Snowflake Machine Learning Simplified: 4 Critical Aspects

Data, data, everywhere! But how can I get some valuable insights from it? Sounds like a familiar question ringing in today’s corporates? Snowflake Machine Learning combo is raising a lot of interest in recent times. Will Snowflake Machine Learning be the answer to your question? Probably yes! Let’s find out.

We are generating a massive amount of digital data with each passing day. Our mobile devices, social media interactions, personal data, official data, and the increasing digitization are generating vast volumes of data. All the data gets stored in digital databases. Some get linked to various Cloud-based and other applications, which raises the question – Can we integrate data easily between multiple apps and databases? What about handling structured and unstructured data?

In 2020, people created 1.7 MB of data every second.

We as human beings find it challenging to derive value from all the data available around us. Machine learning techniques can process vast amounts of data.

This blog talks about the different salient aspects of Snowflake Machine Learning in detail. It also gives a brief overview of Snowflake before jumping into the Snowflake Machine Learning lifecycle phases.

Table of Contents

• What is Snowflake?
  • Key Features of Snowflake
• What is Machine Learning?
• Are Snowflake and Machine Learning a Good Combination?
• How does Snowflake support the ML Lifecycle?
  • Snowflake Machine Learning Lifecycle: Discovery Phase
  • Snowflake Machine Learning Lifecycle: Training Phase
  • Snowflake Machine Learning Lifecycle: Deployment Phase
  • Snowflake Machine Learning Lifecycle: Monitoring Phase
• Conclusion

What is Snowflake?

Setting up a data warehouse may sound like a daunting task some time back. But today, we have Snowflake, which is a cloud-native platform. One of the main advantages is eliminating the need to segregate data warehouses, lakes, and data marts, allowing secure data sharing across your organization.

The differentiators of Snowflake are its architecture that separates computing from storage, and each of them can get scaled independently. That means as a customer; you can use and pay for computation and storage separately. The secure sharing functionality makes for quick sharing in real-time. With Snowflake, you can seamlessly run your data solution across multiple regions and Clouds for a consistent experience. Snowflake makes it possible by abstracting the complexity of underlying Cloud infrastructures.

Snowflake also allows you to access shared datasets and data services via the Snowflake Data Marketplace that provides ample opportunities to connect with thousands of Snowflake customers.

Key Features of Snowflake

Here are a few features of Snowflake as, a Software as a Service (SaaS) offering:

• Accelerate Quality of Analytics and Speed: Snowflake allows you to empower your Analytics Pipeline by shifting from nightly batch loads to real-time data streams. You can accelerate the quality of analytics at your workplace by granting secure, concurrent, and governed access to your Data Warehouse across the organization. This allows organizations to optimize the distribution of resources to maximize revenue by saving on costs and manual effort.
• Improved Data-Driven Decision Making: Snowflake allows you to break down Data Silos and provide access to actionable insights across the organization. This is an essential first step to improve partner relationships, optimize pricing, reduce operational costs, drive Sales effectiveness, and much more. 
• Improved User Experiences and Product Offerings: With Snowflake in place, you can better understand user behavior and product usage. You can also leverage the full breadth of data to deliver customer success, vastly improve product offerings, and encourage Data Science innovation.  
• Customized Data Exchange: Snowflake allows you to build your Data Exchange which lets you securely share live, governed data. It also provides an incentive to build better data relationships across your business units and with your partners and customers. It does this by achieving a 360-degree view of your customer, which provides insight into key customer attributes like interests, employment, and many more. 
• Robust Security: You can adopt a secure Data Lake as a single place for all compliance and cybersecurity data. Snowflake Data Lakes guarantee a fast incident response. This allows you to understand the complete picture of an incident by clubbing high-volume log data in a single location, and efficiently analyzing years of log data in seconds. You can now join Semi-structured Logs and Structured Enterprise Data in one Data Lake. Snowflake lets you put your foot in the door without any indexing and easily manipulate and transform data once it is in Snowflake.  

What is Machine Learning?

Machine learning is a sub-section of artificial intelligence. Computers learn automatically without any human intervention or assistance and adjust their actions accordingly. We can say a machine can imitate intelligent human behavior at a high level. For example, computer systems can recognize an image or get data from video scenes, understand text written in natural language, or perform an action in the physical world. 

Are Snowflake and Machine Learning a Good Combination?

Machine learning has the powerful ability to process vast volumes of data. With the growing volume of data, organizations are sitting on a goldmine. They can be staring at a loss if they are unable to leverage Machine Learning and Snowflake database capabilities in gaining intelligent insights. 

Machine learning iteratively learns from a given data set to understand patterns and behaviors with close to little or no programming. All major cloud platforms offer machine learning platforms. Snowflake data cloud works with leading cloud platforms like Amazon Web Services, Microsoft Azure, and Google cloud. That means there is no need to have dedicated resources for setup, maintenance, and support for in-house servers.  

While talking about integrating data in Snowflake, you might want to explore options like  Hevo that offer a faster way to move data from 100+ sources such as Databases or SaaS applications into your Data Warehouse/desired destinations/Databases like Snowflake. 

Moreover, Machine learning applications follow a typical lifecycle, and the Snowflake database supports ML at all stages.

How does Snowflake support the ML Lifecycle?

The ML lifecycle has four typical phases – The Discovery Phase, Training, Deployment, and Monitoring. Let us see how the Snowflake data cloud supports ML in all the phases.

1. Snowflake Machine Learning Lifecycle: Discovery Phase
2. Snowflake Machine Learning Lifecycle: Training Phase
3. Snowflake Machine Learning Lifecycle: Deployment Phase
4. Snowflake Machine Learning Lifecycle: Monitoring Phase

1) Snowflake Machine Learning Lifecycle: Discovery Phase

In the discovery phase, the first step is to collect data that is relevant to the Machine learning application. Having data in Snowflake is quicker for use; otherwise, use an enterprise data warehouse with common access patterns.

After gathering data, the data scientists will try to understand the quality and value of the collected data using exploratory data analysis and data profiling. In case there is a need for any feature engineering or ad-hoc analysis, one can perform it using the SnowSQL or the Snowflake UI itself.

In case of complex statistical methods for profiling or data analysis needs, the Snowflake Connector for Python is an excellent choice to extract data.

2) Snowflake Machine Learning Lifecycle: Training Phase

Training the Snowflake Machine Learning model will involve using various data sources. In case you need to use external data, the Snowflake Data Marketplace access will help. If your specific data needs require a purchase from the data marketplace, one can purchase and incorporate it directly into a Snowflake account.

While maintaining Snowflake Machine Learning models and training them, its time travel features enable accessing historical data that may have changed or got deleted and can save a lot of effort.

3) Snowflake Machine Learning Lifecycle: Deployment Phase

Deployment of Snowflake ML models is a critical phase and is ably supported with its releases of Snowpark and Java user-defined functions (UDFs). Snowflake supports UDFs written in multiple languages too. You can call UDFs from Snowflake in the same manner as calling a built-in function.  

Snowpark enables handling tables in Snowflake using Java or Scala, which is a high-level language to perform operations on tables like SQL. UDFs operate on a single row in a Snowflake table. Snowpark integrates well with UDF for Snowflake Machine Learning usage.

UDFs, help to encapsulate ML models during deployment using libraries from Java or Scala. Both UDFs and Snowpark support transforming data during deployment in case of any pre-or-post-processing requirements.

4) Snowflake Machine Learning Lifecycle: Monitoring Phase

The Snowflake Scheduled Tasks feature is an exciting orchestration tool that helps us to monitor machine learning predictions. Monitoring complex issues like data drift by using UDFs enhance efficiency, and one can even build processes with Snowpark that help in monitoring.

The intuitive Snowflake UI helps troubleshoot issues and do a relevant root-cause analysis. Using the versatile Snowflake connector, one can create convenient and comprehensive dashboards for Snowflake machine learning predictions.

Conclusion

As you can see, Snowflake is a perfect tool for machine learning projects. Combining machine learning with cloud data warehouses requires a flexible and powerful cloud-based data platform like Snowflake. Gain infinite expansion, scalability, and ease of use. Discover more.

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