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AI on Edge, encounter between Data Science and IoT Jean-Pierre Riehl
That session explains how to put AI algorithms on Edge devices, with feedbacks from a computer vision project
The video is not available to view online.
Art of Feature Engineering- For Machine Learning Sandip Pani
The most challenging area of machine learning are Data acquisition, Feature extraction, Feature Selection. Almost in all data science project, 80% of time people spend in Data acquisition and Feature engineering.
Rapid Requirements: Introducing the Machine Learning Canvas Terry McCann
In this session, we will introduce the Advancing Analytics Machine Learning Canvas and how it can be used to capture requirements for Machine Learning Projects.
Taking Models to the Next Level with Azure Machine Learning Amy Boyd
Learn how Tailwind Traders data science team uses Azure Machine Learning features and services to create bespoke open source NLP models and optimise them. Includes Automated ML, Azure ML SDK and Hyperparameter tuning
An Introduction to AI on Azure Robin Lester
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
The video is not available to view online.
Azure Data Lake : SELECT * FROM DataLake WHERE People=Happy Jean-Pierre Riehl
See how to analyze images in your Data Lake with Azure Data Lake Analytics, U-SQL and custom models
The video is not available to view online.
Azure Databricks for ML on Big Data workloads Mahesh Balija
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.
The video is not available to view online.
Azure Machine Learning: Applying Software Engineering to ML Amy Boyd
Azure Machine Learning is a platform for developing and deploying your machine learning models on Azure. We will look at the life cycle of ML projects: from data, to model, to consumption. This will include Automated Machine Learning capabilities.
Implementing a CI/CD pipeline for Anomaly Detection and Predictive Maintenance on Azure Mithun Prasad
Implementaing a CI/CD pipeline for Anomaly Detection and Predictive Maintenance on Azure
The video is not available to view online.
So what's this Machine Learning all about? Alex Whittles
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
SQL Server ML End-to-End Anna Hoffman
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. 
The Maths behind Machine Learning Matt Willis
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.
The video is not available to view online.
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