Practical Machine Learning: How To Decide What Really Matters

Big data is beginning to make a big difference when it comes to building machine learning systems. Moreover, many of the methods for building these systems don’t require years of study before you can apply them. In this talk, I will present examples of key principles that really matter in making choices about data, algorithms and architecture when you build practical learning systems.

From innovations in online recommendations to slicing an internet of things-based anomaly detection problem into approachable pieces, your ability to choose the right simplifcations is an important factor in getting good results. In addition to presenting key guidelines into how to make the right choices I will do a deep dive into a specific example: how to build a powerful but simple recommender with surprisingly easy implementation.

Meet Ted Dunning:

Ted Dunning is Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects and mentor for Apache Storm. He contributed to Mahout clustering, classification and matrix decomposition algorithms and helped expand the new version of Mahout Math library. Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems, he built fraud detection systems for ID Analytics (LifeLock) and he has 24 issued patents to date. Ted has a PhD in computing science from University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. 

An Introduction to Practical Machine Learning: Puppies, Ponies and the Ideas Behind Recommendation

It's useful to take a look at the basic concepts that underlie machine learning, both as a way to better understand the project you are going to build and as a way to better communicate with other teams or organizations. Many of the big ideas are surprisingly accessible to grasp even to those who lack the technical expertise to execute them. I'll provide a quick look at how to find the key ideas around which successful machine learning projects are built.

Meet Ellen Friedman:

Ellen Friedman is a committer for the open source Apache Mahout project and a contributer for the Apache Drill project. She works as a consultant in big data and scientific projects and writes about big data, including being co-author of the book Mahout in Action and the white paper Practical Machine Learning: Innovations in Recommendation. With a PhD in biochemistry, she has years of experience as a research scientist and has written about a variety of other technical topics including molecular biology, medicine and oceanography. Ellen is also co-author of a book of magic themed cartoons, A Rabbit Under the Hat.


[masked]pm: Registration, Networking & book signage 

6:[masked]pm: Introduction 

7:00-7:45pm: Talk by Ted Dunning & Ellen Friedman

7:[masked]pm: Q&A session

In addition to inspiring presentations by Ted Dunning and Ellen Friedman, we will have a book signing session from 6 to 7pm. Make sure to be there on time, get your O'Reilly book 'Practical Machine Learning, Innovations in Recommendation' and have it signed by its authors Ted Dunning and Ellen Friedman.

Join or login to comment.

  • jui s.

    will any slide to be post by host ?

    March 20, 2014

  • Pashu P.

    Ted's slides from last night are now available on slideshare Enjoy and thanks for joining last night!

    2 · March 20, 2014

  • Chantal C.

    Thanks for making it approachable to a large audience.

    3 · March 20, 2014

  • madhu s.

    Thanks, Ellen and Ted, for a great talk.

    March 20, 2014

  • Stanley W.


    March 20, 2014

  • Tam N.

    Excellent talk by professional experts, I was informed but not overwhelmed

    March 20, 2014

  • Chirag

    Will the slides/video be posted?

    March 20, 2014

  • Jega A

    Very good presentation of a complex subject.

    March 20, 2014

  • Menon

    Awesome talk and very informative event - keep it up!

    1 · March 20, 2014

  • Ellen F.

    The audience raised some very interesting questions and discussion after the talk - really a great audience. And thank you O'Reilly and MapR for providing copies of our book to everyone and to Ravi and Pashu for inviting us.

    2 · March 20, 2014

  • Vamsi K.

    Informative and insightful into the world of Recommendations. Thanks Ted/Ellen and Ravi for hosting another great event.

    1 · March 19, 2014

  • Govind T.

    Good - simplified yet provoking interesting discussion and thinking. Thank you Ted and Ellen.

    1 · March 19, 2014

  • dirk h.

    Really liked the easy digestible talk.

    1 · March 19, 2014

  • Ken G.

    Great talk and material. Very informative.

    1 · March 19, 2014

  • Ravi T.

    Just arrived.

    March 19, 2014

  • sedef


    March 19, 2014

  • Pashu P.

    Please make sure to change your RSVP status by 12pm Wed, March 19th if you are not able to attend as this event is sold out - thank you!

    1 · March 18, 2014

  • Paolo m.


    March 18, 2014

  • Pashu P.

    Attending the event? Make sure to read Ellen Friedman's latest blog post before joining the discussion

    March 18, 2014

  • Pashu P.

    This event happening MARCH 19th (and not April as mentioned in a previous email) has reached its maximum capacity.

    If you registered and are not able to attend anymore please change your RSVP status. If you are marked as attendee but don't show up and sign in at the event, we will have to place you on the waiting list for our future events. We are keen to make sure that our free events are available to all who want to come.

    Thank you and looking forward to seeing you all!

    March 17, 2014

  • Pashu P.

    'Practical Machine Learning, Innovations in Recommendation' e-book is available on MapR's website - enjoy and see you all at the event!

    March 13, 2014

  • Pashu P.

    Hello data bees! Good news, in addition to an inspiring talk by Ted Dunning, we will have a book signing session from 6 to 7pm. Make sure to be there on time, get your O'Reilly book 'Practical Machine Learning, Innovations in Recommendation' and have it signed by its authors Ted Dunning and Ellen Friedman. We look forward to seeing you there.

    3 · March 13, 2014

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