Build, Serve and Deploy

Introduction to Ai-APIᵀᴹ

Overview

AI-API™ makes moving trained ML models to production easy:

• Package models trained with an 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 can 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 the 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