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Machine Learning Week, the leading independent conference for machine learning and deep learning, is back live in Las Vegas once again on June 18 – 22, 2023. Join fellow data scientist and other business professionals to discover and discuss the latest in machine learning and deep learning.
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Get the First Step Right for Each ML Initiative
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James Taylor kicks off Track 1 with this session:
Setting Machine Learning and AI Projects Up for Success
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ML and AI projects are technology-heavy, data-rich and rely on increasingly large teams with deep technical skills and expertise. As more companies make larger investments in ML and AI, the pressure to succeed in these complex projects is increasing. Still, many of these projects fail. They fail not because the technology fails, not because the data is poor or because the team lacks skill but because they were not set up for success. The way the project was conceived, framed and begun destined them for failure. In this session to kick off the business track, you'll learn how to set ML and AI projects up for success by getting that first step right. You'll see how to frame the problem correctly, learn why step 1 has to be business-led and understand why deployment and operationalization depend on getting step 1 right.
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James Taylor will also moderate this expert panel session:
Model Interpretability: When and How?
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Model explainability or interpretability is often demanded, posed as a requirement. But not always. Under what circumstances is it pragmatically necessary (or even legally required) -- and, when it is called for, what exactly does it mean? Would it suffice to explain each model prediction by showing what differences in inputs would have changed the prediction? Or must the model be "understood" globally? Is understanding how the model derives its predictions sufficient, even without the why -- that is, without causal explanations for the correlations it encodes?
Join this expert panel to hear seasoned experts weigh in on these tough questions.
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Featuring eight full-day workshops -- including:
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Monday, June 19th, 2023 | 8:30am – 4:30pm PDT
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Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it. To get to that point, your business must follow a gold standard project management process, one that is holistic across organizational functions and reaches well beyond executing the core number crunching itself.
At this workshop, you will gain a deep understanding of the concepts and methods involved in operationalizing machine learning to deliver business outcomes. This workshop focuses on the elements of a machine learning project that define and scope the business problem, ensure that the result is useful in business terms, and help deliver and operationalize the machine learning outcome. Based on CRISP-DM – the most well known, established industry standard management process for machine learning – this course does not dive into the core machine learning technology itself, but focuses instead on how machine learning must be applied in order to be effective. Attendees will have opportunity to apply what they learn to real-life scenarios.
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Sunday, June 18th, 2023 | 8:30am – 4:30pm PDT
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This one-day workshop reviews major big data success stories that have transformed businesses and created new markets.
Marc and Vladimir will cover these revealing stories in order to illustrate the key concepts, tools, and value-proven applications driving the big data revolution.
“Big data” is a open buzzword – it could be defined as any amount of data you can’t afford to handle – but the big, newfound value achieved by computing at scale is no fad.
What you will learn:
- Where does big data come from: Common sources of big data.
- What makes data big: Velocity, Variety, and Volume!
- How can we leverage it: Open tools and platforms for storing and analyzing big data.
- The new paradigm: Today’s shift from hypothesis testing to a broad exploration for correlations is a revolutionary change in the way data is explored.
- Where data science comes into play: Best practices in the field.
- Best practices for analyzing big data: Key methods in data science, predictive analytics, machine learning, and network and text analytics to analytically learn from data.
- Social Data: Finding key connections in webs of people and events.
- Applications of big data insights to business.
- Future directions in big data: bigger, bolder, and better.
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Take advantage of the early bird price and secure your ticket to Machine Learning Week.
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