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

Easy steps to Ai-API™ Deployment

1. Select your notebook

2. Build Model

3. Deploy