Serverless AI Partner
Produvia is a serverless machine learning development service. Partner with Produvia to develop and deploy machine models using serverless cloud infrastructure.
Fortune 500 companies and Global 500 enterprises partner with Produvia develop and deploy machine learning models using modern cloud infrastructure.
At Produvia, we use state-of-the-art methods in machine learning and deep learning technologies to solve business problems.
Organizations overspend on infrastructure costs. Modern organizations use serverless architectures to reduce server costs.
Be More Productive
Organizations are held back by complex servers and legacy code. Modern organizations use machine learning technologies to rewrite technology stacks.
Companies hire software developers to write code. Modern companies use machine learning to develop software that writes code.
Build Business Resilience
Organizations struggle to cope with COVID-19. Modern organizations use machine learning to develop scalable and low-cost solutions in response to COVID-19.
Companies want to automate tedious, repetitive, and boring tasks. Modern organizations collect data and apply machine learning technologies to automate everything.
What People Say About Serverless
Fortune 500 companies and Global 500 enterprises use Produvia to hire machine learning researchers and engineers.
Serverless is one of the hottest design patterns in the cloud today, allowing you to focus on building and innovating, rather than worrying about the heavy lifting of server and OS operations.
If we end up in a situation where we need to cobble together several tools and services to build and manage serverless applications, something has gone wrong. Serverless is about simplifying software, by radically reducing its complexity.
While the cost benefits are the most easily expressed improvements with serverless, it’s this reduction in lead time that makes me most excited. It can enable a product development mindset of continuous experimentation, and that is a true revolution for how we deliver software in companies.
Technologies we use
Here are a list of technologies we use
Libraries, Frameworks, Tools
Developing and maintaining machine learning (ML) systems is costly. ML practitioners (Machine Learning Researchers, Engineers, Developers, and Data Scientists) incur multiple costs as they shift from prototype to production. Understanding real costs (true costs) of ML systems allows companies to better develop prototypes and deploy models into production.
Ready to start?
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