Xpresso.ai accelerators for a AI development and deployment

Digitalized Facts (DF) has a systematic approach towards enterprise AI solutions using the framework "Xpresso.ai". The platform enables a comprehensive approach towards building enterprise Artificial Intelligence (AI) solutions. With its five-stage process-based cognitive journey, xpresso.ai delivers AI solutions using a reproducible methodology in a timely and robust manner. The enterprise AI journey starts with use case discovery, data intake to data preparation, and cognitive modeling that leads to actionable insights.

xpresso.ai is optimized for AI-based analysis and operational-wide environments, from an account, package, source version, data management, application development to deployment, operations, and monitoring. It also enables additional data engineering capabilities to manage Big Data. The platform provides scalable, reliable, easy-to-use, automated tool kits and accelerators to build useful complex AI solutions with MLOps Environment and microservices repository. It also provides accelerators for each phase of the journey that are built for specific cognitive goals and are enterprise-tested with our customers. Depending on the cognitive maturity of the enterprise, xpresso.ai leverages appropriate accelerators to manage the AI/ML.


Use Case Discovery

Data Engineering

AI/Dev Ops

Data Science

Cognitive AI Solutions

xpresso Data Connectivity

Our data Connectivity seamlessly connects any data source available in structured, un-structured and streaming formats. Enables easy creation of custom connectors for any APIs, databases or file-based formats which leverages existing open source plugins and connectors.

xpresso Data Engineering

Data engineering and management architecture capable of consuming various data sources in a fast and inexpensive manner. Multiple internal and external data feeds within enterprises from various sources can be processed in parallel and merge a wide variety of data coming in at high velocity and high volume.

xpresso Data Storage

All the data sources are funneled into the data storage Layer after systematized validation and cleansing. The storage landscape with different storage types and extreme flexibility is built-in to manipulate, filter, select, and co-relate different data formats. Various data adapters are available through a common catalogue of services which simplifies interoperability and scalability concerns, enable APIs and abstract all the technical complexities from the service consumer.

xpresso Cognitive Framework

Leverage the latest Machine Learning/Deep Learning/Big Data libraries to build a flexible cognitive framework with ability to integrate external packages through API and flexibility to handle automated feature engineering, model parameter optimization and get actionable visual. insights.

xpresso Solution Components

Offers a range of modularized solution components like Natural Language understanding, Semantic Knowledge engine, recommendation engine, information retrieval engine, summarization engine etc, in a microservice architecture framework.

xpresso Infrastructure

Ability to process large volume of data in parallel with high flexibility and scalability framework. Dockerized components make these easy to deploy in production environment (on-premise or in the cloud)

Each stage contains its own approaches:

1. Discovery:

  • Business understanding

  • Identify use cases

  • Feasibility study

3. Cognitive AI:

  • Learning algorithms

  • Model building / training

  • Model Experimentation

5. Deploy:

  • Container driven / cluster

  • Online / offline

  • Feasibility CI / CD

7. Operate:

  • End User Application

  • Micro Services

  • Real Time

  • Optimization

2. Data Management:

  • Data understanding

  • Data sources

  • Data preparation

4. Evaluation:

  • Model evaluation

  • Model accuracy

  • Model Serving

6. Automate:

  • Pre-trained model

  • Realted Models

  • Parallel Pipelines

  • Library of Models

8. Monitor:

  • Model Prediction

  • Model Visualization

  • Logging / monitoring

Implementation Benefits

  • Enables a streamlined and structured methodology with a cognitive framework to embark on a successful AI transformation.

  • Quick integration of reusable components to accelerate robust AI solution deployment.

  • Availability of each and every component of AI in the framework.

  • Easily configurable and deployable in any cloud (such as AWS and Azure) or on-premise infrastructure.

  • Feedback loop for continuous improvement.

  • Flexibility to plug-in open-source AI packages and libraries.