README.md

PipelineAI Quick Start (CPU + GPU)

Train and Deploy your ML and AI Models in the Following Environments:

• Hosted Community Edition
• Docker
• Kubernetes
• AWS SageMaker

Having Issues? Contact Us Anytime... We're Always Awake.

• Slack: https://joinslack.pipeline.ai
• Email: [email protected]
• Web: https://support.pipeline.ai
• YouTube: https://youtube.pipeline.ai
• Slideshare: https://slideshare.pipeline.ai
• Workshop: https://workshop.pipeline.ai
• Troubleshooting Guide

PipelineAI Community Events

• PipelineAI Monthly Webinar (TensorFlow + Spark + GPUs + TPUs)
• Advanced Spark and TensorFlow Meetup (Global)

PipelineAI Products

• Features
• Products

Consistent, Immutable, Reproducible Model Runtimes

Each model is built into a separate Docker image with the appropriate Python, C++, and Java/Scala Runtime Libraries for training or prediction.

Use the same Docker Image from Local Laptop to Production to avoid dependency surprises.

Sample Machine Learning and AI Models

Click HERE to view model samples for the following:

• Scikit-Learn
• TensorFlow
• Keras
• Spark ML (formerly called Spark MLlib)
• XGBoost
• PyTorch
• Caffe/2
• Theano
• MXNet
• PMML/PFA
• Custom Java/Python/C++ Ensembles

Supported Model Runtimes (CPU and GPU)

• Python (Scikit, TensorFlow, etc)
• Java
• Scala
• Spark ML
• C++
• Caffe2
• Theano
• TensorFlow Serving
• Nvidia TensorRT (TensorFlow, Caffe2)
• MXNet
• CNTK
• ONNX

Supported Streaming Engines

• Kafka
• Kinesis
• Flink
• Spark Streaming
• Heron
• Storm

Drag N' Drop Model Deploy

Generate Optimize Model Versions Upon Upload

Distributed Model Training and Hyper-Parameter Tuning

Continuously Deploy Models to Clusters of PipelineAI Servers

View Real-Time Prediction Stream

Compare Both Offline (Batch) and Real-Time Model Performance

Compare Response Time, Throughput, and Cost-Per-Prediction

Shift Live Traffic to Maximize Revenue and Minimize Cost

Continuously Fix Borderline Predictions through Crowd Sourcing