SKIL Documentation

Skymind Intelligence Layer

The community edition of the Skymind Intelligence Layer (SKIL) is free. It takes data science projects from prototype to production quickly and easily. SKIL bridges the gap between the Python ecosystem and the JVM with a cross-team platform for Data Scientists, Data Engineers, and DevOps/IT. It is an automation tool for machine-learning workflows that enables easy training on Spark-GPU clusters, experiment tracking, one-click deployment of trained models, model performance monitoring and more.

Get Started

SKIL reduces the friction between experimental data science modelling, key testing and product decisions, and scalable deployment engineering. It bridges the gap between the Python ecosystem and a deployment architecture for Devops, IT, and Data Engineers.

From Start to Finish

Teams using SKIL can expect support for the following workflows:

  • Model and data configuration
  • DNN Training
  • Collaborative user interfaces for data and results
  • Versioning of experiments and models
  • Scalable microservice deployment architecture
  • APIs for model serving
  • Management UI

Integrated with Hadoop and Spark, SKIL is designed to be used in business environments on distributed GPUs and CPUs on-prem, in the cloud, or hybrid.

Tooling & Configuration

Before learning about different workflows such as Transforming Data or Deploying Transforms, it's important you understand some basic tools. This includes:

Configuration is just as important especially when dealing with issues like out-of-memory errors (OOMs). These basic configurations can help you avoid common issues:


Overview