Build, Serve and Deploy

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

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