Machine Learning From Notebooks to Production
The Skymind Intelligence Layer (SKIL) launches data science projects into 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.
Universal notebook support for Deeplearning4j, Tensorflow, and Keras.
Starting with the Eclipse Deeplearning4j open-source ecosystem, Skymind has expanded its offering into a full-scale deep learning platform, an on-prem AWS Sagemaker.
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.
|Deeplearning4j||Deep Learning for the JVM on Hadoop & Spark||✔||✔|
|Tensorflow||Importing Pre-Trained Models from TensorFlow||✔||✔|
|Keras||Importing Pre-Trained Models from Keras||✔||✔|
|DataVec||Data ETL Normalization and Vectorization||✔||✔|
|ND4J||High Performance Linear Algebra CPU and GPU Library on the JVM||✔||✔|
|RL4J||Reinforcement Learning Algorithms||✔||✔|
|Model Server||Integrated Model Hosting, Management, and Version Control||LIMITED||✔|
|Model Import||Importing Pre-Trained Models from TensorFlow and Keras||✔||✔|
|Workspaces||Notebook System for Model Construction and Collaboration||LIMITED||✔|
|Hardware Acceleration||Managed CUDA for GPU and MKL for CPU||✔||✔|
|Integration Tooling||Native Integration with CDH and HDP||✔||✔|
|Somatic||Sensor Vision and Control Integration for Robotics||✖||✔|
|Online Community||Access to Community Forum, Videos, and Documentation||✔||✔|
|Development Support||General Feature Engineering and Model Tuning Advice||✖||✔|
|SLA||Guaranteed Uptime and Response Times||✖||✔|