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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?


Additional Resources

  1. Original AutoML notebook
  2. Iris notebook
  3. Why it’s hard to design fair machine learning models](
  4. AI Trust
  5. Removing Unfair Bias Part 1
  6. Removing Unfair Bias Part 2
  7. AI Explainability
  8. ML Ops Day, OSCON 2019
  9. OSS related to AutoML
  10. Awesome AutoML Paper
  11. Labeling, transforming, and structuring training data sets for machine learning
  12. Data cleaning is a machine learning problem that needs data systems help!


  1. Show Your Work
  2. Energy and Policy Considerations for Deep Learning in NLP
  3. The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning


  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 See the section above for a listing.

  6. Who can attend this session?

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

  7. 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.


Paco Nathan, expert in data science, natural language processing, machine learning and cloud computing,


Upkar Lidder, IBM Data Science and AI Developer Advocate,


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