FEATURE

Time, technology, talent: The three-pronged promise of cloud MLCloud ML appears to be the future of AI, and the future seems bright

Sudi Bhattacharya, Ashwin Patil, Jonathan Holdowsky, and Diana Kearns-Manolatos

THE DELOITTE CENTER FOR INTEGRATED RESEARCH

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AS ORGANIZATIONS LOOK to use machine learning (ML) to enhance their business strategies and operations, the retailer

Wayfair looked to the cloud. Wayfair believes what so many other organizations seem to—that the cloud and the fully integrated suite of services it offers may give organizations the best approach to create ML solutions with time to market, existing resources, and available technologies. For its part, Wayfair pursued the cloud for its scalability—key for retailers with varying demand levels—as well as a full suite of integrated analytics and productivity tools service offerings to drive actionable insights. The results? Faster insights into changing market conditions to save time. Immediate access to the most current ML technologies. And tools of productivity that can drive efficient talent.1

Indeed, ML is transforming nearly every industry, helping companies drive efficiencies, enable rapid innovation, and meet customer needs at an unprecedented pace and scale. As companies increasingly mature in their use of ML, the heavy reliance on large data sets and the need for fast, reliable processing power are expected to drive the future of ML into the cloud.

The well-known benefits of the cloud—including modular, elastic, rapidly deployable, and scalable infrastructure without heavy upfront investment—apply when bringing ML into the cloud. Cloud ML additionally introduces cutting-edge technologies,

services, and platforms—including pretrained models and accelerators—that provide more options for how data science and engineering teams can collaborate to bring models from the lab to the enterprise. Indeed, because of all these advantages, time, technology, and talent—which might otherwise serve as challenges to scaling enterprise technology—comprise the three-pronged promise of cloud ML (see sidebar, “The measurable value of cloud ML”).

The recognition of this potential has fueled rapid growth: The current cloud ML market is estimated to be worth between US$2 billion and US$5 billion, with the potential to reach US$13 billion by 2025. What such growth says is that adopters are increasingly realizing the benefits of cloud ML and that it is the future of artificial intelligence (AI). And with just 5% of the potential cloud ML market being penetrated according to one estimate, the community of adopters should only deepen across sectors and applications.2

To explore the potential cloud ML represents for organizations looking to innovate, we conducted interviews with nine leaders in cloud and AI whose perspectives largely inform the content that follows. In this paper, we provide an overview of three distinct operating models for cloud ML and offer insights for executives as they seek to unlock the full potential of the technology.

Start with the business case, assess your data and model requirements, invest in the right talent, and be willing to fail fast to reap the maximum benefits from cloud machine learning.

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THE MEASURABLE VALUE OF CLOUD MLIn the 2020 Deloitte State of AI in the Enterprise survey, 83% of respondents said that AI will be critically or very important to their organizations’ success within the next two years. Around two-thirds of respondents said they currently use ML as part of their AI initiatives. And our survey suggests that a majority of these AI/ML programs currently use or plan to use cloud infrastructure in some form. Our additional analysis of the survey data for this research showed that cloud ML, specifically, drives measurable benefits for the AI program (figure 1) and generally improves outcomes as compared to nonspecific AI deployments:

• Forty-nine percent cloud ML users said they saw “highly” improved process efficiencies (vs. 42% overall survey results)

• Forty-five percent saw “highly” improved decision-making (vs. 39% overall)

• Thirty-nine percent experienced “significant” competitive advantage (vs. 26% overall)

Importantly, these data points showed that cloud ML tools appear to make a notable impact on longer-term, strategic areas, such as competitive advantage and improved decision-making, when compared to general AI adoption. This is an important distinction and shift in the approach organizations are taking to run their AI programs (away from on-premise AI platforms), and a trend that is expected to continue as cloud ML takes greater focus for global organizations.

The addition of low-code/no-code AI tools, which are part of some cloud ML service offerings, can extend these advantages with responses showing additional gains in decision-making, process efficiencies, and competitive advantage and other areas. For instance:

• Fifty-six percent of low-code/no-code cloud ML users said they saw “highly” improved process efficiencies (vs. 49% in cloud ML users vs. 42% overall)

• Fifty-four percent saw “highly” improved decision-making (vs. 45% in cloud ML users vs. 39% overall)

• Forty-nine percent experienced “significant” competitive advantage (vs. 39% in cloud ML users vs. 26% overall)

• Low-code/no-code cloud ML users saw additional gains in areas that general cloud ML users did not see with outcomes such as new insights and improved employee productivity, among others.

The emergence of low-code/no-code tools is part of a broader trend toward greater accessibility to cloud AI/ML solutions and a more diverse user community. These positive survey results suggest that this trend may only strengthen in the near to medium term.

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33%32%

30%29%

31%31%

30%26%

39%42%

45%54%

42%46%

49%56%

36%37%

38%41%

36%38%

40%49%

36%38%

40%54%

37%37%37%

38%

35%36%

38%40%

34%33%

32%40%

26%34%

39%49%

FIGURE 1

Cloud ML can drive improved outcomes in key areas of decision-making, process efficiencies, and competitive advantage

Total survey respondents (N=2737) Cloud-based AI as a service (N=776) Cloud-based ML (N=432)

Cloud-based ML with low-code/no-code (N=104)

(Percentage who achieved outcome to a “high” degree)

Note: As a prerequisite to participate, all 2,737 respondents to the survey must have adopted AI technologies in at least some fashion, based on any of several criteria. See survey methodology for more detailed description of criteria.Sources: Beena Ammanath, David Jarvis, and Susanne Hupfer, Thriving in the era of pervasive AI: Deloitte’s State of AI in the Enterprise, 3rd Edition, Deloitte Insights, July 14, 2020; Deloitte analysis.

Deloitte Insights | deloitte.com/insights

Lowering cost

Reducing headcount

Improving decision-making

Making processes more efficient

Enhancing relationships with clients/ customers

Making employees more productive

Discovering new insights

Enhancing existing products and services

Creating new products and services

Enabling new business models

Enabling a “significant” lead over competitors

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Cloud ML: Adopting the right approach

“AI is a tool to help solve a problem,” says Rajen Sheth, vice president, AI, Google Cloud. “[In the past,] people were doing a lot of cool things with AI that didn’t solve an urgent problem. As a result, their projects stopped at the concept stage. We now start with a business problem and then figure out how AI solves it to create real value.”

As organizations look to advance their ML programs supported by the cloud, there are three basic approaches to cloud ML (figure 2) they can take—cloud AI platforms (model management in the cloud); cloud ML services (including pretrained models); and AutoML (off-the-shelf models trained with proprietary data).

Selecting the right approach starts with understanding the business context—what problem

the organization is trying to solve, the technology context (to what degree the organization has data and models on-premises or in the cloud already), and the talent context (to what extent the organization has the right people resources in place to build and train models).

There is no one-size-fits-all approach. Depending on the business problem, multiple approaches may be employed at the same company. As Gordon Heinrich, senior solutions architect at Amazon Web Services, says, “At the end of the day, it comes down to where you can put ML and AI into a business process that yields a better result than you are currently doing.” Building a model in-house requires significant investment of time in data collection, feature engineering, and model development. Depending on the specific use case and its strategic importance, companies can employ cloud ML services, including pretrained models, to help speed up time to innovation and deliver results.

FIGURE 2

Cloud ML: Three core approaches and their benefitsCloud AI platforms: Model management in the cloud

Cloud ML services: Pretrained models and more

AutoML: Customized pretrained models

What it is Brings new and existing ML models into the cloud

Allows developers and data scientists to use cloud-enabled tools for training, deployment, and model management

Allows organizations to access pretrained models, frameworks, and general-purpose algorithms including: • Vision, speech, language, and

video APIs • Virtual assistant and bot

frameworks • General-purpose algorithms

for fraud detection, inventory management, call centers/conversational AI, etc.

Allows for “customizing” off-the-shelf versions of pretrained API models with proprietary data

Allows for ongoing model refinement, which, in some cases, may even include a low-code or no-code environment

Benefits Helps manage cost of spot training, gain greater elasticity to scale infrastructure needs, and improve orchestration across the model management life cycle

Accelerates development of new business applications without the need for a large data science team to build the underlying model and speeds time to innovation3

Serves as a way to drive efficiency in the life cycle of model deployment for organizations with talent constraints

Provides access to ML for developers to get started quickly and offers flexibility to shift to other operating models as needs change

Source: Deloitte analysis

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Conversational AI: This is one use case that came up repeatedly during our research where cloud ML and pretrained models seem particularly well-positioned to help solve business, technology, and talent challenges. Conversational AI is frequently applied to help improve call center operations and enhance customer service. In fact, one estimate suggests that by 2025, the speech-to-text technology will account for 40% of all inbound voice communications to call centers.4

Organizations can use conversational AI to support customer service improvements in many ways—from helping answer customer questions directly via chatbots to supporting contact center staff in the background with next-best answer technology. These capabilities bring great value to customer service today. However, it takes a tremendous amount of work, time, and resources to continuously enhance conversational AI models, and it is not differentiating enough for most companies to build these models in house. So, most companies are employing large cloud vendors’

pretrained models instead of building their own conversational AI models.

Broadly speaking, there is no single approach to a cloud ML initiative. But any approach should contemplate a complex array of potentially deep and vast issues that are strategic, technical, and cultural in character and range the full arc of the project lifespan.

For general cloud ML use cases, companies should think through comparable aspects of business, technology, and talent to determine the right approach, while keeping in mind the following high-level recommendations:

• Business context: Define the problem statement.

– The most impactful applications for cloud ML typically start with identifying the broader business problem AI is suited to help solve—whether it be scaling the digital business, enhancing customer experience, or addressing risk management challenges. Executives should then examine the business problem from both an industrywide and company-specific perspective.

• Technology context: Understand which data/models you have and/or need and where they reside.

– Consider how long it will take to get access to the data and to develop and test the models as it relates to time, resources, risks, and deployment needs. Pretrained models or AutoML solutions may offer time and resource savings for initiatives that are less strategically differentiating for your business.

“[In the past,] people were doing a lot of cool things with AI that didn’t solve an urgent problem. As a result, their projects stopped at the concept stage. We now start with a business problem and then figure out how AI solves it to create real value,” says Rajen Sheth, vice president, artificial intelligence, Google Cloud.

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– Consider program goals and outcomes. Starting small allows teams adequate time to test and refine the model and also to operationalize and scale it over time. Think about easy-to-achieve targets, mid- to longer-term business outcomes, and value.

• Talent context: Assess data, ML, cloud, and data science expertise across your team.

– Review what talent you have to advance your ML initiatives and what talent you need to achieve your stated goals.

– Assess areas where cloud ML services could save training time, optimize the potential of your existing talent, or fill in resourcing gaps.

Although not comprehensive by any means, thoughtful evaluation of these three representative areas should point teams in the right direction as they assess which applications are most appropriate for cloud ML. It can also help ensure valuable talent hours are utilized strategically toward the company’s most differentiating features and initiatives.

Use cases: Cloud ML adoption across industries

The broad applicability of cloud ML business archetypes has driven rapid adoption across industries. Analysts, however, have indicated a standout opportunity for cloud ML to solve business challenges at scale for industries with significant call center footprints as well as supply chain challenges.5

As Eduardo Kassner, chief technology and innovation officer, One Commercial Partner Group, Microsoft, says, “Certainly, there is a big evolution in cloud ML services and APIs across a number of industries. We are seeing increased activity in manufacturing where you bring together IoT, big data, and advanced analytics.”

In figure 3, we have compiled examples from six different industries where cloud ML has helped solve a range of tangible and definable business challenges. These companies have drawn on distinct cloud ML technology services such as speech and vision APIs and pretrained models for recommendation, fraud, and inventory management to advance their AI programs and achieve tangible business outcomes.

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FIGURE 3

How different industries are reportedly benefiting from cloud ML adoptionIndustry Organization Business use case Category

Financial services UBSa UBS created a digital app to allow financial advisors to have personalized touchpoints with clients. It used an AI-driven personalization engine to customize the client experience around personal financial goals, milestones, and achievements. They believe this resulted in a significant uptick in high net worth and ultra-high net worth clients.

Recommendations

Financial services Capital Oneb Capital One analyzed large volumes of customer data to detect anomalous activity that could be indicative of fraud; alert customers when suspicious activity occurs; manage fraud reporting; and expedite new card deployment. They reported enhanced customer experience and minimized risks.

Fraud detection

Life sciences and health care

Takedac Takeda used clinical evidence to predict, diagnose, and treat nonalcoholic steatohepatitis (NASH) and treatment resistant depression (TRD). It used cloud ML to quickly and efficiently analyze a large data set with greater accuracy and rapidly tested predictive models to achieve nearly a 40% increase in accuracy, improving predictive accuracy from 53.4% to 92%.

Cloud-enabled AI/ML toolkit

Consumer (automotive, retail, transportation)

Wayfaird Wayfair embraced cloud infrastructure to scale its retail operations, enhance customer experience, and better manage a large network of employees and suppliers. The shift began with a hybrid cloud solution to handle burst capacity needs for an annual sale across thousands of customers and millions of transactions. Wayfair then used additional data, AI, and ML services to support better and faster insights into consumer trends, patterns, and preferences to to drive real-time decisions related to merchandise forecasting, personalized customer experiences and recommendations, and targeted marketing promotions across 19 million active customers, 16,000 employees, and 11,000 suppliers.

Customer experience; inventory management

Technology, media, and telecommunications

Hulue Hulu reimagined its customer service experience across 25 million active subscribers. The cloud ML contact center and virtual agent helped engage users, resolve issues, escalate issues to a human agent, and recommend the next-best action for cross-sell/up-sell opportunities. The company reported a 50-second improvement on average handle time and improved customer service experience.

Speech API, virtual agent/bot framework, conversational AI

Government and public services

US Navyf The US Navy modernized its vessel and facility repairs function by using vision APIs to analyze drone-captured images of vessels and facilities. Using AutoML, it built a model that detected corrosion with a high degree of accuracy.

Vision API,AutoML

Energy, resources, and industrials/consumer (automotive, retail, transportation)

Convoyg Convoy used cloud ML to analyze millions of shipping jobs versus trucker availability to recommend matches via a digital marketplace for carriers and drivers. This helped the company manage work better and create greater process efficiency across the entire freight network. The company reported improved demand forecasting and reduced carbon emissions.

Recommendations, inventory management

Sources:

a. Tamara Cibenko, Amelia Dunlop, and Nelson Kunkel. Human experience platforms: Affective computing changes the rules of engagement, Deloitte Insights, January 15, 2020.

b. AWS Machine Learning, “At Capital One, enhancing fraud protection with machine learning,” accessed November 26, 2020.

c. Deloitte, “Cloud-based machine learning in health care creates patient-centered treatments,” accessed November 26, 2020.

d. PRNewswire, “Wayfair chooses Google cloud to help scale its growing business, while creating engaging customer (and seller) experiences,” MARTECHSERIES, January 13, 2020.

e. YouTube, “How AI is transforming the enterprise (Cloud Next ’19),” April 10, 2019.

f. Stephanie Condon, “Federal contractor taps Google Cloud AI and ML to inspect US Navy vessels,” ZDNet, August 27, 2020.

g. AWS Machine Learning, “Convoy is revolutionizing trucking through machine learning.”

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Talent approach: Cloud ML is a team sport

“When talking about talent in the AI/ML space, think of a staircase with four steps,” suggests Kassner.

“The first step is the developers who leverage cognitive services APIs in their applications. The next step is developers who know how to call APIs for process automation, customer support, object detection, among other scenarios of cognitive services. The third level in the talent staircase is developers who understand big data, data cataloging, data warehousing, data wrangling, and data lakes. The fourth step is the one where specific algorithms and customer models are to be created with ML and AI Ops, which may require data scientists or data analysts.”

To achieve the business objectives of any cloud ML initiative, data science professionals typically work with a well-balanced talent team—cloud engineers, data engineers, and ML engineers. But PhD-level data science talent is scarce.6 As a result—and as our interviewees repeatedly confirmed—many companies are training a growing number of developers and engineers to contribute alongside them as non-PhD data scientists.

Some speculate that pretrained model and AutoML capabilities could reduce the need for scarce

PhD-level data science talent and thereby help “democratize” ML. However, we recommend viewing these capabilities as a way to extend the reach of deep data science expertise across a broader team made up of new types of specialists. With cloud ML, pretrained models and AutoML tools have “hidden” the complexity and blurred the lines between previously distinct roles. This often empowers ML engineers to fill the data science PhD talent gap and affords adopters greater flexibility in how they staff teams. And, as these tools get more nuanced and powerful, such flexibility should only grow. Ultimately, however, as an organization’s modeling needs evolve and become more complex, a PhD-trained data scientist may become indispensable.

MLOps and governance: Maintaining rigorous quality and efficiency The term MLOps, or “machine learning operations,” refers to the steps that an organization takes as it develops, tests, deploys, and monitors cloud ML models over their life cycle. In effect, these steps come to represent a set of governance practices that ensure integrity throughout the life cycle. As development operations (DevOps) become increasingly automated due to intensifying pressure for frequent model releases, these steps take on greater importance and urgency—especially as companies become more cloud-focused in their infrastructure.

Commenting on the importance of this trend, Heinrich notes, “[MLOps] is about explainability and getting a notification when a model is starting to drift. It is about making the data pipeline easier to use with data tagging and humans in the loop to achieve high-quality results. It really helps businesses feel more confident about putting algorithms into production.”

“When talking about talent in the AI/ML space, think of a staircase with four steps,” says Eduardo Kassner, chief technology and innovation officer, One Commercial Partner Group, Microsoft

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While there is no single accepted global standard for MLOps process and best practices, a comprehensive end-to-end MLOps program should typically include four basic elements: versioning the model, autoscaling, continuous model monitoring and training, and retraining and redeployment (figure 4).

Model bias: A related concept within a broader ML governance program is model bias. In the past few years, we have seen instances of bias creeping into AI/ML models. In fact, a recent experiment revealed that an AI vision model associated various pejorative stereotypes with gender, race, and other characteristics.7 ImageNet is one of a number of projects underway to identify and mitigate the role that bias plays in AI.8

According to Sheth, as cloud ML moves toward providing decision support or recommendations, the issue of bias becomes even more acute because multiple human judgment factors are manufactured into the model. “There are tools that

“[MLOps] is about explainability and getting a notification when a model is starting to drift. It is about making the data pipeline easier to use with data tagging and humans in the loop to achieve high-quality results. It really helps businesses feel more confident about putting algorithms into production,” says Gordon Heinrich, senior solutions architect at Amazon Web Services.

Source: Deloitte analysis.Deloitte Insights | deloitte.com/insights

FIGURE 4

Four basic MLOps elements

It is important to develop effective ways to measure model performance continuously to ensure it produces quality results. This requires consistent evaluation of the model output and monitoring of drift and degradation over time. This is because external factors (e.g., macroeconomic conditions) keep changing, rendering the data that formed the initial training process obsolete.

CONTINUOUSMODEL MONITORINGAND TRAINING

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Timely detection of drift triggers the need for retraining and redeployment of the model. As model drift sets in, it becomes necessary to retrain the model on new data and then to redeploy it.

RETRAINING ANDREDEPLOYMENT4

Developing a cloud ML model requires experimenting with different data sets and algorithms to solve the same business problem. These data sets are ingested through a cloud pipeline. In this sense, reproducibility is key. Versioning each data set, algorithm, and pipeline is critical to ensure that reproducibility.

VERSIONINGTHE MODEL 1

Once deployed, a model—as well as the supporting cloud infrastructure—should be able to scale up or scale down quickly as capacity demand fluctuates. Autoscaling is a feature that allows for an immediate response to changing demand conditions, which may ultimately save adopters money to the extent that they only pay for the capacity that they use.

AUTOSCALING2

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help explain why the model gave a particular outcome and where there are biases and where there aren’t and what impact such biases will have on people in general. Organizations will have to dig deep to understand what is happening and supplement the data accordingly. It’s a painstaking process,” he says.

One cloud ML area where bias manifests frequently is pretrained models. When developing and training models in-house, the development team can implement safeguards and testing protocols to lessen the effect of bias since control of the process and training data rest with them. In contrast, they are less likely to be aware of bias when using pretrained models, as they have typically limited visibility into how a model was trained and thus how much bias, if any, was present prior to model deployment. Under such circumstances, testing for bias after model deployment requires heightened scrutiny using a variety of known data sets so that the results can be detected based on their output. (For more information on AI ethics and avoiding bias, see Deloitte’s Trustworthy AITM framework.)

Five key considerations to set up cloud ML initiatives

Cloud ML can be transformational, and there is strong evidence that organizations are already reaping its benefits in wide-ranging initiatives across industries, use cases, and archetypes. Based on our research and interviews with industry leaders, here are some important points for organizations to keep in mind while starting out on their cloud ML journey:

• Strategic alignment. Identify a business problem ripe for transformation that has the

highest potential to benefit from AI augmentation. The wrong process may lead to frustration and wasted resources. Gaining both awareness and enthusiastic buy-in from the highest level of leadership into these initiatives can help identify meaningful problems to solve and maximize success.

• Orchestrated talent. Remember that cloud ML is a team sport. It is not just about modeling. Investing in the right balance of data science and ML, cloud, and data engineering talent brings cloud ML to life.

• Cost savings and elasticity of cloud. Take advantage of the opportunity cloud provides to bring infrastructure cost from capex to opex. Given the extraordinary stress on infrastructure, cloud provides the elasticity to use infrastructure on demand.

• Data requirements. Know the quality and quantity of the training data you have and also what data you need and how you are going to get it.

• Importance of trial and error. Embrace the notion of “failing fast.” AI needs experimentation and innovation. It is not a tried-and-tested recipe.

By putting some forethought into these key recommendations and gaining a thorough understanding of what they want to achieve, organizations can optimally employ cloud ML. They can thus reap the three-pronged promise of cloud ML—extending the reach of critical ML talent and technology investments and cutting time to innovation.

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The authors wish to thank Rajen Sheth (Google Cloud), Eduardo Kassner (Microsoft, One Commercial Partner Group) and Gordon Heinrich (AWS), as well as Deloitte professionals David Kuder, James Ray, Jason Chiu, David Linthicum, and Scott Rodgers, for the time they spent in sharing their invaluable insights. The authors would also like to thank Lisa Beauchamp, Amy Gonzalez, Claire Melvin, and Saurabh Rijhwani for their marketing support. Negina Rood provided vital research support and Jay Parekh, critical analytical insights. Finally, this paper would not have been possible without the overarching and tireless support of Siri Anderson.

Acknowledgments

1. Aithority, “Wayfair chooses Google Cloud to help scale its growing business, while creating engaging consumer experiences,” January 13, 2020; Angus Loten, “Pandemic has online sellers leaning on cloud,” Wall Street Journal, August 24, 2020.

2. Ed Anderson and David Smith, Hype cycle for cloud computing, 2020, Gartner, August 1, 2020.

3. MIT Sloan Management Review, “How AI changes the rules: New imperatives for the intelligent organization,” February 10, 2020.

4. Anthony Mullen, Bern Elliot, and Adrian Lee, Market guide for speech to text solutions, Gartner, April 22, 2020.

5. ABI Research, Cloud-based AI in a post-COVID-19 world, 2020.

6. Emilia Marius, “How to solve the talent shortage in data science,” InformationWeek, April 17, 2019.

7. Will Knight, “AI is biased. Here’s how scientists are trying to fix it,” Wired, December 19, 2019.

8. ImageNet website.

Endnotes

Time, technology, talent: The three-pronged promise of cloud ML

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Sudi Bhattacharya | sudibhattacharya@deloitte.com

Sudi Bhattacharya is a managing director with Deloitte Consulting LLP’s Cloud Engineering practice. He leads big data– and AI/ML-driven business process transformation projects and high-performance teams in public cloud environment for Fortune 500 businesses in finance, retail, CPG, and foodservice. A seasoned pragmatic thinker, Bhattacharya is adept at striking the right balance between developing a strategic vision and agile execution of that strategy in complex organizations with multiple competing priorities. He builds and leads high-performance teams that execute AI/ML augmented cloud-native application development projects on time and on budget.

Ashwin Patil | ashpatil@deloitte.com

Ashwin Patil leads the Analytics and Information Management practice for the manufacturing sector at Deloitte Consulting LLP. He has more than 18 years of experience helping clients drive economic value in different functions by leveraging analytics and data. He has led large-scale, global implementations of warranty/quality solutions, supply chain risk and optimization solutions, customer segmentation and analytics solutions, sales and operations planning solutions, etc. He drives eminence in capability areas such as advanced analytics, big data, business intelligence, data warehousing, performance management, and enterprise information management.

Jonathan Holdowsky | jholdowsky@deloitte.com

Jonathan Holdowsky is a senior manager with Deloitte Services LP and part of Deloitte’s Center for Integrated Research, managing a wide array of thought leadership initiatives on issues of strategic importance to clients within the consumer and manufacturing sectors. His current research explores the promise of emerging technologies such as additive and advanced manufacturing, Internet of Things, Industry 4.0, cloud, and blockchain. Holdowsky recently served as a coauthor of Deloitte’s 2020 Global Blockchain Survey, among many other publications.

Diana Kearns-Manolatos | dkearnsmanolatos@deloitte.com

Diana Kearns-Manolatos is a senior manager in the Deloitte Center for Integrated Research where she analyzes market shifts and emerging trends across industries. Her research focuses on cloud and the future of workforce. Additionally, Kearns-Manolatos draws on almost 15 years of award-winning marketing communications expertise to align insights with business strategy. She speaks on technology and women in leadership and holds a bachelor’s and master’s degrees from Fordham University.

About the authors

Cloud ML appears to be the future of AI, and the future seems bright

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Contact usOur insights can help you take advantage of change. If you’re looking for fresh ideas to address your challenges, we should talk.

Industry leadership

Sudi BhattacharyaManaging director | Cloud Engineering | Deloitte Consulting LLP+ 1 469 417 3240 | sudibhattacharya@deloitte.com

Sudi Bhattacharya is a managing director with Deloitte Consulting LLP’s Cloud Engineering practice.

Beena AmmanathManaging director | Deloitte Consulting LLPExecutive director of the Deloitte AI Institute+ 1 415 783 4562 | bammanath@deloitte.com

Beena Ammanath is a managing director with Deloitte Consulting LLP and serves as executive director of the Deloitte AI institute.

The Deloitte Center for Integrated Research

Brenna SnidermanExecutive director | The Deloitte Center for Integrated Research | Deloitte Services LP+ 1 215 789 2715 | bsniderman@deloitte.com

Brenna Sniderman leads Deloitte’s Center for Integrated Research. Her research focuses on Industry 4.0, advanced technologies, and the intersection of digital and physical technologies in the supply network, operations, strategy, and the broader organization.

Jonathan Holdowsky Senior manager | The Deloitte Center for Integrated Research | Deloitte Services LP+ 1 617 437 3198 | jholdowsky@deloitte.com

Jonathan Holdowsky is a senior manager with Deloitte Services LP and part of Deloitte’s Center for Integrated Research, leading thought leadership initiatives that explore the promise of emerging and disruptive technologies.

Time, technology, talent: The three-pronged promise of cloud ML

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