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There is a reason the enterprise doesnt build their own cloud computing infrastructure.Last decade, IT infrastructure teams sought to build their own private clouds because they thought they could do it cheaper and better suited to their business versus public cloud. Instead, they ended up taking longer and costing more than expected to build, requiring more resources to maintain, and having less of the latest capabilities in security and scaling than what was provided by the public clouds. Instead of investing in core business capabilities, these enterprises ended up investing significant time and headcount to infrastructure that couldnt match expanded business needs.

Many enterprises are now repeating that same do-it-yourself approach to most things MLOps by creating custom solutions cobbled together from various open source tools like Apache Spark.

These often result in model deployments taking weeks or even months per model, inefficient runtimes (as measured by inferences run over compute and time required), and especially lack the observability needed to test and monitor the ongoing accuracy of models over time. These approaches are too bespoke to provide scalable, repeatable processes to multiple use cases in different parts of the enterprise.

The case of the misdiagnosed problem

In addition, conversations with line of business leaders and chief data and analytics officers have taught us that organizations keep hiring more data scientists but arent seeing the return. As we delved deeper, however, and started asking questions to identify the blockers to their AI, they quickly realized their bottleneck was actually at the last mile deploying the models to use against live data, running them efficiently so the compute costs didnt outweigh the gains, and then measuring their performance.

Data scientists excel at turning data into models that help solve business problems and make business decisions. But the expertise and skills required to build great models arent the same skills needed to push those models in the real world with production-ready code, and then monitor and update on an ongoing basis.

This is where ML engineers come in. ML engineers are responsible for integrating tools and frameworks together to ensure the data, data pipelines, and key infrastructure are working cohesively to productionize ML models at scale (see our more in-depth breakdown comparing the roles of data scientists versus ML engineers available here).

So now what? Hire more ML engineers?

But even with the best ML engineers, enterprises face two major problems to scaling AI:

How to get the most value from AI

Enterprises have poured billions of dollars into AI based on promises around increased automation, personalizing the customer experience at scale, or delivering more accurate and granular predictions. But so far there has been a massive gap between AI promises and outcomes, with only about 10% of AI investments yielding significant ROI.

In the end, to solve the MLOps problem, Chief Data & Analytics officers need to build the capabilities around data science that are core to the business, but invest in technologies that automate the rest of MLOps. Yes, this is the common build vs. buy dilemma, but this time the right way to measure isnt solely OpEx costs, but in how quickly and effectively your AI investments are permeating throughout the enterprise, whether generating new revenues through better products and customer segments or cutting costs through greater automation and decreased waste.

About the Author

Aaron Friedman is VP of Operations atWallaroo.ai. He has a dynamic background in scaling companies and divisions, including IT Outsourcing at Verizon, Head of Operations forLowes.comand JetBlue, Head of Global Business Development at Qubole, and growing and selling two systemintegrationcompanies.

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MLOps | Is the Enterprise Repeating the Same DIY Mistakes? - insideBIGDATA