Nowadays AI is all the hype, but what many might not know is that AI is an established discipline originating from a workshop in the 1950s. In this talk I will present the historical milestones of AI from the originating workshop up until present days. In addition we will look into the crystal ball in order to see what the future might have in store. See Less

Databricks is an analytics platform based on Apache Spark, optimised to run on the Microsoft Azure cloud. This tutorial presents a complete Data Science workflow based on Databricks, from data ingestion to deployment through exploratory analysis and modelling.

In particular, the tutorial covers the following topics

• Deploying a new Databricks cluster on the Azure cloud
• Loading data from different sources, particularly large CSV files
• Querying using Spark SQL
• Predictive modelling using Python

Other topics introduced during the course of the tutorial include

• Accessing data stored on Azure Blob Storage directly
• Managing and sharing notebooks
• Building ETL pipelines from notebooks using jobs.

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Current production search solutions are not taking advantage of the last research on NLP (Natural language processing) and Machine Learning. We are going to explore how you can apply ML and NLP to build a search index, and see how they can be applied to problems like finding the best answer on a FAQ, or discovering similar documents. See Less

In this session we'll discuss the opportunities presented by AI, IBM's history in this space and how that's got us to where we are today. We'll take a look at the challenges faced when building AI at scale and how IBM are collaborating to design infrastructure specifically designed for the job. Finally, we'll explore how you can benefit and get started today. See Less

AI is such a buzzword, with its futuristic implementations and sophisticated machine learning algorithms (Hello, Deep learning!). We are using ML when we need external data to reach a working product because it would be impossible to solve it with the regular for/if/loops. What are the next steps? Moreover, what about Test, Release, and Deployment? We always value data and call our organizations “data-driven”, but now the impact is even bigger. If you are using an ML component, misused/dirty/problematic data will affect not your internal reports as before… but your application deployment and quality of service. Let’s discuss some AI implementations stories (its advantages/problems) finding common mistakes and future challenges for such a hyped theme to end the session with a live demo (if the gods allow it!)! See Less

What about making your app smarter without any knowledge in AI? With pre-trained models and a few lines of code, Machine Learning APIs can analyze your data. Moreover, AutoML techniques can now help in getting even more specific insights tailored to your needs. In this session, you’ll see how to transform or extract information from text, image, audio & video with the latest ML APIs, how to train an AutoML custom model, and you’ll be an active player of a live demo. Don’t put your smartphone in airplane mode! See Less

TensorFlow is an open-source software library for dataflow programming across a range of tasks, used for machine learning applications such as neural networks. Among the supported APIs, a Go API that allows training and serving the model is available. If you want to understand the basics of how Tensorflow works, and never had the chance to experiment with, and have basic familiarity with Go, this is the perfect workshop for you. In this workshop we will see examples of usage and talk about what can it do: what actions are supported, what’s in plan, how can this be used for your personal and professional projects and discuss using Go rather than the traditional choice for ML - Python. We will then use the Go API to serve a pre-trained TensorFlow. Ideally you will have the latest versions of Go and TensorFlow pre-installed prior to attending the workshop. See Less

Artificial Intelligence, even without significant progress from its state today is generating huge amounts of economic and personal value, but it’s not always shared fairly. Today, women form around 17% of Silicon Valley tech jobs and only 10% of people working directly with AI. As a result, we are seeing bias in AI, where systems work better for white men, and at their worst discriminate against groups in society. In this presentation, we will look at examples of bias in AI, the gender bias problem and ways that we might improve. See Less

Elastacloud are at the forefront of innovation in the solar energy market. We use the scale of Azure with AI to make predictions on energy output from large portfolios of solar farms. This talk lays out the fundamental architecture, technology and approaches that make the platform work beginning with key features of Azure Databricks and how it works seamlessly with Azure Data Lake and Azure Event Hubs. We will cover how Spark ML is used to make real-time decisions. See Less

With the last year's release of TensorFlow.js, machine learning in Javascript is here to stay. In this session, we’ll have a look into why on Earth is javascript and AI even a thing, what are the benefits of using it over the more traditional competitors, and what the future might look like. See Less