Machine Learning for DevOps Summit Europe

RE•WORK

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Discover how to optimize DevOps and enhance automation capabilities with machine learning

#reworkDEVOPS

Previous Speakers include:

Marios-Eleftherios Fokaefs

Assistant Professor

École Polytechnique de Montréal

Nicola Montecchio

Music Information Retrieval Scientist

Spotify

Alan Lu

Director of Engineering & Applied Science

eBay

Courtney McGill

Lead Machine Learning Engineer

Bank of New York Mellon

Bernhard Hientzsch

Director, Head of Model, Library, and Tools Development for Corporate Model Risk

Wells Fargo

Pooyan Jamshidi

Assistant Professor

University of South Carolina

Diego Oppenheimer

Founder & CEO

Algorithmia

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Get to know the speakers

Since its release in 2014, Alexa has become increasingly sophisticated at understanding American English-speaking users. Alexa has gone on to expand to German and Japanese, as well as British, Canadian, Indian, and Australian dialects of English.

Google released a tool called DeepVariant that uses deep learning to identify all the mutations that an individual inherits from their parents. Engineers at Google Brain and Verily (Alphabet’s life sciences spin-off) have taught one model to take raw sequencing data and line up the billions of As, Ts, Cs, and Gs that make you you.

About a year ago I left Google to join Slack, and I hit the ground running. The team I joined was really excited about the platform we were planning to launch and things were super hectic. We were working with partners to implement bots, deliberating if developers will understand and buy into the concept of bots, and speaking at events to introduce this notion of the conversational office.

Conservation Metrics, a recipient of Microsoft’s AI for Earth grant program, is using algorithms to analyze a corpus from Cornell University Lab of Ornithology’s Elephant Listening Project, which collects data from acoustic sensors embedded throughout Nouabalé-Ndoki National Park and adjacent logging areas in the Republic of Congo.

How can you leverage Machine Learning

The adoption of machine learning is being used more & more to improve DevOps practices. Examples include: troubleshooting and triage analytics; preventing production failures; ensuring application delivery; managing production and alert storms.

Best practices & examples

Discover best practices when implementing ML tools to DevOps, including the preparation & monitoring phases, to ultimately deliver more value to your business through better automation, more efficient problem solving, reduced operational complexity, and increased collaboration.

Expand Your Network

A unique opportunity to interact with industry leaders, developers, data scientists, DevOps specialists & IT decision makers leading the ML revolution. Learn from & connect with 200+ industry innovators sharing best practices to implement machine learning into DevOps.