Presentation: IBM Watson

Track: Hands-on Codelabs & Speakers Office Hours

Location: Mission

Duration: 10:40am - 10:50am

Day of week: Tuesday

Share this on:

Abstract

Want to work with IBM Watson, but don't know how to get started?

Watson on the IBM Cloud allows you to integrate the world's most powerful AI into your applications and store, train and manage your data in the most secure cloud. With Watson Visual Recognition, you can quickly and accurately tag, classify and train visual content using machine learning. With Watson Assistant, you can quickly build and deploy chatbots and virtual agents across a variety of channels.

Come to this 10-minute talk, and we'll help you get started. Then stay for our office hours to work with Watson via a few code labs, including Conversation Robot lab to work with Watson Assistant and Watson Speech to Text, and a code lab to create a Core ML model using Watson Visual Recognition, and then deploy it into an iOS application.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Attendee: Yacine Rezgui

Developer Advocate @IBM

Before becoming a Developer Advocate at IBM, Yacine was a developer in several startups in London working on school social networks, data mining, business intelligence, e-commerce tools and mobile enterprise solutions. He studied computer science at Nice Sophia-Antipolis University. He speaks English, French, Arabic and is getting started on Korean. At IBM, he arranges and participates in a range of developer activities, from conferences to meetups to corporate hackathons, and helps enterprises succeed with incorporating artificial intelligence into their strategy. He has a strong interest in democratizing computer science education outside of the tech industry.

Follow Yacine at @yrezgui and github.com/yrezgui 

Find Yacine Rezgui at

Speaker: Ross Cruickshank

Developer Advocate @IBM

Ross Cruickshank is a Developer Advocate in the London City team, helping developers (startups through enterprise) across the UK benefit from IBM Cloud platform and services. He has a broad background in development (hardware and software), operations (network and data centre) and security, and is an experienced services consultant. Industry experience in engineering, banking, (re)insurance, utilities, and transport. He is keen and active in recruiting into and maintaining the IBM and open source ecosystem. Living in rural England, and experienced the variability of mobile carrier coverage, he has developed in interest in LPWAN technologies, and hosts both SIGFOX and LoRaWAN public access gateways. Hackathon opportunities are greatly appreciated, particularly weekends. Today, he will be mostly building prototypes and event-oriented systems with Node-RED, IOT and the Watson cognitive and data platform.

Speaker: JeanCarl Bisson

Developer Advocate @IBM

JeanCarl Bisson is an IBM Developer Advocate and found his passion of building rapid prototypes. He inspires everyone, from beginner to advanced developers, to build their ideas via rapid prototyping methodologies by breaking down larger projects into smaller pieces. He enjoys tinkering with Internet of Things, IBM Watson, and serverless technology. In his spare time when not building random stuff, he enjoys photography, traveling, and watching rocket launches.

Follow JeanCarl at @dothewww, jeancarlbisson.com and github.com/jeancarl

Find JeanCarl Bisson at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.