Machine learning platforms provide users with the tools necessary to develop, deploy, and improve machine learning — specifically, machine learning algorithms. Machine learning platforms automate data workflows, accelerate data processing, and optimize related functionality.
As the amount of data businesses amass increases, so does the importance of applying machine learning and other artificial intelligence (AI) strategies to turn data into insights, drive business decisions, and improve products and services. Machine learning platforms and tools combine intelligent algorithms with data, enabling you to derive business insights and deploy new solutions at scale.
Benefits of Machine Learning Platforms
Data-driven business decisions
Improved products and services
Time and energy saved through automation
Shared insights, as users can share data, models, and related information with collaborative tools
Simplified, scalable data science via user-friendly features and out-of-the-box solutions
Optimized experimentation through data visualization, augmentation, and preparation tools
Users of Machine Learning Platforms
Though machine learning has traditionally been the domain of professional data scientists, machine learning platforms are becoming increasingly easier to use for developing AI solutions with features such as drag-and-drop capabilities and prebuilt algorithms. Some platforms also provide resources for non-technical users to understand machine learning models, the data involved, and their impact on the business.
Types of Machine Learning Platforms
Machine learning platforms fall into one of two categories — cloud and on-premise. Most machine learning platforms operate in the cloud, allowing for flexible resource use and making on-site infrastructure unnecessary. Some machine learning platforms are housed on-premises, either at the business site or another location — usually selected for security or speed (latency) issues.
Machine Learning Platform Features
Machine learning platforms facilitate machine learning from end to end, giving users the ability to manage the entire data lifecycle, from data ingestion to inference. A few essential processes a machine learning platform should enable:
Data ingestion, providing users the ability to integrate and ingest data from various internal and external sources, including enterprise applications, databases, and internet of things (IoT) devices
Data preparation, including data cleansing and data augmentation
Feature engineering, transforming raw data into features that better present the underlying problem to the predictive models
Model training, in which proper values are determined for the various weights and biases from the data by either supervised learning or unsupervised learning
Model deployment, making the models available in production environments where they provide predictions to other systems
Model management, to monitor and manage models and ensure that they are accurate and up-to-date
Model comparison, giving users the ability to quickly compare models to a baseline or a previous result to determine the quality of the built model. Many platforms also have tools for tracking metrics, such as accuracy and loss
Accessible development environment, supporting multiple skill levels with accessible authoring options
Built-in support for familiar frameworks such as ONNX, Python, PyTorch, scikit-learn, or TensorFlow
Enterprise-grade governance, security, and control to help protect infrastructure
Snowflake for Machine Learning Platforms
Snowflake's architecture enables easy data preparation for machine learning model building.
Snowflake was designed from the ground up to support machine learning platforms. In conjunction with tight integrations to Spark, R, Qubole, and Python, Snowflake partners with machine learning platform providers including H20.ai, DataRobot, Dataiku, Amazon SageMaker, and others.
Performance speed is critical for supporting machine learning models. Snowflake can scale up or scale down, and can bear machine learning data preparation responsibilities, reducing data-related burdens from machine learning tools.
By offering a single consistent repository for data, Snowflake removes the need to retool the underlying data every time you switch tools, languages, or libraries. Furthermore, these activities' output is easily fed into Snowflake and made accessible by non-technical users to generate business value.
Snowflake offers best-in-class cloud data analytics capabilities for optimizing your machine learning platform of choice. Regardless of which machine learning platform you choose, Snowflake allows you to consume the results via dashboards, reports, and business analytics tools by leveraging connections to other ecosystem partners such as Tableau, ThoughtSpot, and Sigma. Furthermore, Snowflake allows you to store and replicate your data across any region or across multiple clouds, including AWS, Microsoft Azure, and Google BigQuery.