DevOps And MLOps – How to Marry Both?

Introduction

From elevating customer service to improving the productivity of the workforce in industries ranging from retail to healthcare, businesses worldwide are finding new ways for artificial intelligence to solve their biggest pain points and create new possibilities. Today even 65% of customers trust businesses that leverage AI in their key operations. However, what allows AI to deliver these great outcomes? If we look under the hood, what’s inside the engine driving AI?

One of the key enablers driving the AI boom is an old favorite of tech-focused companies – machine learning. ML is back, or maybe it never went away!

ML is being used by businesses to build digital experiences powered by algorithms that mimic the core fundamentals of human thinking. Among other applications, we are seeing a significant escalation of activity in the conversational AI space as enterprises leverage ML to build powerful AI bots to amp up customer engagement. From sectors as complex as healthcare and finance to even consumer-focused apps like movie recommendations, machine learning is being leveraged as the enabling foundation of new digital systems that interact with customers. As always, ML models learn about behavioral dynamics across customer cohorts and populations to generate actionable insights. AI drives that forward to create outcomes that will reflect a positive sentiment from each interaction.

As machine learning gains prominence in the AI age, organizations are rapidly increasing their investments in building more intelligent capabilities driven by ML. However, the influx of ML models with the underlying accelerated development philosophy is also causing an array of challenges. The stability, accuracy, and reliability of ML models thus developed do raise concerns, especially as usage volumes surge and applications scale. In many ways, this is the same pattern of problems that enterprises once faced when they went on overdrive with cloud adoption.

That’s why enterprises turned to DevOps-like approaches for solutions.

The MLOps solution

A few years ago, when accelerated software development driven by escalated cloud adoption caused problems in managing continuous growth, the philosophy of DevOps came into existence. The aim was to create a seamless experience for companies whose software development processes were focused primarily on cloud applications. DevOps brought about a high degree of automation, and seamless synchronization of business objectives with technical capabilities, and enabled the continuous growth of cloud assets with a well-defined roadmap.

The same principles can be adopted for managing the expansive ML universe across companies. It’s rightly named MLOps. In simple terms, MLOps is the practice of deploying and maintaining machine learning models for digital systems in their production environment. Borrowing from the DevOps philosophy, MLOps leverages automation heavily to enable continuous analysis, processing, and deployment of the data architecture at scale. It unifies release or launch activities for all the projects that involve ML-linked operations. From validation of data artifacts to testing of ML models, and checking for compatibility with other features or assets, MLOps helps in scaling experimental ML projects into their best real-life use cases. And it enables those capabilities at scale and in very high volumes.

The criticality of MLOps today

There are a range of activities involved in the ML lifecycle based on a combination of data, models, processes, deployments, and supporting tools. Enterprises need to achieve a balanced synchronization between the different constituent stages of the ML lifecycle and the people involved in building models to ensure that business needs get maximum coverage from the capabilities being developed. MLOps provides this balance as it facilitates the much-needed rigor and orchestration for different stages in an ML lifecycle at the necessary speed and scale.

MLOps allows businesses to evolve beyond the traditional model of focusing on every ML iteration as a project. Enterprises get a productized version of their ML capability that can evolve, adapt, and scale seamlessly in synchronization with their growth needs. The people, processes, and technology needed to facilitate MLOps collaborate frequently without disruptions to ensure that enterprises get what they need from their ML capabilities.

Our Services

Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform