Splice Machine ML Manager

Make Better Decisions Faster with Splice Machine ML Manager

Splice Machine ML Manager Overview

Splice ML Manager is an integrated machine learning platform that minimizes data movement and enables enterprises to deliver better decisions faster by continuously training the models on the most updated available data.

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Categories:
All
MLOps
Data
Data+MLOps

System-wide Features:
Teamwork and Collaboration
Enterprise Security
Governance
Enterprise Support

MLOps Features:
Data Acquisition
Data Versioning
Data Visualization
Data Preparation
Data Pipelines
Data Labeling
AutoML
Featurization
Feature Store
ML Pipelines or Workflows
Model Registry
Model Marketplace
Model Training
Distributed Model Training
Model Debugging
Experiment Management
Deep Learning Support
Reinforcement Learning Support
Bias Detection and Mitigation
Model Explainability
Hyperparameter Optimization
Model Packaging
Model Deployment and Serving
Edge ML Support
Model Monitoring
Cost Management
ML Infrastructure Orchestration
Accelerator Support
Kubernetes Support

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Additional Product Information

Product Links

Deploys On

Amazon Web Services
Google Cloud Platform
Microsoft Azure
Other Public Cloud
Kubernetes
NVIDIA
Private Cloud or Datacenter
SaaS

Marketplace Links

Splice Machine ML Manager on AWS Marketplace

Splice Machine ML Manager Features and Benefits

Benefits

With Splice ML Manager, data science teams are able to produce a higher number of more predictive models as they are empowered to:
• Experiment frequently using diverse parameters to compare model effectiveness
• Leverage updated operational data to concurrently train the model
• Minimize the movement of data by running the models in the database
• Compress the time from model deployment to action

Features

Seamless Database Deployment
Deploy machine learning models as intelligent tables inside a unique hybrid (HTAP) database, removing endpoints and containers so models can be deployed with just one line of code

World-Class Feature Store
Leverage a single engine feature store for better performance with none of the latency or sync issues that can occur with separate online and offline databases

Support for Jupyter Notebooks
In Splice Machine 3.0 Jupyter notebooks are the standard. Splice Machine’s native Jupyter support comes with JupyterHub as well as BeakerX

Industry-Leading Libraries
Access to all of the open source ML frameworks like H2O, SparkML, Scikit-learn, Keras and more

Ease of Use Across the Entire Product
Access to Apache Spark’s ML library, including algorithms, featurization, pipelines, persistence and utilities

Rapid Experimentation
MLflow to manage the experiments and model runs based on key parameters, versions and metrics

Superior Performance
Direct API between Splice Machine tables and Spark Data Frames for high performance

Splice Machine Vendor Information

Vendor Overview

Splice Machine is a data platform that offers offline, and batch analysis, and powers intelligent applications for operational workflows. Splice Machine is disrupting the $30 billion traditional database world with the open-source dual-engine RDBMS for mixed operational and analytical workloads, powered by Apache Hadoop and Apache Spark. The Splice Machine RDBMS executes operational workloads on Apache HBase® and analytical workloads on Apache Spark. Splice Machine makes it easy to develop and create modern, real-time, scalable applications, or to offload operational and analytical workloads from expensive Oracle, Teradata, and Netezza systems. Typical use cases are ETL, operational reporting, or real-time applications.