications.
5. SQL-based Machine Learning: While not a fully-fledged machine learning framework, Snowflake does offer some basic statistical and window functions that can assist in data transformation and feature engineering tasks commonly associated with machine learning.
6. Integration with ML Tools: Snowflake easily integrates with popular machine learning platforms and languages like Python, R, TensorFlow, and others through connectors and APIs. This allows data scientists to query data directly from Snowflake into their machine learning environment.
7. Data Versioning: Snowflake’s Time Travel feature enables users to access historical data, useful for backtesting machine learning models.

Common ML Workflows with Snowflake:

1. Data Collection: Store raw data in Snowflake from various sources.
2. Data Cleaning: Use SQL queries to clean and pre-process the data.
3. Feature Engineering: Again use SQL or integrate with tools to generate new features from the data.
4. Model Training: Export the cleaned, feature-engineered data into a machine learning platform for model training.
5. Evaluation and Tuning: Assess the model and tune it. Use Snowflake for storing evaluation metrics and results.
6. Deployment: Once the model is trained, predictions can be stored back in Snowflake for further analysis or operational use.

In summary, while Snowflake isn’t a machine learning tool in the traditional sense, it plays a critical role in machine learning and data science operations by serving as a robust, scalable, and flexible data platform.