With the rise of big data and machine learning, organizations are looking for data platforms that can support advanced analytics use cases. One platform that has gained popularity in recent years is Snowflake, the cloud-based data warehouse. But is Snowflake a good choice for machine learning workloads? Let’s explore the key capabilities of Snowflake for ML and some of the potential limitations.
Snowflake’s Strengths for Machine Learning
Here are some of the aspects of Snowflake that make it well-suited for machine learning projects:
Scalability
Snowflake utilizes a unique multi-cluster, shared data architecture that makes it highly scalable. This allows users to leverage extra compute resources on demand when working with large datasets or complex models. Snowflake can scale storage and compute completely independently, which is important for ML workloads.
Performance
Snowflake offers high concurrency and fast query performance. Response times remain fast even with multiple concurrent queries. This performance can accelerate iterative processes like model training. Snowflake also offers caching and materialized views to speed up repeat queries.
Flexibility
Snowflake provides a lot of flexibility in deploying ML pipelines. Data can be loaded from diverse sources. Compute can be separated from storage for optimization. Snowflake also supports Python, R and other languages for in-database model training.
Collaboration
Snowflake uses a secure data sharing architecture that facilitates collaboration between data science teams. Different users can be granted access to development, test, and production environments. Snowflake’s sharing model helps enable reproducibility and reusability.
Cloud-native architecture
As a cloud-native platform, Snowflake provides inherent support for elasticity, high availability, and automation. These capabilities simplify the deployment and management of ML applications in the cloud.
Limitations of Snowflake for Machine Learning
While Snowflake has many strengths, it also has some limitations when it comes to machine learning workloads:
Lack of native ML capabilities
Snowflake lacks native machine learning algorithms and model training capabilities. While it provides connectivity to external ML platforms, data movement can add overhead. Some competing data platforms like BigQuery offer more embedded ML functionality.
Data type restrictions
Snowflake imposing some restrictions on semi-structured and unstructured data that are common in ML workflows. Complex data types like images, audio, and video require extra processing to load into Snowflake.
Inefficient for iterative work
Snowflake uses a traditional MPP architecture which requires writing intermediate data to storage. This can be inefficient for highly iterative ML training processes that require low latency access to intermediate state data.
ETL overhead
Getting raw data ready for training models can involve extensive ETL (extract, transform, load) work. While Snowflake has good ETL tools, a specialized data lake may have lower preprocessing overhead for some use cases.
Cost
Snowflake offers great flexibility, scaling, and concurrency but at a relatively high price point. The storage and computing costs required for sizable ML projects on Snowflake can be prohibitive for some organizations.
Architectural Approaches for Using Snowflake and Machine Learning
While Snowflake has some limitations for ML, there are architectural approaches to enable effective use of Snowflake alongside other technologies:
Snowflake + SageMaker/Databricks
A common pattern is using Snowflake for structured data storage and related ETL, while leveraging SageMaker, Databricks or similar for modeling and deployment. Snowflake functions as the backend serving clean, query-able training data.
Multi-stage pipelines
Data can be staged in object storage like S3 as raw input, preprocessed in Snowflake, and then loaded into a Spark cluster or GPU server for model training/scoring.
External functions and clustering
Snowflake external functions and UDFs allow Python/R scripts to be executed in compute environments optimized for ML. Temporary clusters can provide the required performance.
Caching and materialized views
Strategic use of caching, materialized views, and temporary tables in Snowflake can reduce iterative processing overhead for ML algorithms.
Complementing a lakehouse
For some organizations, Snowflake complements a lakehouse environment optimized for ML, providing transformed, queriable data.
When to Use Snowflake for Machine Learning
Here are some good use cases for using Snowflake as part of a machine learning pipeline:
Structured/relational data
Snowflake shines when the primary training data is structured and relational. This could include sales transactions, customer records, device telemetry, etc.
Regulated industries
For regulated industries like healthcare and financial services, Snowflake provides the security, governance, and auditability required for accountable ML applications.
Model operationalization
Snowflake is great for operationalization – making models available for low latency scoring at scale via APIs or queries.
Collaboration
Snowflake facilitates collaboration between data engineers, analysts, and data scientists when developing and deploying ML in production.
Hybrid/multi-cloud
Snowflake’s availability across clouds makes it appealing for ML initiatives that involve a hybrid or multi-cloud environment.
Key Considerations When Using Snowflake for ML
Organizations adopting Snowflake for machine learning should keep the following considerations in mind:
Understand Snowflake capabilities and limitations
Have realistic expectations about what Snowflake can and can’t provide out-of-the-box for ML. Be prepared to leverage other platforms to fill gaps.
Use appropriate architecture
Design with Snowflake’s strengths and limitations in mind. Connect it with external tools optimized for tasks like data preprocessing and model training.
Implement caching strategy
Plan and test different caching approaches to improve performance for iterative ML workloads.
Monitor and optimize
Closely monitor Snowflake performance under ML workloads. Identify and resolve bottlenecks related to queries, concurrency, clusters, etc.
Leverage external functions
Make use of Snowflake external functions and clustering to offload ML scripts to suitable compute environments.
Manage costs
Carefully evaluate storage and computing requirements for ML to minimize Snowflake costs. Turn off resources when not needed.
Conclusion
Snowflake offers many attractive capabilities like scalability, performance, and collaboration features that make it well-suited for some machine learning use cases. However, for intensive workloads involving lots of unstructured data or iterative model training, Snowflake has some limitations.
The key for organizations is understanding Snowflake’s strengths and weaknesses, and architecting their ML platform accordingly. Used strategically in conjunction with external machine learning platforms, Snowflake can play an effective role in many end-to-end ML pipelines and applications.