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.
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.
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: