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?
This talk surveys the landscape and history for projects and research efforts related to AutoML, looking beyond just hyperparameter optimization and considering the impact on end-to-end workflows and data science practices.
We will show sample code using different open source projects and provide pointers to online resources to learn more.
Known as a "player/coach", with core expertise in data science, natural language processing, machine learning, cloud computing; 35+ years tech industry experience, ranging from Bell Labs to early-stage start-ups. Co-chair Rev and JupyterCon. Advisor for NYU Coleridge Initiative, IBM Data Science Community, Amplify Partners, Recognai, Primer. Formerly: Director, Community Evangelism @ Databricks and Apache Spark. Cited in 2015 as one of the Top 30 People in Big Data and Analytics by Innovation Enterprise.