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.

Talk : IBM Watson Codelabs Speakers Office Hours [10:50am - 12:50pm]

Talk : IBM Watson

Talk : AI Benefits for the Lazy Hacker

Other talks from track Hands-on Codelabs & Speakers Office Hours

Parallel processing and machine learning @Google
Parallel processing and machine learning @Google
Developer Advocate @IBM
Parallel processing and machine learning @Google
Parallel processing and machine learning @Google
Spark Committer & Open Source Developer Advocate
Engineering Manager @Linkedin focused on Big Data

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

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