Layer

Overview

Layer is a comprehensive MLOps platform designed for data science teams to build, train, and deploy machine learning models more efficiently and reliably. It provides a collaborative environment that integrates various stages of the ML lifecycle, including data management, feature engineering (via a Feature Store), model training, experiment tracking, model registration, deployment, and monitoring.

The platform aims to remove infrastructure complexities and manual tasks, allowing data scientists and engineers to focus on model development and innovation. Key benefits include accelerated model delivery, improved reproducibility, enhanced collaboration among team members, and robust governance over the ML pipeline. Layer helps organizations operationalize their machine learning initiatives at scale.

Key Features

• Feature Store for managing and serving features
• Model Registry for versioning and managing models
• Experiment Tracking for logging and comparing training runs
• Model Deployment and Serving
• Production Monitoring for deployed models
• Automated pipelines for training and inference
• Collaboration tools for data science teams
• Data Versioning and lineage tracking
• Reproducibility of ML workflows

Supported Platforms

• Web Browser
• API Access
• Python Client (SDK)

Integrations

• Various Data Sources (e.g., S3, GCS, Snowflake, BigQuery, Databases)
• ML Frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM)
• Cloud Providers (AWS, GCP, Azure)
• Orchestration/Deployment Tools (e.g., Kubernetes, Seldon, BentoML)
• Notebooks (e.g., Jupyter)