Within Azure we have a rich ecosystem of AI services that can be leveraged to gain new insights into your data. This session will give you an easy to digest breakdown of the key services that matter and how to approach each one. Cognitive Services, Bot Framework, Azure Machine Learning Studio, Databricks, Notebooks, the Azure ML SDK for Python and the Azure ML Service

Azure Databricks support both Classic and Deep Learning ML Algorithms to analyse Large DataSets at scale. The Integrated Notebook experience gives the Data Scientists and Data Engineers to do exploratory Data Analysis, also feels like native to Jupyter notebook users. In this session we will extract intelligence from Higgs Dataset (Particle Physics) by running Classic and Deep Learning models using Azure Databricks. We will also peek into AMl service's integration with Azure Databricks for managing the end-to-end machine learning lifecycle.

Machine Learning is a popular buzzword, but what does it actually look like, and how can we use it? This session will show a number of high level examples of using ML to do some useful and fun stuff, including training a model to play a game

Learn how Machine Learning Services in SQL Server is a powerful end-to-end ML platform for customers, on both Windows and Linux. Come learn about the unique value proposition of doing your entire machine learning pipeline in-database – right from data pre-processing, feature engineering, and model training to deploying ML models and scripts to production in secure and compliant environment without moving data out. 

With a strong focus on the algorithms used in Machine Learning, we will explore the maths involved to gain a deeper understanding. Using practical examples, with Databricks to consume a dataset and Python scripts to execute the models.