By Prangya Pandab - June 13, 2022 4 Mins Read
Many businesses have developed and implemented a variety of AI use cases. However, to become a truly AI-enabled organization, several standalone use cases must be developed, maintained, and deployed to address various challenges across the enterprise. Machine Learning Operations (MLOps) promises to make it seamless to leverage the potential of AI without hassle.
Due to increasing digitization and the surge in IoT and the cloud, the world generates petabytes of data that organizations want to mine to obtain business insights, transform operations and drive decisions.
AI and ML insights can help businesses gain a competitive advantage, but they come with their own set of obstacles in terms of development and operations. This is where MLOps comes into play.
While tools for evaluating historical data to generate business insights have been more widely embraced and easier to use, leveraging that data to make judgment calls is a very different story. These technologies have gained popularity over the last decade. They have emerged as appealing solutions for building predictive use cases by leveraging enormous volumes of data to help users deliver consistent results. As a result, businesses can increase operations without increasing employee headcount proportionately.
Previously, data-scientist implementation teams worked in silos, on separate business processes, and with a lack of commitment to IT regulations, utilizing different deployment approaches and development tools.
While the benefits promised are true, replicating them across geographies, client segments, functions, and distribution channels, all of which have their nuances, necessitated a tailored approach across these categories. This resulted in the creation of a slew of specialized models that had to be communicated to individual business teams and significant infrastructure and deployment expenditures.
As Machine Learning has progressed, software providers have begun to offer techniques to democratize model development, allowing users to design unique ML models for different contexts and processes.
Machine Learning Operations to the Rescue
Developing various models that suit diverse goals is less complicated in today’s world. Individual business teams must be equipped with monitoring capabilities and model deployment to become AI-enabled and implement AI at scale successfully.
As a result, software vendors have begun to offer a DevOps-style method to centralize and manage the deployment requirements of many ML models, with each team focused solely on building models that best suit their needs.
MLOps, an emerging methodology for scaling Machine Learning across businesses, is a structured approach that brings together skills, tools, and techniques utilized in data engineering and ML.
Also Read: MLOps: A Promising Way to Tackle Top Machine Learning Challenges
What’s Required for It to Work
By adding DevOps-like capabilities to operationalizing ML models, MLOps helps organizations decouple the operational and development aspects in an ML model’s lifecycle. MLOps are available to businesses in the form of licensable software that has the following features:
Model deployment – The ability to deploy models on any infrastructure is critical at this point. Other advantages include storing an ML model in a containerized environment and scaling options for compute resources.
Model monitoring – Tracking the performance of models in production is complex and necessitates the use of a performance measuring matrix. Models are given to the development team for examination and retraining as soon as they exhibit signs of deteriorating prediction accuracy.
Platform management – MLOps solutions increase reusability and collaboration among many stakeholders, including ML engineers, data scientists, data engineers, and central IT operations, by providing platform-related capabilities such as security, version control, access control, and performance assessment.
In addition, MLOps vendors support a variety of Integrated Development Environments (IDEs) to help democratize the model development process.
While some vendors have built-in ML development capabilities, connectors are being developed and integrated to handle many ML model file types. Furthermore, the ML lifecycle management ecosystem is becoming convergent, with companies providing end-to-end ML lifecycle capabilities through partner integrations or in-house.
MLOps can support rapid innovation through effective ML lifecycle management and boost productivity, reliability, and speed while lowering risk – making it an approach worth paying attention to.
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Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.
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