AI Model Training with Python & Snowflake

AI model training is the process of developing an intelligent system capable of performing specific tasks by providing data to an untrained or pre-trained algorithm. This process requires providing the algorithm with clear objective, relevant data and iterative feedback loops.

Python’s rich ecosystem of machine learning libraries facilitates this process, providing development teams with essential tools at each stage of AI model training.  In this article, we’ll explain the step-by-step AI model training process and how to use Python to develop generative AI models for specific use cases. We’ll conclude by sharing new innovations at Snowflake that simplify model training and democratize access to AI-driven insights for nontechnical users.

The AI Model Training Process

AI model training can significantly impact an AI system's accuracy and efficiency. While specifics may vary by use case, the process typically involves the following key steps:

Data preparation

Clean, well-structured data is the foundation of all AI systems, enabling accurate outputs. During data preparation, data is collected, transformed and cleaned before being divided into training, validation and testing datasets.

Model selection

An AI model is a mathematical algorithm that learns from data to solve specific use cases. Model selection can significantly impact performance, efficiency and interpretability. An appropriate model leads to accurate task performance, while an unsuitable model may result in poor outcomes, overfitting or fail to capture important patterns in the data.

Initial training

During this phase, the AI model is exposed to training data to learn patterns and relationships within the data to make predictions. These predictions are compared to expected results, and adjustments are made to improve the model's performance over numerous iterations.

Validation 

In this stage, the model is tested with validation data sets, a sample of data that wasn’t part of the initial training data. These data sets are typically broader and more complex than the training data, designed to push the model in ways that can reveal potential underlying performance issues. 

Model testing

After validation, the model is tested using real-world data to test its performance on new, unseen data similar to what it will experience post-deployment. Model testing provides a final evaluation of the model's readiness for production. If successful, the model is prepared for deployment.

Streamline AI Model Training with Python

Python is ideal for AI model training. It’s versatile and offers an extensive ecosystem of libraries and frameworks, providing developers with a comprehensive toolkit for streamlining and optimizing the AI model training process.

Extensive collection of machine learning libraries

Python is an easily readable programming language with an intuitive syntax. Strong community support and cross-language compatibility have made it one of the most popular languages for machine learning applications. 

Data preprocessing

Python libraries are used to handle a number of data preprocessing tasks including missing data management, feature scaling, and dataset splitting to get training and testing sets.

NumPy, Pandas and scikit-learn are popular choices for ensuring models are trained on relevant, high-quality data.

Model building and training

AI is a broad term that includes LLMs, deep learning, generative AI and more. Numerous Python libraries exist for building and training models for different AI domains. Some provide features that make them ideal for specific use cases. For example, Keras specializes in neural networks, while Pytorch provides a toolkit well-suited for NLP and computer vision applications.

Hyperparameter tuning

Hyperparameters control a model’s learning process. Selecting the right combination maximizes model performance. Several machine learning libraries for Python have strong hyperparameter optimization capabilities. Hyperopt is a popular library for hyperparameter tuning, including random search, Tree-structured Parzen Estimator (TPE) and adaptive TPE. 

Visualization

ML visualization techniques make complex model structures and data patterns easier to understand. By representing models and data graphically, developers and other stakeholders can better understand the algorithms and data paths, making results easier to interpret. Popular Python libraries like Matplotlib, Seaborn and Bokeh provide options for static and interactive visualizations.

Tap into the Full Potential of Generative AI and ML with Snowflake

Snowflake Cortex AI is a fully managed service designed to unlock generative AI’s potential for everyone within an organization, regardless of their technical expertise. It provides access to industry-leading LLMs, allowing users to easily build and deploy AI-powered applications. With Cortex AI, enterprises can bring AI directly to their governed data, extending access and governance policies to the models.

Cortex offers a unified platform for secure LLM development and deployment, providing efficient, easy-to-use and trusted solutions. These solutions can be accessed via no-code, SQL, Python and REST API interfaces.

Snowflake Notebooks: Simplify LLM training and evaluation

Snowflake Notebooks, now available in public preview, enables data teams proficient in SQL, Python or both, to run interactive analytics, train models or evaluate LLMs in an integrated cell-based environment. This eliminates the processing limits of local development and the security and operational risks presented by moving data to a separate tool. Integration with Streamlit libraries makes it possible to share insights as interactive applications by taking code developed in Notebooks and deploying it in Streamlit in Snowflake. 

Cortex Analyst: Data-driven answers using natural language

Cortex Analyst, coming soon to public preview, allows business users to query their data using natural language, providing unprecedented access to data-driven insights.  Cortex Analyst is designed to turn questions into answers and do so from any application that business users interact with on a daily basis. Developers simply provide a semantic model during the setup, and Snowflake handles the heavy lifting through a combination of state-of-the-art LLMs from Meta and Mistral AI.

Cortex Search: Enterprise-grade document search and chatbots

Cortex Search simplifies the implementation and integration of search in your applications. It offers a robust set of capabilities to index and query unstructured data and documents, managing the end-to-end workflow for data ingestion, embedding, retrieval, reranking and generation. The embedding process is completely automated for easy configuration. Our state-of-the-art hybrid search enables better results.

Document AI: Streamline document data extraction for business users

Document AI, in general availability soon, provides a new framework to easily extract content like invoice amounts or contract terms from documents using Arctic TILT, a state-of-the-art built-in, multimodal LLM. Nontechnical business users can use the natural language interface to define the set of fields or values that need to be extracted and if necessary, fine-tune the model to better understand specific document formats. 

Cortex Guard: Use generative AI safely with a comprehensive set of safety controls

In general availability soon, this feature allows users to filter harmful content associated with violence and hate, self-harm and criminal activities. Safety controls can be effortlessly applied to any LLM in Cortex AI. Using Cortex Guard, organizations can quickly implement the safety controls necessary to deliver gen AI in production applications. 

Snowflake AI & ML Studio: No-code AI development 

Snowflake AI & ML Studio, part of Snowflake Cortex AI and now in private preview for LLMs, brings no-code, AI development to the AI Data Cloud. Studio provides interactive interfaces for teams to quickly review multiple models with their data and compare results, helping them accelerate deployment to applications in production. New features include an interface to compare and evaluate responses of multiple LLMs from a single prompt, execute LLM fine-tuning and more. This no-code experience makes it easier for teams to evaluate and select the state-of-the-art model that best fits their task and cost goals and is especially valuable for generative AI development.

Snowflake positions organizations to realize the full potential of AI-driven innovation. Snowflake Cortex AI, our unified platform for secure development and deployment allows developers to quickly train, evaluate and deploy powerful generative AI and ML models. With cutting-edge generative AI features, business users can query their data using natural language, providing on-demand access to the data-driven insights needed to drive truly intelligent decision-making.