Bridge Data Science and DevOps
Deliver Prediction Services Anywhere
Getting completed machine learning models into production is challenging. Data scientists are not experts in building production services and DevOps best practices. Trained AI/ML models produced by a data science team are hard to test and hard to deploy. This often leads to a time consuming and error-prone workflow, where a pickled model or weights file is handed over to a software engineering team.
Our Ai-API Engine is a framework within Zeblok’s Ai-MicroCloud™ for serving, managing and deploying completed Ai/ML models. It bridges the gap between data science and DevOps, and enables teams to deliver prediction services in a fast, repeatable and scalable way.
Zeblok’s Ai-API Engine makes moving trained Ai/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
List of APIs
Quick view of the APIs that are successfully deployed
Option to select a namespace
Create and Distribute API
Click Create button to create the API – the Ai-API Engine does the rest, deploying the Ai inference to all your locations
Select Model to Deploy as API
Select the completed Ai/ML model to deploy as an API
Select Data Centers/Edge Locations
Select from the list of your data centers or Edge locations where model is to be deployed