The evolution of Data Engineering with AI

As Artificial Intelligence (AI) continues to become a focus for an increasing number of enterprises, these organizations are realizing how important it is to have the right people and skills in place.

Akira AI For Engineering

Akira AI allows engineers to integrate models from their own team or the AI Marketplace with ease.

  • Modern Agile & DevOps Architecture
  • Build Intelligent Applications, Digital Virtual & Predictive Agents
  • Include any data sources.

Powerful Solutions & Features from Data Engineering Platform

Akira AI Platform enables you to Automate the infrastructure to train and deploy Deep Learning Models on Public Cloud as well as On-Premises.

  • Deploy any model in minutes
  • Advanced Monitoring
  • Autoscale to match any demand
  • Empower your data scientists
  • Optimized GPU usage

Deploy a pre-built app or distro

Skip the installation and configuration and get straight to deploying your code.

Deploy a pre-built app or distro

Skip the installation and configuration and get straight to deploying your code.

Gathering data

Skip the installation and configuration and get straight to deploying your code.

Training, testing model

Skip the installation and configuration and get straight to deploying your code.

Automated Machine Learning

Skip the installation and configuration and get straight to deploying your code.

Transform into an AI-driven enterprise

Skip the installation and configuration and get straight to deploying your code.

All in One Place

Technologies Integrations

All of the ways Akira AI can fit into your workflow. We’ve built dozens of native integrations, for different Data Sources, Programming Languages & Framework, and Intelligence & Analysis Tools, so anyone can build and work with Akira AI Platform.

  • 10+

    Data Sources & Connectors
  • 10+

    Programming Languages
  • 6+

    Popular Framework
  • 6+

    Intelligence & Analysis Tools

Blog & Use Cases on Data Engineering

Read our Thought Leadership Driven content on Best Practices, Implementation and Use Cases of AI For Engineering.

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  • Use Cases arrow_right_alt

Blog
Blog

Influential and Informative Deep Learning and AI Blogs

Here Jenkins shall be used in order to create a continuous integration pipeline for NodeJs applications.

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Documentation
Blog

Build and Deploy Continuous Delivery Pipeline with Jenkins

Automate analytical model building, using algorithms that iteratively learn from your data.

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Use Cases
Use Cases

Provide Envied Customer Engagement

Deploy applications that process massive amounts of events in real time.

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Documentation
Use Cases

Deliver Better Analytics Recommendations

Automate analytical model building, using algorithms that iteratively learn from your data.

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