Companies are running full-steam-ahead in the quest for extracting value from data. Many now have fully capable in-house data science (DS) teams; some companies are built around them entirely. The successful ones may have adopted AI/ML use cases, and are seeing the benefits. The most successful though, have not just adopted AI with proof of concepts; instead, they have automated AI to productionize it rapidly.
A recent report  found that companies automating AI (full adopters) have 7pp higher profit margins than those who have built only proof of concepts (partial adopters). So if there’s so much value in automating AI, why isn’t everyone doing it?
Exhibit 1: Profit margin against respective industry average, for Full AI Adopters and Partial AI Adopters.
About 1 in 10 corporate AI initiatives actually make it into production. Even with the experimental and uncertain nature of AI applications, this is low and inefficient. This is a consequence of two natural occurrences, that have led to a host of issues:
Projects fall flat, confidence dries up and budgets dwindle when not approached correctly. With up to 80% of ML project overhead being spent on manual tasks – that could be automated – it’s easy to see why.
When businesses think about machine learning, they think typically about building a model with a data science team. In reality, building a model might get you 5% of the way to production. A successful machine learning project requires capability across a much wider spectrum. For companies without the end-to-end capability required to deliver these tasks, this is a major roadblock to progress.
Exhibit 2: There is more than just a model build required when productionizing ML.
MLOps focuses on taking ML models into production by the most efficient means possible. MLOps has formed naturally over the past 3-5 years, in response to the challenges posed by enterprise ML, and is still evolving at speed.
Without MLOps, these tasks present a host of issues, driven by the factors mentioned earlier of 1.) AI’s novel usage in business applications, and 2.) the resourcing gap within automation & engineering of AI.
Issues presented come from:
With MLOps, the process is standardized, steps are automated, goals are aligned and tooling meets the business requirements. An ML Ops solution provides:
The application of MLOps to Automate AI is different from business to business. The certainty though, is that there’s no better time to start than now. Every model that a company puts into production outside of a standard process, is another model that must be rewired back through the standard process later on.
At Datatonic, we have world-class ML Engineers dedicated to automating the ML process. Our ML Engineers can productionize your AI initiatives, using an MLOps approach, and have done this for many leading companies. Check out our latest case studies with RealEyes and Lush to find out more.
For more technical information on optimizing your AI workflows using leading technologies from Google Cloud and Intel, access our 2-Page Brochure below.
 McKinsey, Artificial Intelligence: The Next Digital Frontier?, June 2017.
 Indeed, The Best Jobs in the US, 2019.
 Dice, Tech Job Report, 2020.
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