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Edge and Cloud AI Powered Anomaly Detection for Predictive Maintenance

Predictive maintenance is one of many use cases for Anomaly Detection. In ...

Dave Glover

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Taiwan COVID-19 vax card image recognition by Azure Form Recognizer

Azure Form Recognizer has been wide used for invoices, W-2 form, health i...

Ryan Chung

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Machine Learning simplified for Developers with ML.NET

Do you want to try machine learning, but don't want to invest too much time...

Jernej Kavka

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Spoken or written speech insights with AI and Azure Cognitive Services.

Join this session to learn how valuable data insights from text or audio ca...

Catalin Popa

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Create A Multi-Language Translator Using Blazor And Azure Cognitive Services

We will create a Multilanguage translator using Blazor and the Translator...

Ankit Sharma

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Let's talk GANs

In this session we would be talking about what Generative Adversarial Ne...

Rohan Raj

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Data data everywhere, No time to think

A talk to break common misconception with data science projects. Understand...

Aman Sharma

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Building a Responsible AI strategy

FATE AI ( Fair, Accountable, Transparent, Ethical) What is Responsible AI?...

Sammy Deprez

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Panel: Start to AI journey with Microsoft Azure

Discussion around to begin the AI journey with Microsoft Azure. Learning Pa...

Kasam Shaikh

Priyanka Shah

Abdul Rasheed Feroz Khan

Hatim Nagarwala

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Airplane buddy matching using Azure Form Recognizer

Let's dive into the computer vision world by designing a system that can a...

Radu Vunvulea

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Anomaly Detection, Powered by Azure AI and Digital Twins

Detecting the Onset of System Failure using Anomaly Detection Techniques is...

Mihail Mateev

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The dawn of large neural networks and their transformative impact on AI

The last two years have seen a dramatic growth of neural networks. Models...

Adam Grzywaczewski

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Real-Time Machine Learning with Azure ML Endpoints

Are you using the most appropriate machine learning approach for your use...

Vlad Iliescu

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Machine Learning Frameworks Interoperability

To develop mature data science, machine learning, and deep learning applica...

Miguel Martínez

Christian Hundt

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The price is right! - Machine Learning thinking of costs

From an academic point of view, Machine Learning models generally try to mi...

Pau Sempere

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AI for the better

Do you want to be inspired what AI can do for the better (life of people of...

Dennie Declercq

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AI for Accessibility: Improving reading fluency

Practicing pronunciation and getting timely feedback are essential for...

Luis Beltran

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Building a SmartMirror with 15+ Azure AI Cognitive Services

Speech Technologies, ComputerVision, Internet of Things, ... a picture for ...

Jean-Philippe Encausse

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Speak to your Power App

Use Azure Cognitive Services to speak to your Power App and convert your...

Roberto Corella

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Troubleshooting real-time business metrics with StarTree ThirdEye

StarTree ThirdEye is a source available anomaly detection platform that...

Madhumita Mantri

Suvodeep Pyne

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Hacking Your Vacation: Using Data for Fun

Data can be powerful, meaningful, AND fun. Making data-driven decisions can...

Becky Gandillon

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AI to the edge with Azure Percept

AI enabled IoT creates intelligent machines that simulate smart behavior a...

Ron Dagdag

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GPT-x, is it really worth it?

GPT, the state of the art for NLP and NLG. People go crazy in front of the...

Sebastiano Galazzo

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Responsible AI

Nowadays with the development of AI, it became a big part of our lives. It...

Veronika Kolesnikova

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Finding Dory using MS Custom Vision API

Dory (my fish) is lost in the deep ocean. Let's go on mission to find Dory...

Vedant Bahel

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AWS vs Azure vs Google AI

In this presentation, awarded Google Developer Expert and Microsoft Most...

Chander Dhall

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Panel: Start to AI journey with Microsoft Azure

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Discussion around to begin the AI journey with Microsoft Azure. Learning Path, Certifications prep, Cinch with Tooling, etc would be the topic of discussion.

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Kasam Shaikh

Priyanka Shah

Microsoft AI MVP, Innovation Leadership (AI/IoT)

Abdul Rasheed Feroz Khan

Director - CodeSizzler

Hatim Nagarwala

Community leader

Anomaly Detection, Powered by Azure AI and Digital Twins

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Detecting the Onset of System Failure using Anomaly Detection Techniques is one of the key demands in Industry 4.0. Nowadays implementation of this functionality is often related to two concepts: Digital Twins and Anomaly Detection with AI
Anomaly detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, anomalous data can be connected to some kind of problem or rare event such as e.g. bank fraud, medical problems, structural defects, malfunctioning equipment etc.
Digital twins have been around for several decades, the rapid rise of the internet of things (IoT) is that they have become more widely considered as a tool of the future. Digital twins are getting attention because they also integrate things like artificial intelligence (AI) and machine learning (ML) to bring data, algorithms, and context together
In this presentation will be discussed various techniques that can be used to detect the onset of failure occurring in systems in the context of Microsoft Azure, using Azure Digital Twins Service and Azure Cognitive Service Anomaly Detector API.
Azure Digital Twins is a game-changer in the modern IoT and AI solutions. This a SaaS offering easy to build digital models of complex systems.
The Anomaly Detector API enables you to monitor and detect abnormalities in your time series data with machine learning. This feature adapts by automatically identifying and applying the best-fitting models to your data, regardless of industry, scenario, or data volume.
This talk is covering the whole content from Digital Twins and Anomaly Detection concepts via Azure based implementation demonstrating real use cases and demos.

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Mihail Mateev

Senior Solution Architect at EPAM Systems, Soft Project, Owner

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The dawn of large neural networks and their transformative impact on AI

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The last two years have seen a dramatic growth of neural networks. Models such as Bert Large with its 345 million parameters which were considered huge just three years ago are being dwarfed by modern DNNs which are frequently more than 1000 times bigger. This growth is not driven by accademic vanity of the AI comunity but by their exceptional few shot learning capability. In contrast to their smaller counterparts that require thousands/tens of thousands of training samples to adapt to new tasks large models achieve state of the art performance with just a handful of samples fundamentally transforming the way we do AI. This revolution started in Natural Language Processing but is already transforming countless other disciplines like computer vision, automatic speach recognition, chmical compount modeling or protein modeling where very large unsupervised models are becoming a common practice. In this talk we will dive deeper into the properties of large DNNs and discuss the process of creating them, from data prepartion through distributed training to production deployment.

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Adam Grzywaczewski

Senior Deep Learning Data Scientist at NVIDIA

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Machine Learning Frameworks Interoperability

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To develop mature data science, machine learning, and deep learning applications, one must develop a large number of pipeline components, such as data loading, feature extraction, and frequently a multitude of machine learning models. The complexity of those components frequently requires use of a broad range of software components/tools, creating challenges during pipeline integration. We'll discuss zero-copy functionality across several GPU-accelerated and non-GPU-accelerated data science frameworks including, among others, PyTorch, TensorFlow, Pandas, SciKit Learn, RAPIDS, CuPy, Numba, and Jax. Zero-copy avoids unnecessary data transfers, hence drastically reducing execution time of your application. We'll also address memory layouts of the associated data objects in various frameworks, the efficient conversion of data objects using zero-copy, as well as using a joint memory pool when mixing frameworks.

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Miguel Martínez

Senior Deep Learning Data Scientist at NVIDIA

Christian Hundt

Use GPUs, always.

Troubleshooting real-time business metrics with StarTree ThirdEye

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StarTree ThirdEye is a source available anomaly detection platform that helps with monitoring, anomaly detection and root cause analysis of complex business metrics in real-time. In this presentation, we'll walk through some real-life examples of how identifying problems in real-time metrics and diagnosing them quickly is crucial for businesses. We'll cover different data verticals and their challenges and how StarTree ThirdEye attempts to address some of these issues.

Takeaways:
- Explore the landscape around real-time business metrics
- Learn about how StarTree ThirdEye addresses a variety of use cases leveraging a flexible architecture and a pluggable framework
- Learn about the strategies around root cause analysis and how StarTree ThirdEye leverages data to identify potential causations using correlations present in data.

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Madhumita Mantri

Product Lead, StarTree.ai

Suvodeep Pyne

Founding Engineer, startree.ai

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