End to End Solution Implementation
End to End Solution Implementation

End to End Solution Implementation

The building blocks of an end to end AI implementation will include the development and implementation of a required data strategy as well as machine learning models as required. This should be deployed as a scalable, fault tolerant solutions. The end implementation might vary as per the business case which may be a Web application, desktop tools, movile apps, backend APIs, cloud deployments, BI dashboards and so on.

IMPLEMENTATION ASPECTS

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DATA PIPELINES

Our expertise ranges from data pipelines by cloud providers like Azure, AWS, IBM, GCP to open source Apache stack. The business decision of how to implement data pipelines depends on backward and forward compatibility as well as cost required

ML INFRASTUTURE

DevOps has been an emerging area in the deployment of Ai in business. For a successful deployment the planning needs to start at inception so that codes are developed and packages in a reusable scalable format. While in the hands of experienced data scientists and devops engineers, open source technologies give more customisation and can work wonders, cloud based devops services might be a better way to go if lacking in technological expertise

 
 
 
 
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DEVOPS AUTOMATION

Automation, AI, and DevOps: we tend to think about them synonymously, and there is a good interdependency between them. DevOps is a business-driven approach to delivering software. AI makes up the technology that integrates into that system. AI has two intersection points: with the tools DevOps teams use and the people who run them. The first point in this automation is Infrastructure as a code. IaC creates the right environment to automate your testing further than you may be doing it now, especially on the infrastructure end of things.

AUTOMATED DEVOPS CONTINUOUS IMPROVEMENT

We can use AI to improve the DevOps process and AI software itself. Continuously. The salient point to be noted about continuous integration, delivery, and deployment, is: there is a tremendous amount of data generated from these logs and process flows. Some more practical uses include: 1> Rooting out code that breaks builds 2> Scoring software packages 3> Simulating production environments

 
 
 
 
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