Machine learning libraries are incredibly useful tools that provide pre-built functions and algorithms to streamline the development, training and deployment of machine learning (ML) models. Different libraries are designed to serve different purposes, from data preparation to training to anomaly detection and more. In this article, we’ll explore how ML libraries accelerate development and simplify deployment. We’ll highlight five popular machine learning libraries and explore how Snowflake enables organizations to get the most out of ML libraries and integrate ML into products and services.
What Are Machine Learning Libraries?
Machine learning libraries are collections of reusable, pre-built components, utilities and conventions that automate low-level programming tasks that are part of the ML workflow. By clearing away the busy work, machine learning libraries allow machine learning operations (MLOps) teams to focus their attention on higher-level work such as model design and evaluation. There are many types of machine learning libraries. General-purpose libraries offer algorithms and utilities useful for completing common ML tasks such as classification, regression and clustering. Other machine learning libraries provide support for specific tasks such as data analysis and manipulation, data visualization, natural language processing (NLP), computer vision and deep learning.
Why Use Machine Learning Libraries?
Machine learning libraries dramatically reduce the time required to develop and deploy ML models. Here are three ways ML libraries streamline the ML lifecycle.
Faster development cycles
ML libraries offer prepackaged collections of algorithms and other functions so teams can avoid reinventing the wheel for common development tasks. With ML libraries available for every use case, teams can easily access a comprehensive set of resources ideal for supporting their specific use case.
Simplified workflows
ML libraries remove much of the complexity involved in ML tasks, such as data preprocessing, model training, deployment and monitoring. Libraries play an essential role in automating large portions of the ML lifecycle, allowing teams to dedicate more attention to higher-level, value-added development work.
Community support
Popular ML libraries are backed by large communities of active users. These communities host online forums and offer extensive amounts of support resources. For both new and experienced developers, these communities are a place to learn, get help troubleshooting issues, and stay up to date on new features and best practices.
Leading Machine Learning Libraries
There are hundreds of open source machine learning libraries that data scientists and engineers use to perform various tasks in the machine learning lifecycle. To illustrate the value of ML libraries, let’s look at five that have become mainstays in ML development.
Scikit-learn
Built on NumPy, SciPy, and Matplotlib, the open source scikit-learn machine learning library is popular for predictive data analysis. Written in Python, it includes a large collection of algorithms that can be used for both supervised and unsupervised ML training projects.
XGBoost
XGBoost is a popular machine learning library for gradient boosting, which is the practice of sequentially combining the predictions of multiple weak learners into a more accurate model. This library offers parallel tree boosting, an additive training method used in gradient boosting. XGBoost can be deployed in a wide range of applications, such as regression, classification and ranking. XGBoost also features cloud integrations such as Snowflake.
LightGBM
LightGBM is another machine learning library for gradient boosting. As the name implies, this decision-tree-boosting framework optimizes for speed and efficiency. It uses histogram-based algorithms rather than presort-based algorithms, allowing it to accelerate training time while using fewer compute resources.
TensorFlow
TensorFlow is a widely used machine learning library for building and training AI models, including deep learning and neural networks. Popular for use in image and speech recognition, NLP, and various computational-based simulations, Tensorflow can support applications on CPUs, GPUs and clusters of GPUs.
PyTorch
PyTorch is a robust framework commonly used for building deep learning models. It is written in Python and based on the Torch library. Its strong support for GPUs and its use of reverse-mode auto-differentiation, an ML technique for solving problems with many variables, make it an excellent choice for image recognition and language processing use cases.
Snowpark ML: End-to-End Machine Learning in Snowflake
Snowpark ML contains the APIs for building end-to-end ML workflows in Snowflake. From feature engineering to model training and deployment, Snowpark ML allows developers to preprocess data, and train, manage and deploy ML models, all within Snowflake. Snowpark includes two primary components: Snowpark ML Modeling and Snowpark MLOps.
Snowpark ML Model Development
Snowpark ML Model Development includes a collection of Python APIs you can use to develop models efficiently inside Snowflake:
The modeling package provides APIs for data preprocessing, feature engineering and model training. The package also includes a preprocessing module with APIs that use compute resources provided by Snowpark-optimized data warehouses to provide scalable data transformations. These APIs are based on familiar ML libraries, including scikit-learn, XGboost and LightGBM.
A set of framework connectors provide optimized, secure and performant data provisioning for PyTorch and TensorFlow frameworks in their native data loader formats.
Snowpark Model Registry for MLOPS
The path to production from model development starts with model management, which is the ability to track versioned model artifacts and metadata in a scalable, governed manner. The Snowpark Model Registry allows customers to securely manage models and their metadata, such as versions, in Snowflake. This supports not only models built in Snowflake, but also models trained externally, including PyTorch and TensorFlow model types. The Snowpark Model Registry stores machine learning models as first-class schema-level objects in Snowflake, with full role-based access control (RBAC) support.
Using Snowpark ML, data scientists can develop, test and manage ML models directly in Snowflake using familiar Python ML frameworks without any data movement. Data scientists and ML engineers can leverage Snowflake’s proven performance, scalability, stability and governance at every stage of the machine learning workflow.