Top 3 Insights from the Google Cloud Applied ML Summit Keynote

Did you miss the keynote “Accelerating the deployment of predictable ML in production” at the Google Cloud Applied ML Summit? Don’t worry; we’ve got you covered.

Led by Priyanka Vergadia (Developer Advocate-Google Cloud) and Andrew Moore (VP & GM, Cloud AI & Industry Solutions, Google Cloud), this informative keynote covered:

  • The latest Google AI and ML product announcements
  • New capabilities supporting everyone from data scientists to enterprises
  • How real-world customers have transformed their operations using Google Cloud Vertex AI

Here are the Top 3 Insights:


The keynote kicked off with an overview of the current state of AI and ML:

  • There’s a significant gap in the skills needed for developing ML-powered applications
  • Removing technology dependencies will allow more people to participate in successful deployments
  • Only 10% of organizations have more than half of their software engineers trained with ML skills*
  • On average, only 53% of products actually make it to production*


Next, we learned about four big paradigms that need to be addressed in order to reach “machine learning utopia”:

1)     Freedom of choice. Data scientists must be able to mix and match all kinds of different machine learning components.

2)     Meeting users where they are.  Everyone should be able to participate in an ML-driven company, from those who are just starting to experts who are comfortable building custom models.

3)     Streamlining the relationship between data and AI. Both need to work together as one system, so pushing the tools for data management into AI workbenches is critical.

4)     Managing the machine learning models. Make it easier for data scientists to do their jobs and not expect them to be infrastructure or operations engineers.

Google Cloud is helping address these paradigms through enhanced tools such as Vertex AI to accelerate the deployment and maintenance of machine learning models.


We heard from Smitha Shyam (Director of Engineering, UberAI). Uber makes billions of real-time machine learning decisions on a global scale using Michelangelo, its ML platform. In many use cases, the structured data they gather can be imbalanced, or they may have to input missing data.  In its partnership with Google Cloud, Uber incorporated Vertex AI into the training phase and solved their data set size limitations.

Bryan Goodman (Director of AI and Cloud, Ford) touched on AI leading innovation revolutions in electrified, connected, and automated vehicles. Ford’s partnership with Google Cloud helped them manage enormous amounts of data. Vertex AI accelerated their efforts to scale AI for non-software experts as they engineer and build solutions.



Applied ML Summit

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Applied ML Summit

Get more knowledge and insights from leading professional ML engineers and data scientists on cutting-edge AI tools for developing, deploying, and managing ML models at scale.

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Advancing innovation and healthcare accessibility: Hologic's AI Solution with Slalom

Watch our lightning talk

“Advancing innovation and healthcare accessibility: Hologic AI Solution with Slalom" focuses on an AI solution Slalom developed in partnership with medical technology company Hologic and Google Cloud. Watch this session to learn about their mission to provide accessible health care for women across the globe using AI.

Some of the highlights include:

  • Using AI models to screen for cervical cancer faster and more efficiently
  • Leveraging AI to make new solutions and technologies available globally
  • Making accurate predictions and returning lab results quicker



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You trained a model. Now what?

Watch our in-demand session

Watch our informative on-demand session led by Mark Kobe (Senior Director, Global AI, Slalom) and Jake Evans (Senior Consultant, Global IT, Slalom). Using their findings from hundreds of engagements, they’ll share the most important steps your team needs to take before, during, and after training a model.

Key topics from this session include:

  • The additional requirements needed in training a model—technology, strategy, and adoption
  • The life cycle of a successful ML project—one that delivers measurable business impact
  • The effect of field service on customer experience and cost



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Slalom follows five core principles to help you create a modern AI-powered organization. Paired with Google Cloud technology, Slalom can enable and scale the positive impact of AI on your business.

Learn more about our people-first approach to AI and ML, and when you’re ready, request a workshop with our team.

Mark Kobe speaking at Google Summit

View sessions on demand

Mark Kobe speaking at Google Summit

Get more knowledge and insights from leading professional ML engineers and data scientists on cutting-edge AI tools for developing, deploying, and managing ML models at scale.

* Source: Gartner Survey Analytics: by Van Baker, Benoit Lheureux, “ AI Adoption Spans Software Engineering and Organizational Boundaries” Published 25 November 2021, ]

Get more information on Google Cloud and Slalom.