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Acing Python Machine Language using Snowflake

Jason Li
Sr. Software Development Engineer
Skilled Angular and .NET developer, team leader for a healthcare insurance company.
September 05, 2022

Snowflake Data Cloud

Recent innovations illuminate that the Snowflake Data Cloud helps many business organizations with proper Machine Learning or ML initiatives. For better understanding, it is essential to recognize the general lifecycle of ML applications. Snowflake props up the Python Machine Language as it enable the entire lifecycle as we know it. Out of the four phases involved in the ML lifecycle, two get well supported through the Snowflake UI, SnowSQL, and the Snowflake Connector. The other two phases get supported by Snowpark and UDF pretty heavily. 

Machine Language Life Cycle

Discovery: The life cycle of the Machine Language commences with proper data discovery. This phase gets characterized by the data-discovery activity done by various data scientists wherein all available data gets collected. A challenging process such as this data discovery is made easier by using an enterprise data warehouse on Snowflake. With all the data available through Snowflake, data gathering becomes easier. The enterprise data warehouse gets used with common access patterns, which is a significant step in enabling the data science and Machine Learning process. The Snowflake Connector for Python is a sophisticated statistical method necessary for proper data analysis and profiling. 

Training: For an optimal training process, Snowflake provides data access in addition to using your own data. One can purchase their required data from the Data Marketplace using the Snowflake Account, as it gets easily integrated into the account. The whole data transfer process and its transformation get undertaken by Snowflake. A reliable training routine gets used for the proper maintenance of ML models into the framework you require. 

Deployment: The release of Snowpark and Java User-Defined Functions (UDFs) has considerably improved the support and deployment of Snowflake for the training of Machine Language (ML) models. UDFs or Java and Scala function, aid the Snowflake data as it is taken as an input to produce the output based on the custom logic. For ML, the UDF provides a mechanism that encapsulates various models for proper deployment using Java/Scala libraries. 

Monitoring: The Snowflake simplifies the follow-up process of ending of ML lifecycle training loop. The scheduled tasks of Snowflake help with valuable orchestration of properly mentoring the Machine Language characteristics and predictions. These Scheduled Tasks leverages the UDFs for designing and building processes with Snowpark while constantly monitoring any complex issues like Data Drift. Once these are detected, the analysts and data scientists use the Snowflake UI to dig deeper and understand the issues, which becomes easier to rectify without any hassles. 

One can easily ace the Python Machine Language using Snowflake as it gets aided by the recent releases of Snowpark and Java/Scala UDFs. It can be concluded that Snowflake is powerful as it completes the entire ML lifecycle. 

Designing better ML Models with the aid of Snowflake Data Clouds

Through the Snowflake data clouds, one can leverage the data marketplace. Herein, semi-structured data support gets used for enriching ML models. Studies have shown that an increase in data volume produces better results, which has a clear impact on Machine Learning models. 

Data scientists spend considerable time selecting the much-needed features that give better results for their models. The Snowflake data cloud simplifies the whole process as a single source of truth with all required data; an easy way to consume and use the data in the training of Machine Learning models. 

With Snowflake, it becomes easier to combine data from three different sources to improve the ML models for better training and to provide the best predictions. The use of Technology Partners around Snowflake provides better models and insights that reduce the design time.

Snowflake simplifies the training of Python Machine Language Models

The Snowpark of Snowflake for the Python Machine Language models provides the data scientists with a great way to execute the Data-Frame-Style Programming. It gets done against the Snowflake data warehouse, which includes the ability to setup full-blown Machine Learning pipelines that run on a recurrent schedule as per the requirements.  

An overview of Snowflake

The Snowflake got designed as a fully relational ANSI SQL enterprise data warehouse. It got developed with a blueprint or architecture that separates computing from storage. This design aids in a simplified scale-up and down on the fly without any delays and disruptions. With all these features or characteristics, Snowflake currently runs on Microsoft Azure, Google Cloud Platform, and Amazon Web Services. 

Snowflake users can access their data easily as it got recently added with External Tables On-Premise Storage. The unique design of Snowflake, which is a columnar database, helps in with a vectorized execution of addressing the most demanding analytical workloads. It supports unlimited concurrency through its shared data architecture or blueprint. Another unique feature of Snowflake is that it can automatically scale-up to handle varying demands of its multi-cluster virtual warehouse feature. Snowflake can transparently add computing resources during peak loads and proper scale down when the loads subside. 

An overview of Snowpark

The Snowpark of Snowflake brings in deeply integrated Data-Frame-Style Programming in languages such as Scala, which then gets extended to Java, and now to Python. The Snowpark got designed to simplify complex data pipelines, which allows developers to interact with Snowflake directly without moving the data. The library of Snowpark provides a very intuitive Application Programming Interface (API) to query and process data in a pipeline. This Snowpark API provides programming language constructs to design and build SQL statements. The Snowpark operations get executed lazily on the server, and it reduces the data transferred between the clients and Snowflake databases. Data-Frame is the core abstraction in Snowpark. It represents data sets with which Snowflake and Snowpark operates to improve training and execution of the Python Machine Language. In the client code you use, you can construct a Data-Frame object, which sets it up to retrieve the data to be used. When the data gets retrieved, one can perform actions that evaluate the Data-Frame objects that send the corresponding SQL statements to the Snowflake database for proper execution. 

An overview of Snowpark for Python

It includes a proper local developmental experience that gets installed in the machine the analysts or relevant developers use. One can use the preferred Python IDEs and Dev tools to upload the related codes to Snowflake with the knowledge that it gets compatible. The Snowpark for Python is a Free Open Source software application, which is a change from the Snowflake’s history of keeping its codes proprietary. 

Getting Started with Snowpark Python

An end-to-end Data Science Workflow mentioned in the Snowflake tutorial gives a great idea of using Snowpark for Python to load, clean, and prepare relevant and related data. This data gets deployed for training the Machine Language models of Python as Snowflake uses the Python-UDF for inference or reference. The Snowpark nominally sheds light on how to create a Data-Frame that loads data from a stage along with ways to perform data engineering using Snowpark Data-Frame API. It also helps in understanding how to bring a proper learning model into Snowflake as a UDF to score new data. 

Here, the task is to provide the proper solution to the straightforward binary classification program, which is the classic customer churn production for the internet service provider. The tutorial commences with a local setup phase that uses Anaconda, for which Miniconda can be installed and used. Although it took longer than usual to load and install all the dependencies of the Snowpark API, it works just the way we need it to be. This quickstart process begins with a single Parquet file of raw data and extracts that transforms and loads relevant information into multiple Snowflake tables. Studies showcase that Snowpark for Python is the best way to go about training the Machine Language models of Python language. The extensibility support from Snowflake helps in resolving issues that happen with the quickstart. A wide range of popular Python Machine Language and deep learning libraries along with relevant frameworks gets included in the Snowpark for proper Python Installation. 

The Python code keeps running in the machine of analysts and developers and it can control Snowflake warehouses dynamically. It does so by scaling them up and down with ease at will for controlling costs and keeping runtimes reasonably short. The heavy lifting for Snowflake warehouses gets done well by proper use of Snowpark. Deploying of UDFs in Snowflake without incurring the costs of prediction endpoints on major cloud services is a major step in training Python Machine Language models successfully. Thus, essentially the Snowpark for Python does give data engineers and data scientists a great way to perform Data-Frame-Style programming against the Snowflake Enterprise Data Warehouse. It includes the ability to set up full-blown Machine Language training pipelines for Python that runs on a proper recurrent schedule. 


One can easily ace the training of Python Machine Language using the Snowflake Data Cloud and Snowpark application. The new technology saves considerable time and effort in training various Machine Language Tools for the Python language.