Leverage The AI/ML OS That Powers The xpresso.ai Platform

Full hosted and secure environment focused on improving data scientist’s productivity by integrating features and functionality that result in accelerated performance

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Integrated Operations Across DataOps, MLOps And DevOps With Built In CI/CD/CT Capabilities

xpresso.ai : AI Application Lifecycle

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Enable Broad Set Of

Integrated Tools And Best Practices To Simplify, Organize And Optimize The Journey To Build Cognitive Solutions

1

xpresso Controller

xpresso.ai AI/ML stack incorporates open, standard software for machine learning. xpresso’s core controller enables data science solution building with best-of-breed security, monitoring, CI/CD, workflows, and configuration management capabilities to

  • setup the workspace for data scientists
  • create solutions with pre-configured code repository, generated skeleton object oriented code, automated CI/CD pipelines and deployable container registry
  • create pipelines, deploy experiments, and monitor the model behavior
  • enable creation of modular and reusable components
  • manage each project using a standardized structure that is easy to scale maintain, and collaborate

VERSIONED DATA
REPOSITORIES

Raw / Processed Data / Meta Data

2

Data Source & Connectivity

xpresso.ai Data Sources & Connectivity module enables to

  • leverage existing open source plugins and connectors
  • connect to any data source available in structured, un-structured and streaming formats
  • create custom connectors for any APIs, databases, or file-based formats
  • manage multiple internal and external data feeds within enterprises
3

Data Management

xpresso.ai data engineering and management architecture is capable of consuming wide range of data sources with high volume and velocity to

  • explore the data and visualize
  • transform, manipulate and prepare
  • to track and refine the data with inbuilt version control libraires
4

Data Science Workbench

xpresso empowers data scientists to use their preferred technologies, languages and libraries in an virtual desktop environment that can be local to their environment or part of the broader enterprise-wide infrastructure

5

Model Management

xpresso.ai AI/ML Model Management enables to

  • version control all code, models, and workflows; allows to concurrently run multiple versions of models to optimize results, and provides ability to rollback to previous versions when needed
  • utilize code repository push/pull commands to move consistent packages of ML models, data, and code into Dev, Test, and Prod environments
  • deploy machine learning systems to production with the ability to run many models and multiple versions of models at the same time

Deploy, Run / Pause / Restart / Stop Expirements

6

Deployment Management

xpresso.ai Deployment Management modules are designed to

  • alleviate some of the more tedious tasks associated with machine learning
  • manage deployment of machine learning apps through the full cycle of development, testing, and production, while allowing for resource scaling as demand increases
  • achieve fault tolerance and scalability for the REST services using containers and Kubernetes clusters for the deployments
7

Enterprise Applications

xpresso.ai provides a collaborative environment to share resources, reuse components, and rapidly build AI solutions with easy to use tools. Leverage MLOps capabilities and automation to

  • empower data science teams to build, train and deploy machine learning models efficiently and at scale
  • accelerate the process by which an idea goes from development to production
  • enable secure and reliable deployments to observe model behavior in the production

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