Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.
Machine Learning Use Cases
Machine learning has applications in all types of industries, including manufacturing, retail, healthcare and life sciences, travel and hospitality, financial services, and energy, feedstock, and utilities. Use cases include:
Manufacturing. Predictive maintenance and condition monitoring
Retail. Upselling and cross-channel marketing
Healthcare and life sciences. Disease identification and risk satisfaction
Travel and hospitality. Dynamic pricing
Financial services. Risk analytics and regulation
Energy. Energy demand and supply optimization
iVentura-The data detective
when deployed a PaaS platform will facilitate a future where developers and data scientists can easily access and consume AI and ML technologies and capabilities in support of their business and organizational goals.
AI and ML terms once reserved for academia and research,have permeated their way into the knowledge of general public.Artificial Intelligence(AI) and machine learning(ML) are increasingly being used to run the data into assets,thereby laying the foundation for the next wave of digital transformation. Organization are increasingly investing in and adopting artificial intelligence and machine learning to better serve their customers,create value,grow their business and reduce cost and complexity.
Why ML on PaaS
Developers are increasingly embracing containers and kubernetes to help accelerate application development and deployment. Leveraging containers and kubernets, PaaS platform such as RedHat Openshift can abstract and simplify access to underlying infrastructure and provide robust capabilities to manage application lifecycle and development workflows. With its additional capabilities for self-services build ,deployment and automation, the PaaS platform further enhances this experience. Additional features in security, storage ,networking, monitoring and observability make it well suited for enterprise environments.
Prodevans Machine Learning Platform, deployed on Kubernetes flavours will facilitate a future where developers and data scientists can easily access and consume AI and ML technologies and capabilities in support of their business and organizational goals.
Intelligent Resource Management
Stitched with CI/CD Pipeline
100% Cloud Agnostic. No Vendor Lock-ins
No burden of building in-house Infra from Scratch
Top of the Line Support
Simple to Use & Deploy