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:
SQL Server is a complete Machine Learning platform - learn a complete process to use it from data ingestion to model deployment.
In this session we will share our experiences and examples of real-world AI scenarios and architectures from around the globe, covering a variety of use cases across different industries.
This session is designed to explore some of the powerful features that made Databricks the leader in the Gartner 2021 Quadrant for Data Science and Machine Learning.
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An introduction to the philosophy of tidy data and the collection of R packages called Tidyverse that help to treat your data appropriately. Including lots of demos from ingesting to cleaning to visualizing your data.
Curious about Visual Recognition and Object Detection in Azure? Are you wondering what the difference is between the Computer Vision API and the Custom Vision API? Get up Speed with the Vision API in less than an hour!
That session explains how to put AI algorithms on Edge devices, with feedbacks from a computer vision project
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DBA and Data scientists should work together! Analyzing data gathered with XE and Query Store data using R or Python for better database insight and discovering hidden patterns.
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What is the difference between Artificial Intelligence, Machine Learning and Deep Learning and how can each be used? Join Buck Woody cover simple, clear explanations for these technologies, how they can be applied.
AI and data is at the center of the digital feedback loops. We have invested in a comprehensive portfolio of AI tools, infrastructure and services. Come to this session to get an update of Azure AI with demos.
Understand how to create, train and operationalize your data science projects in Azure Machine Learning.
Come learn how you can convert you data into structured formats, apply machine learning skills and index it to make it easy to find information and relationships that can save you time and money.
If AI is on the cards in your business you might need to make a recommendation as to whether your company should Buy or Build. Come to this session to work out the moving parts to turn "It Depends!" into an answer.
We will see how to construct a cloud-first architecture based on serverless data analytics. We will look at specific challenges and cost saving strategies, to produce a reliable, scalable and cost effective solution!
Taken from the 20+ years of field experiences, many common statistical and data science mistakes have been detected. Session will tackle couple of them.
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Learn how Azure supports interactive, exploratory notebooks (e.g. Jupyter) for data processing and experimentation across a range of scales from simple single-computer work up to massively parallel Databricks clusters.
Come and join us this session to unleash the power from your data as we introduce Machine Learning Services for Python and R across the SQL Platform.
How to understand where bias lies, how to collect data and the impact of bias and ethical considerations in your data science solutions.
In this session, we will discover how to utilize common machine learning approaches for daily SQL Server DBA tasks.
Wanted to join with open-source projects but don’t know how? This quick session will give you all that you need to start doing so.
In this session, we will discuss the basics of IA and how we can apply it in our business using the Azure Machine Learning Services
This short session will cover some scenarios of cleansing data in Power Query using M, Python, or both languages.
In this session we’ll look at what ML tools are available in the Power BI and Azure worlds, and how you go about using these in your Power BI reports
In this session we focus on how Spark implements Machine Learning at Scale with Spark ML.
Are you a .NET developer who's being FOMO'ing over all the machine learning goodness? Thanks to ML.NET, this is no longer the case! Learn how you can use your C# skills to build some awesome ML applications!
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How can data professionals help a company deliver more personal and prompt communication to customers that are demanding it? Learn how data knowledge and the power of Azure Cognitive Services APIs can make that happen!
Are you looking to take your career to the next step? Microsoft Certifications help validate knowledge and ability required to perform current and future industry job-roles in a modern digital business. Our certification
Azure Databricks has become one of the staples of big data processing. See how to make the most of it by understanding how Spark works under the covers.
This session provides an end-to-end walk through of how to use Azure Synapse for cloud-hosted advanced analytics, based around a real-world predictive maintenance use case.
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.
An opportunity to explore Scala, and why it is truly a “Data Engineers language”. Using Azure Functions, Data Factory, Azure Data Lake Gen2 and Databricks the basics will be explored, followed by real world examples
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