Track:
Learn about machine learning in practice and on the horizon
Eric is the CEO of streamdata.io. He was a founding team member at Internet Way (French B2B ISP, sold to UUnet) then Radianz (Global Finance Cloud, sold to BT). He is a High Frequency Trading infrastructure expert, passionate about Fintech, IoT and Cleantech. Eric looks after 3 bozons and has worked in San Francisco, NYC, Mexico and now Paris.
by Sahil Dua
Developer at Booking.com; Open Source Contributor in DuckDuckGo, GitHub and Pandas
by Alison Lowndes
Artificial Intelligence DevRel @NVIDIA
by Guillaume Laforge
Developer Advocate at Google Cloud and PMC Chair for Apache Groovy
by Antoine Pichot
Quantitative Researcher @Systematica Investments
by Sarah Aerni
Director, Data Science @Salesforce Einstein
by Eric Horesnyi
CEO @streamdata.io
by Philip Winder
Consultant, Engineer, Scientist @Winder Research and Development Ltd.
Join the track speakers and invited guests as they discuss where AI is heading and how it's affecting software today.
by Sahil Dua
Developer at Booking.com; Open Source Contributor in DuckDuckGo, GitHub and Pandas
While there are a lot of machine learning frameworks and libraries available, putting the models in production at large scale is still a challenge. I’d like to talk about how we took on the challenge of supporting the data scientists with their efforts by making it easy to put their models in production. I’ll be covering how we:
- chose our tools and developed the internal deep learning infrastructure
- train our models in docker containers...
by Alison Lowndes
Artificial Intelligence DevRel @NVIDIA
Artificial Intelligence will improve productivity, products and services, across a broad range of applications, all benefiting humanity. NVIDIA is researching all areas and working closely with top research labs around the world, Enterprise & startups in both problem-solving and getting started. Alison's talk will briefly cover the HW & SW that comprise NVIDIA's GPU computing platform for AI, across PC to data center, cloud to edge, training to inference. The talk will also detail...
by Guillaume Laforge
Developer Advocate at Google Cloud and PMC Chair for Apache Groovy
The biggest challenge of Deep Learning technology is the scalability. As long as using single GPU server, you have to wait for hours or days to get the result of your work. This doesn't scale for production service, so you need distributed training on the cloud eventually, or take advantage of pre-trained models. Google has been building infrastructure for training the large scale neural network on the cloud for years, and started to share the technology with external developers. In this...
by Antoine Pichot
Quantitative Researcher @Systematica Investments
In the Financial industry, Artificial Intelligence has been one of the sophisticated techniques used by early adopters to manage multiple assets. Those early adopters are Quantitative Hedge Funds, around since the 80s and managing today an estimated USD 940 billion. After presenting the main trends in the Asset Management industry, I will describe the set-up of a Quantitative Hedge Fund as well as some of the problems that AI helps solving in the industry.
by Sarah Aerni
Director, Data Science @Salesforce Einstein
Companies are redefining their businesses by building models and learning from data. Whether it is using data science to predict their best sales and marketing targets, automating digital customer interactions using bots, or reducing waste in logistics and manufacturing - Artificial Intelligence will improve your business once deployed.
Serving up good predictions at the right time to drive the appropriate action is hard. It requires setting up data streams, transforming data, building...
Tracks
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Microservices/ Serverless: Patterns and Practices
Stories of success and failure building modern service and function-based applications, including event sourcing, reactive, decomposition, & more.
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Distributed Stateful Systems
Architecting and leveraging NoSQL revisitied
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Evolving Java and the JVM: Mobile, Micro and Modular
Although the Java language is holding strong as a developer favourite, new languages and paradigms are being embraced on JVM.
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The Practice & Frontiers of AI
Learn about machine learning in practice and on the horizon
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Operating Systems: LinuxKit, Unikernels, & Beyond
Applied, practical, & real-world deep-dive into industry adoption of OS, containers and virtualisation, including Linux on Windows, LinuxKit, and Unikernels
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Stream Processing in the Modern Age
Compelling applications of stream processing & recent advances in the field
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Leading Edge Backend Languages
Code the future! How cutting-edge programming languages and their more-established forerunners can help solve today and tomorrow’s server-side technical problems.
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Modern CS in the Real World
Applied trends in Computer Science that are likely to affect Software Engineers today.
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DevEx: The Next Evolution of DevOps
Removing friction from the developer experience.
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Bare Knuckle Performance
Killing latency and getting the most out of your hardware
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Tech Ethics in Action
Learning from the experiences of real-world companies driving technology decisions from ethics as much as technology.
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Security: Red XOR Blue Team
Security from the defender's AND the attacker's point of view
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Architecting for Failure
If you're not architecting for failure you're heading for failure
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Architectures You've Always Wondered About
Topics like next-gen architecture mixed with applied use cases found in today's large-scale systems, self-driving cars, network routing, scale, robotics, cloud deployments, and more.
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Observability: Logging, Alerting and Tracing
Observability in modern large distributed computer systems
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Speaker AMAs (Ask Me Anything)
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Building Great Engineering Cultures & Organizations
Stories of cultural change in organizations
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Speaker AMAs (Ask Me Anything)