Deloitte AI Institute
Take a new view on MLOps
And AI spreads to every corner of your company
Leveraging AI/ML Capabilities to create business value
Artificial Intelligence (AI) is creating amazing results across many industries. It can predict pricing. It can prevent maintenance failures. It can help doctors find early disease. It can detect supply chain issues. It can automate customer service round the clock. Every department in every company wants some aspect of AI to drive business value, just take a look at this dossier, which highlights dozens of compelling, business-ready use cases for AI.
However, deploying AI solutions in production can be challenging. If business stakeholders and technologists fail to collaborate effectively, resulting investments in AI can fail to address the business need. Too often, data science driven teams' focus can lie more on designing and deploying highly accurate AI/ML models than working with business and product teams to reach the heart of the challenge. By redefining the framing of MLOps, organizations can better meet the needs of the business and drive value.
What exactly is “MLOps?”
MLOps is the process of operationalizing machine learning models, using automation across different domains.
Born out of agile and DevOps principles, MLOps aims to accelerate software infrastructure development and delivery. By nature, DevOps is iterative with quick releases that trump the long development processes of yesteryear. MLOps is based on the same premise, delivering the benefits of AI to the organization in a faster way. The workflow traditionally focuses on the build, deploy, and monitor pieces of the process or the technical details, overshadowing the importance of connecting data scientists with the broader ecosystem of business objectives, people and processes.
Don't forget the business value
MLOps provides data science teams a structured way to rapidly develop, deploy, monitor, and maintain AI/ML solutions. Well-functioning MLOps processes arm data scientists with the tools to continually monitor AI models and adapt to changing conditions in their data.
However, too often the engineering focus of the MLOps process makes AI model development, deployment, monitoring and maintenance seem like "just another technology." Organizations that take this approach and rely on data science teams to make inference about how AI should adapt to shifting conditions in the organization are often disappointed in the results. There can be long cycle times in AI model updates and misaligned integrations into business processes and products. Sometimes significant AI ethics and business risks are introduced as a result.
Not only can the model itself drift, but people can seem to drift away from the intended business value at hand.
There's a disconnect
Don't fall in the 'brains in a jar' trap
For too many companies, there's a disconnect between creating models and delivering business value. AI Strategy, and the process to deliver AI, should prioritize alignment to an organization's business strategy over perfecting models. Models that fail to meet the needs of business processes or products are akin to creating “brains in a jar”—sitting on a shelf. Those brains can be expensive and yet almost worthless to an organization at the same time. Why? Because while the models may work perfectly with pilot use cases, they often fail to create business value down the road. Business and data science leaders should be in tune to the challenge.
In fact, according to the recent Deloitte's State of AI in the Enterprise, 5th Edition, which surveyed 2,620 IT and business executives, 60% of respondents viewed AI solutions as strategically “very important” for their organizations’ success.
Companies should consider a new view on MLOps
MLOps can be the right framework for industrializing AI, if business value is at its core and responsible, Trustworthy AI is the guardrail. Business value should be tracked and assessed within an agreed upon boundary at every stage of MLOps. Our new view on MLOps incorporates an envision phase, which defines guiding principles for the entire process. The added step helps ensure that business value is tracked and assessed within an agreed upon boundary at every stage of MLOps.
While AI can deliver exponential benefits to companies that successfully leverage its power, if implemented without ethical safeguards it can also damage a company's reputation and future performance.
AI projects start with decision-makers, end-users, and domain and data science teams. The decision-maker provides clear objectives and defines project success criteria. End users and domain experts provide organizational and use-case context. Data Science teams shape the AI solution hypothesis and how it can be designed to suit the business needs within the confines of responsible AI safeguards.
Companies that approach AI development with a cross-functional mindset can be well positioned for success. It takes solid planning upfront and continuous ownership from thereon.
Putting MLOps in motion
There are four key phases to MLOps:
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Envision
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Build
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Deploy
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Monitor
Carrying the planning from the Envision phase throughout the MLOps Lifecycle requires AI Model Product Managers that sit between the business and the engineers/scientists. As the teams work their way through the MLOps activities, they evaluate model development, deployment, and monitoring trade offs against agreed upon business metrics and target goals.
So how does MLOps get put in motion to create value? It addresses business value assessments and Trustworthy AI tactics in every phase. Key MLOps processes overlap the foundation—business value—at every turn.
The four key phases
Every step addresses business value, keeping guardrails in check. Explore each step below:
Prevent wasted model deployment efforts
MLOps is a process, in classic Lean Six Sigma parlance. It is not dependent on a few experts, niche use, bespoke designs, or custom development. It's a process that integrates humans at every step. By following a value-driven, team-driven strategy and interweaving Trustworthy AI you can help ensure that your data science teams are realizing the promised return on investment.
Change your view and drive business value throughout the MLOps process.
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