ML-Oops to MLOps – Deloitte On Cloud Blog | Deloitte US has been saved
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As artificial intelligence (AI), machine learning (ML), and cloud technologies evolve, they are becoming more ubiquitous and affecting market microstructures. Massive proliferation of data, rapid advancements in data storage and computational availability, and the rise of auto-ML have accelerated AI and ML adoption to inform business decisions.
From smart manufacturing to finance transformation to omnichannel customer experience, today, AI and ML can be widely adopted and operationalized. These technologies can drive stronger outcomes through human and machine collaboration and help an organization realize scale with speed, using data with understanding, making decisions with confidence, and getting outcomes with accountability.1 This is the Age of With™.
Machine learning often conjures images of data scientists designing sophisticated algorithms and writing highly technical code. Yet, those of us who have been at the center of an applied AI practice solving real-world business problems at scale know that seasoned data scientists emphasize that there is a lot more to ML and modeling than the ML code or algorithm.
Success hinges on the combination of data, technique, process, and training. The focus for organizations that want to scale AI and ML should be to implement a set of standards and develop a framework to build production-capable AI and ML building blocks. This is the realm of ML operations (MLOps).
There is a chasm between ML and MLOps that can be tricky to scale, and MLOps can turn into ML-Oops. We’ve seen clients face significant challenges around MLOps due to several factors:
MLOps drives outcomes by focusing on the entire life cycle of design, implementation, and management of ML models.
MLOps aims to achieve the core principles of DevOps: automation (as opposed to siloed custom development); deployment (proliferation, as opposed to one-time use); process (integration, testing, and releasing); and infrastructure considerations.
Then MLOps builds and goes beyond DevOps:
A few illustrative and representative MLOps best practices deployed at our clients
How to make the journey
The path to MLOps and more effective ML development and deployment hinges on selecting the right people, processes, technologies, and operating models with a clear linkage to business issues and outcomes.
The way forward
MLOps is central to industrialized AI3
As AI and ML are adopted enterprisewide, models need to be explainable in their construct; trustworthy in their genesis and underlying data; measurable in their impact; sustainable in their outcomes; scalable in their design; and self-correcting in their behavior.
ML is just like any other powerful tool. When used correctly, it can help build data-driven decisioning processes. On the flip side, incorrect deployment leads to damage to intended business outcomes. One major advantage of ML is its speed of analysis and insights on a huge scale, but if misdirected, models can cause suboptimal and even bad decisions at the same speed and scale. To avoid this, or what we call ML-Oops, we need to embed MLOps into all of our AI and ML efforts at scale during the design phase itself.
For more information on this topic, read our full report.
As the chief cloud strategy officer for Deloitte Consulting LLP, David is responsible for building innovative technologies that help clients operate more efficiently while delivering strategies that enable them to disrupt their markets. David is widely respected as a visionary in cloud computing—he was recently named the number one cloud influencer in a report by Apollo Research. For more than 20 years, he has inspired corporations and start-ups to innovate and use resources more productively. As the author of more than 13 books and 5,000 articles, David’s thought leadership has appeared in InfoWorld, Wall Street Journal, Forbes, NPR, Gigaom, and Lynda.com. Prior to joining Deloitte, David served as senior vice president at Cloud Technology Partners, where he grew the practice into a major force in the cloud computing market. Previously, he led Blue Mountain Labs, helping organizations find value in cloud and other emerging technologies. He is a graduate of George Mason University.