MLSEV Conference Videos: ‘Six Challenges of Machine Learning’

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We really enjoyed virtually hosting thousands of business professionals, developers, analysts, academics, and students during the two jam-packed days of training last week as part of the Seville Machine Learning School. To us, it was one more piece of evidence that Machine Learning is a global phenomenon that will keep positively impacting all kinds of industries as the world economy recovers from the effects of Novel Coronavirus.

As promised during the event, below are useful links to the material covered during MLSEV for your review and self-study as well as related pointers for follow up actions you can take.



One of the cornerstones of MLSEV was BigML Chief Scientist, Professor Tom Dietterich‘s presentation on the State or the Art in Machine Learning.  Professor Dietterich specifically talked about the Six Challenges in Machine Learning by providing the historical perspective for each point as well as the present-day state of affairs as it applies to the advances in research. These six challenges are:

  1. Generalization
  2. Feature Engineering
  3. Explanation and Uncertainty
  4. Uncertainty Quantification
  5. Run-time Monitoring
  6. Application-Specific Metrics


The video above is a must-watch to find out more on each topic and get caught up with some of the best new ideas the Machine Learning research community has been able to offer in recent years. For brevity, we’d like to open up a special parenthesis for Explanation and Uncertainty, which is near and dear to our hearts at BigML. ML Explanation and Uncertainty

In order for interpretability to make a difference, we need to refine the context in which explanations are needed. In many, cases that means understanding the end-user persona to consume the said explanations of the model and its predictions. Sometimes the persona is a Machine Learning engineer worried about more higher-order concepts like model performance metrics and overfitting. Other times, the end-user can be a frontline worker or end-consumer that will be looking for a simple cue (e.g., a recommendation for a similar product that’s in stock). A world-class smart application should be able to discern between the two scenarios while satisfying the needs of both sets of users.

We urge you to watch the rest of the video and find more about these key topics. One more thing…in the coming weeks, we will be covering other focal topics and themes from MLSEV as part of this blog series, so stay tuned!

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