Three Cloud ML Approaches – Deloitte On Cloud Blog | Deloitte US has been saved
Limited functionality available
Artificial intelligence (AI) and machine learning (ML) are helping global organizations solve numerous business challenges, ranging from operational efficiency and risk management to customer experience and more. And, while ML is powerful in its own right, when combined with cloud technologies, it allows organizations to better innovate at speed and scale.
Deloitte’s 2020 State of AI Study reveals that 83% of organizations expect AI to be critical to their business’s success in the next two years. In fact, our recent analysis of the survey found that cloud drives measurable benefits for AI programs, improving processing efficiency (49% versus 42% overall), decision-making (45% versus 39% overall), and competitive advantage (39% versus 26% overall).
Why is that? The answer goes beyond the well-known data scalability and infrastructure elasticity that provide a resilient “scale up and scale down” option for organizations, though that is part of it. Cloud ML brings to bear additional ML services—pretrained models; vision, speech, and video APIs; frameworks; and general-purpose algorithms (fraud detection, inventory management, and others)—as accelerators for developer and data scientist teams.
As organizations look to explore cloud ML, there are three approaches to consider:
And, by virtue of being in the cloud, organizations can achieve downstream benefits across the model development, training, deployment, management, and monitoring life cycle with an enterprise infrastructure for deployed MLOps, including model versioning, autoscaling, continuous monitoring and training, and retraining and deployment.
Organizations across industries are having tremendous success driving efficiency (time); tapping into the power of pretrained models (technologies); and extending and augmenting their developer, data engineer, and data scientist teams (talent). For example, one financial services organization used cloud ML to power personalized recommendations for high-net-worth and ultra-high-net-worth clients. Another technology and media organization used cloud ML to empower contact centers with virtual agents and conversational AI to engage users, resolve and escalate issues, and guide cross-sell and upsell opportunities. In another example, a government organization used cloud ML to capture and analyze drone data to monitor equipment and inform predictive models.
While the potential of these two technologies coming together creates strategic opportunity, perhaps equally as important is the human impact. Cloud ML minimizes organizations’ dependency on having a large team of data scientist PhDs to advance ML programs and empowers data engineers and developers to create new, innovative products and services.
Given the time, technology, and talent barriers broken down by cloud ML, it is no wonder estimates expect today’s $2–5 billion cloud ML market to reach $13 billion by 2025.
As AI programs look to innovate new solutions, expand training data sets, and scale solutions across the enterprise, key considerations like strategic business alignment, talent orchestration, infrastructure finance models, data requirements, and innovation culture needs should all be factored in to take advantage of the cloud ML opportunity.
For more on this topic, check out our recent research, Innovating R&D with the cloud and Time, technology, talent: The three-pronged promise of cloud ML.
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.
Regional banks play a vital role in the US financial ecosystem. But these days, they’re playing from behind—at least when it comes to their IT infrastructure. For these banks, modernizing onto cloud-based platforms is an important technology solution that can help level the playing field.