Executive Briefing: Unpacking AutoML
AutoML is one of the hot topics at the forefront of AI research in academia as well as R&D work in industry. Nearly all of the public cloud vendors promote some form of AutoML service. Tech unicorn companies such as Uber have also been developing AutoML services for their data platforms, which are migrating into open source. Meanwhile a flurry of tech startups promise to democratize machine learning for enterprise customers. Ostensibly, automated machine learning will help put ML capabilities into the hands of nonexperts, 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, there are still questions about the capabilities and limitations in the near term and if there are business risks involved.
Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoML—translating from the hype into business concerns and practices in a vendor-neutral way. You’ll take a look at where the boundaries are emerging between what we call machine learning and what we call artificial intelligence—all jokes about PowerPoint aside. Then you’ll look toward near-term future scenarios: What considerations a business leader should be making in the industry today to prepare for the on-the-ground realities tomorrow.
- Familiarity with business use cases for machine learning
What you'll learn
- Translate the many descriptions and claims about AutoML into current and near-term business realities
- Understand the capabilities and limitations of AutoML services
- See a landscape of emerging vendors, open source projects, and ongoing R&D efforts
- Learn how to prepare as a business leader for the changes that the realities of AutoML imply
Paco Nathan is known as a “player/coach” with core expertise in data science, natural language processing, machine learning, and cloud computing. He has 35+ years of experience in the tech industry, at companies ranging from Bell Labs to early-stage startups. His recent roles include director of the Learning Group at O’Reilly and director of community evangelism at Databricks and Apache Spark. Paco is the cochair of Rev conference and an advisor for Amplify Partners, Deep Learning Analytics, Recognai, and Primer. He was named one of the "top 30 people in big data and analytics" in 2015 by Innovation Enterprise.
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