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Created May 10, 2020 23:08
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Introduction to AutoML
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automl.md

Introduction to AutoML

AutoML is a very active area of AI research in academia as well as R&D work in industry. The public cloud vendors each promote some form of AutoML service. Tech unicorns have been developing AutoML services for their data platforms. Many different open source projects are available, which provide interesting new approaches. But what does AutoML mean?

Ostensibly automated machine learning will help put ML capabilities into the hands of non-experts, help improve the efficiency of ML workflows, and accelerate AI research overall. While in the long-term AutoML services promise to automate the end-to-end process of applying ML in real-world business use cases, what are the capabilities and limitations in the near-term?

Links

Replay

Additional Resources

  1. Workshop github
  2. Workshop github
  3. Workshop github
  4. Workshop github

FAQs

  1. The video is paused for me, what do I do?

    You have to hit the play button in some browsers as the video does not auto play.

  2. Is this webinar being recorded?

    Yes, the webinar is being recorded, you can view the replay on the same link once the event ends.

  3. Where are the slides?

    See the links above for workshops and resources for each of the sessions.

  4. Do you have study materials or courses available?

    See additional links above for additional reading materials

  5. Do you have a certification ?

    There is no certification provided at this point, but IBM offers a number of courses and certifications on Coursera and Cognitive.ai. See the section above for a listing.

  6. How do I access the replays of the individual sessions?

    You can access the individual sessions by using the drop down in the top left corner as shown in the screenshot below. image

  7. Who can attend these sessions?

    Developers, data scientists and architects. Anyone interested in building and deploying AI models.

  8. What will I learn?

    • Learn about the different kinds of AutoML techniques that are currently used
    • Learn about the capabilities and limitations of AutoML in the near-term, as well as research efforts in progress
    • Learn about available open source libraries for working with AutoML techniques
    • Learn about how to leverage this area of technology to improve the end-to-end lifecycle for machine learning workflows
    • In the interactive lab portion, we will review coding samples that compare use of different open source projects for AutoML.

Speakers

Paco Nathan, expert in data science, natural language processing, machine learning and cloud computing, https://derwen.ai/paco

Host

Upkar Lidder, IBM Data Science and AI Developer Advocate, https://www.linkedin.com/in/lidderupk/

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