With the continuous evolution of Machine Learning technologies, mainstreaming of ML-based solutions and production rollouts at scale is gaining momentum rapidly. However, a Gartner study reveals that only 47% of enterprise AI/ML models go into production. Part of the reason lies in underestimating the fundamental realities of machine learning. Some of which include:
Another reason lies in the lack of a streamlined process to execute ML projects. ML initiatives are a coordinated effort of multiple functions, and code is only one of them.
Research shows that most data science teams spend most of their time doing data wrangling, data preparation, software package & framework management, infrastructure configuration, and component integration – most of which can be generalized as supporting tasks. Even ML development per se has been chiefly manual thus far, driven by the Data Science and Data Engineering community.
Overall, these factors determine the pace at which ML projects move from pilot to production stage. To bring a rigor of repeatability and reliability to ML-based solutions, enterprises need a mature MLOps ecosystem. This includes the right tools, processes, and appropriately skilled talent.
According to Boston Consulting Group (BCG), pioneers of AI at scale, companies that have scaled AI across the business have achieved meaningful value from their investments. These companies typically dedicate 10% of their AI investment to algorithms, 20% to technologies, and up to 70% to embedding AI into business processes and new ways of working. In other words, these organizations invest twice as much in people and processes as they do in technologies.
Organizations striving to move beyond experimentation and embed ML into their business processes will find MLOps to be a game-changer. MLOps is the emerging software engineering discipline that helps reduce the development cycle and accelerate the deployment velocity of ML-based solutions at scale. It operates at the intersection of DevOps, Data Engineering, and Machine Learning, as described in the illustration below.
CDOs and CIOs planning to invest in MLOps will need to focus on People, Processes, and Tools/Accelerators. The first step can be to assess the MLOps maturity status.
The MLOps toolchain needs to provide visibility, managed access control, and collaboration features for the teams involved. There are three main categories of MLOps platforms available as of date.
The MLOps market is poised for a decent level of consolidation soon.
Our internal (secondary) research to enquire organizational reality and their application of MLOps revealed the following insights:
The US Banking sector is an early mover (9 out of the top 10 US banks) with designated roles assigned to establish and execute MLOps. The US Automotive industry stands a close second with 7 out of the top 10 grappling with MLOps. Pharma, P&C Insurance Players, and Healthcare Payers are lagging in this area – even though ML seems to be a technology they’re actively investing in, for business innovation. MLOps is relevant and necessary for businesses wanting to leap with AI/ML. Also, industry data shows that the market for MLOps solutions is expected to reach $4 billion by 2025, according to Forbes.
Virtusa offers an MLOps platform that can help accelerate ML development and deployments at scale. The platform serves as a single place for model development, lifecycle management, and monitoring. Model deployments to higher environments, including production, is simplified. The platform offers excellent monitoring and governance capabilities, enabling constant evaluation and continuous learning capabilities to avoid surprise changes in model performance in the future.
To build a complete MLOps model evaluating the best tools, frameworks, and methodologies becomes a critical part of any organization. Understanding what the requirements are for the successful implementation of MLOps, as explained above, will help generate optimal results. While the MLOps platform helps improve the ML maturity curve of the organization, the accelerated adoption of MLOps enhances the success of AI/ML programs implementation by 50-60%. The downstream impact in terms of monetization opportunities is humongous. Virtusa's MLOps platform helps businesses create efficient workflows and improve customer experience by leveraging data analytics to accelerate decision-making.
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