Machine Learning Platform on PaaS with Azure.
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“Prodevans’ Machine Learning Platform, iVentura™ – the Data Detective, when deployed on Red Hat OpenShift and Azure 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. We now associate them with self-driving cars, intelligent personal assistants, smart home systems and a lot more. Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being used to turn data into assets, thereby laying the foundation for the next wave of Digital Transformation. As it is widely acknowledged, developing solid digital platforms can lead to significant competitive advantage.
Organizations are increasingly investing in and adopting artificial intelligence (AI) and machine learning (ML) to better serve their customers, create value, grow their business, and reduce cost and complexity.
ML methods are implemented as components of intelligent applications which must be done in a repeatable, scalable, and resilient manner. Furthermore, some of this requires specialized (and often expensive) hardware resources, which increases the importance of resource management and utilization. While data scientists have access to data and hardware for training and serving models today, the entire process can b complex, inflexible, incomplete and cumbersome. As a result, the impact of advances in ML has largely been limited. This is where Red Hat OpenShift deployed on Azure can open up opportunities.
Why Red Hat OpenShift?
Developers are increasingly embracing containers and Kubernetes to help accelerate application development and deployment. Leveraging containers and Kubernetes, RedHat Openshift can abstract and simplify access to underlying infrastructure and provide robust capabilities to manage application lifecycle and development workflows.
OpenShift, with additional capabilities for self-service, build and deployment and automation, further enhances this experience. Additional features in security, storage, networking, monitoring, and observability make it well suited for enterprise environments.
OpenShift is therefore well positioned to manage the complexity of ML and to democratize access to these techniques.
Providing the Cloud Infrastructure will be Azure, the industry’s only truly consistent and comprehensive hybrid cloud platform, which enables a unified approach to application development.
The ML-as-a-Paas Offering can leverage and improvise on the already mature MicroSoft’s ML Platform. Also, workloads can now be moved from on-premise environments to Azure Cloud thus allowing container platforms across both sides of Hybrid infrastruture
To showcase ML Capability on OpenShift, Prodevans chose a use-case that can be widely used and deployed. The Facial Recognition Intelligent application, an in-house development of Prodevans, uses the algorithm called TensorFlow to process image data. TensorFlow is designed to work with large data sets made up of many different individual attributes.
Any data that is to be processed with TensorFlow has to be stored in the multi-dimensional array requiring a lot of computational power. This processed data is then accessible for developers through Jupiter Notebooks. Making the whole ecosystem truly scalable will be JupyterHub that allows developers to save their code in persistent storage for later retrieval
High Level Diagram
Use Case: Deployment at Red Hat Forum
Prodevans’ Facial Recognition Intelligent application was deployed at the registration desk at the Red Hat forum. Candidates interested in knowing futher details were asked to provide their details. While doing so, the application using three cameras, captured the face image grabs as the input for the Machine Learning Algorithm.
During exit, the application identified the person and flashed their details on the screen, as soon as it did the image grab of their faces. Once done, helping reinforce the utility of the platform, the visitors were given a prize as a token as appreciation
Prodevans Intelligent Facial Recognition Application deployed with Red Hat OpenShift on Azure, will encourage users and will make it easier for developers to build intelligent Machine Learning applications.
Prodevans will facilitate discussions and disseminate best practices for deploying ML applications and workloads on Red Hat OpenShift and Azure.