Webinar

AI Models and Active Learning

Date: On-Demand
Time: 1 hour

The increased availability of computer resources and the prevalence of high-quality training data combined with smart learning schemas, have resulted in a rise in successful AI deployments. However, many organizations simply have too much data, posing a challenge for data scientists: unless at least some of that data is labeled, it's essentially useless for any ML approach that relies on supervised or semi-supervised learning. So, which data needs to be labeled? How much of a dataset needs to be labeled for an ML application to be viable? How can we solve the problem of having more data than we can reasonably analyze?

One promising answer is active learning. Active learning is unique in that it can both solve this data labeling crisis and train models to be more accurate with less data overall. Join us for this latest Data Science Central webinar where we'll cover:

  • The pros and cons of active learning as an approach
  • The three major categories of active learning
  • How your active learner should decide which rows need labeling
  • How to tell if active learning is appropriate for your ML project
Speakers

Jennifer Prendki

VP of Machine Learning, Figure Eight

Jennifer has spent most of her career creating a data-driven culture wherever she went, succeeding in sometimes highly skeptical environments. She is particularly skilled at building and scaling high-performance Machine Learning teams, and is known for enjoying a good challenge.

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Name Last

Job Title

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent neque elit, blandit nec aliquam tincidunt, tristique id lorem. Aliquam dapibus bibendum mauris. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Vivamus quis eleifend purus. Nunc eget nibh ante. Cras nec nisi vel dolor eleifend aliquam.
Speakers

Jennifer Prendki

VP of Machine Learning, Figure Eight

Jennifer has spent most of her career creating a data-driven culture wherever she went, succeeding in sometimes highly skeptical environments. She is particularly skilled at building and scaling high-performance Machine Learning teams, and is known for enjoying a good challenge.

Name Last

Job Title

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent neque elit, blandit nec aliquam tincidunt, tristique id lorem. Aliquam dapibus bibendum mauris. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Vivamus quis eleifend purus. Nunc eget nibh ante. Cras nec nisi vel dolor eleifend aliquam.

Name Last

Job Title

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent neque elit, blandit nec aliquam tincidunt, tristique id lorem. Aliquam dapibus bibendum mauris. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Vivamus quis eleifend purus. Nunc eget nibh ante. Cras nec nisi vel dolor eleifend aliquam.