Build and deploy Machine Learning models at Enterprise scale

Innovate, differentiate, and modernize with data science and machine learning.

Top Common ChallengesSolution
Setting up interfaces manually to collaborate with peers and connect to data sources and infrastructureProvides choice to use preferred interface such as Rstudio, Python or Jupyter while providing a common workspace where everyone on the data team can collaborate
Coordinating specific dependencies between OS, programming language, and the libraries to build models.Solves code portability problem by keeping dependencies intact from one environment to another and also does version control
Inefficient ad-hoc collaborative methods via emails or slackAllows continuous collaboration and governed searches of the code, data, and metadata of peers
Deployment of enterprise-wide machine learning solutions requires the applications to rapidly scale to accommodate variability in the usage or dataScales up compute capacity to meet demand and scales down when the usage drops automatically while optimizing the cost based on workload SLA and performance requirements.

Build Machine Learning Models Faster

Data Scientists can build, deploy and iterate on their models faster with

  • Experiment Tracking
  • Out-of-box Integrations for front end tools: RStudio,, Datarobot
  • End-to-End Workflows anchored by schedulers and Airflow
  • Managed Notebooks – Serverless (Offline) Editing

Achieve higher developer productivity

Developers can now skip steps and build applications with

  • Code auto-complete
  • Code compare
  • Code-free visualizations (QVIZ)
  • Version control
  • Hands-free dependency management
  • Easy access to cloud storage and data catalog

Automate Infrastructure Provisioning for ML

  • Minimize costs automatically while supporting concurrent user growth without a performance impact
  • Have near-zero management overhead regardless of the number of users or model versions
  • Scale up or down automatically to support all workloads at any point in time

Customer Case Studies

Ecosystem Partners

Streamlining Operations of Machine Learning Models
Big Data Engineering for Machine Learning
Leveraging Streaming and Batch Data Sets for ML Applications