AutoML which stands for Automated Machine Learning empowers data teams to quickly build and deploy machine learning models. It aims to reduce the time and expertise required to generate a machine learning model by automating the heavy lifting of preprocessing, feature engineering, model creation, tuning and evaluation. When it comes to machine learning in Microsoft Azure, there are two main options for running your AutoML: (1) Azure Machine Learning Service and (2) Azure Databricks. This session will aim to introduce how to develop ML models using AutoML on both platforms, as well as the features of each and why you would choose one over the other.
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The aim of this session is to highlight the process of implementing Azure Synapse Link for Dataverse and to discuss the reality of working with the data as it is incrementally updated in Azure.
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Machine Learning, Data Science, Artificial Intelligence. These are all big words we hear coming into our businesses lately - but what does it all mean?! Microsoft has created a set of simple and scalable tools that any developer can use and integrate into their applications super quickly! This session will focus on the various Cognitive Service offerings, where we can understand why and when we should use Pre-Build AI. Come and learn how to take advantage of these awesome services for your everyday work!
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In this talk, we will be leveraging Azure computer vision. The Azure Computer Vision is part of Azure Cognitive Services that provides pre-built, advanced algorithms that process and analyse images. If you want to get started with computer vision but do not know where to start, then this talk will give you a good starting point to jumpstart your computer vision journey. You will leave this talk with an understanding of how to build and deploy your own computer vision models using Azure computer vision.
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Going from an machine learning model trained on your laptop in a notebook called “trainmodelV1Final_FINAL (1).ipynb” to a system ready to deploy is difficult. However, MLOps (a set of principles to prepare your model for prime time) is here to help! This talk is an introduction to all the elements you need to get your code production-ready - CI/CD, dev/UAT/prod, pipelines, and more! We'll walk through system diagrams, with a focus on Azure, but the takeaways will all be platform agnostic. Make sure your model deployment isn’t an ML-Flop!
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We will go through a brief introduction to what Machine Learning is and some of its applications. we will then explore why AutoML should be used by all; as a great starting point for anyone new to Machine Learning, as well as a time saving tool for those more experienced. Finally, we will expose a model as an endpoint and understand how we can use it. Specifically in this case how to use it in excel via VBA. Technologies I will demonstrate are: - Azure Machine Learning Studio - VS code (with Azure Machine Learning Studio extension) - Python - VBA
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Creating a new episode of Buffy the Vampire slayer with Azure Machine Learning
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Join Patrick Leblanc from the Power BI CAT team, Josh Luedeman, and Bradley Ball from the Azure FastTrack PG as we talk about the most powerful Azure Service you’ve never heard of. If only you knew the name of the service. Come to this session, and you’ll never forget it!
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In this session, you will get to learn how to enrich your data in spark tables with models created using Automated ML in Azure Synapse Analytics. We will be looking at a demo where we will create regression and classification models using Automated ML and how to use these models for prediction in Azure Synapse Analytics.
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Understand how to get started with any machine learning project using Databricks AutoML
The Key takeaway is that attendees will get ideas about the skills and knowledge required to recognize an opportunity for a machine learning application and seize it. Also attendee gets to know well about Azure Machine Leaning.
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In this session, we will cut through the marketing buzzwords to share experiences, tips, and tricks on how to be successful with Data Science and Analytics in the real world. Tune in to hear the team share real-world experience and get takeaways from industry insiders on real projects with impact. We will also discuss the ethics and fairness of Data Science and Analytics projects and how we can be more inclusive from a technology, people, and process standpoint.
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Deployment == Return on investment. This session looks to show you how to do that for Machine Learning.
Being data-driven is all about making decisions based on insights generated using data.​
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Being data-driven is all about making decisions based on insights generated using data.​
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Come and see the next steps in the evolution of Flyway.
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How to Choose an ML Platform
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In this short session we will go through the pitfalls, lessons learnt and best practices when building your AI consultancy practice.
In this lightning talk I'll demonstrate how to apply the R implementation of Benford's law (which actually is not about crime or fraud) to identify possibly fraudulent invoice or other data.
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A variety of 5 minute sessions to include:
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