Introduction
Introduction to Ai-APIᵀᴹ
Overview
Ai-API™ makes moving trained ML models to production easy:
Package models trained with ML framework and then containerize the model server for production deployment
Deploy anywhere for online API serving endpoints or offline batch inference jobs
High-Performance API model server with adaptive micro-batching support
Ai-API™ server is able to handle high-volume without crashing, supports multi-model inference, API server Dockerization, Built-in Prometheus metric endpoint, Swagger/Open API endpoint for API Client library generation, serverless endpoint deployment etc.
Central hub for managing models and deployment process via web UI and APIs
Supports various ML frameworks including:
Scikit-Learn, PyTorch, TensorFlow 2.0, Keras, FastAI v1 & v2, XGBoost, H2O, ONNX, Gluon and more
Supports API input data types including:
DataframeInput, JsonInput, TfTensorflowInput, ImageInput, FileInput, MultifileInput, StringInput, AnnotatedImageInput and more
Supports API output Adapters including:
BaseOutputAdapter, DefaultOutput, DataframeOutput, TfTensorOutput and JsonOutput