Productionizing Machine Learning Workflows
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.
Support for Python, Java, and Scala languages.
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.
Set up internal services and manage your entire data science applications from the command line.
import skil_clientuploads = client.upload("tensorflow_rnn.pb") new_model = DeployModel(name="recommender_rnn", scale=30, file_location=uploads.path) model = client.deploy_model(deployment_id, new_model) ndarray = INDArray(array=base64.b64encode(x_in)) input = Prediction(id=1234, prediction=ndarray, needsPreProcessing=false) result = client.predict(input, "production", "recommender_rnn")
|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||✖||✔|