Utilize DevOps speed and agility for machine learning. Overcome the challenges of operationalizing ML models.
DevOps speed and agility for machine learning
The ability to apply artificial intelligence (AI) and machine learning (ML) to unlock insights from data is a key competitive advantage for businesses today. Today’s modern enterprises understand the benefits machine learning provide, and want to expand its use.
A machine learning app is an application that incorporates machine learning algorithms to perform specific tasks without being explicitly programmed to do so. Machine learning algorithms use statistical techniques to learn from data, identify patterns, and make predictions or decisions based on that learning.
Machine learning apps can be developed for a wide range of use cases, such as image and speech recognition, natural language processing, recommendation systems, predictive maintenance, fraud detection, and many more. These apps can be built using a variety of programming languages and frameworks, and can run on a range of platforms, including desktops, mobile devices, and the cloud.
Examples of machine learning apps include virtual assistants like Siri and Alexa, image recognition apps like Google Photos and Facebook’s face recognition feature, and recommendation systems like Netflix’s movie and TV show recommendations.
However, as organizations attempt to operationalize their ML models, they encounter last mile problems related to model deployment and management. RTS’ ML Ops provides DevOps-like speed and agility to the ML lifecycle and empowers large enterprises to overcome barriers in deploying and operationalizing AI/ML across the organization.
Much like pre-DevOps software development, most data science organizations today lack streamlined processes for their ML workflows, causing many data science projects to fail. Consequently, this inhibits model deployment into current business processes and applications.
It may seem like a straightforward solution to use DevOps tools and practices for the ML lifecycle. However, ML workflows are very iterative in nature and off-the-shelf software development tools and methodologies will not work.
RTS’ ML Ops addresses the challenges of operationalizing ML models at enterprise scale. Public cloud service providers offer disjointed services, and users are required to cobble together an end-to-end ML workflow.
Also, the public cloud may not be an option for many organizations with workload requirements that require on-premises deployments due to considerations involving vendor lock-in, security, performance, or data gravity. RTS ML Ops helps businesses overcome those challenges with an open-source platform that delivers a cloud-like experience combined with pre-packaged tools to operationalize the machine learning lifecycle, from pilot to production.