Transform 2022

28 NOV - 06 DEC ONLINE
Annual technology conference for software developers, data scientists, and ML engineers about applied artificial intelligence.
1000+
Attendees
6
Days
15+
Talks
5
Workshops

Partners

Want to become a sponsor? Contact us.
devrain
microsoft
nvidia

About Transform

The annual online event for software developers, data scientists, and ML engineers to get up to date with applied AI technologies, cases, the best practices, sharpen skills and get the latest updates from the top experts.

Topics

  • Automated Machine Learning
  • Language and speech services
  • Document processing
  • MLOps
  • Azure OpenAI Service
  • GitHub Copilot
  • AI certifications
  • Deep Learning

How was it?

Watch recordings from the previous Transform conferences.

Speakers

Anton Boyko
Anton Boyko
Kyiv, Ukraine
Architect at @BoykoAnt.PRO, Microsoft Azure Most Valuable Professional
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Oleksandr Krakovetskyi, Ph.D.
Oleksandr Krakovetskyi, Ph.D.
Oleksandr Krakovetskyi, Ph.D.
Kyiv, Ukraine
CEO at DevRain, CTO at DonorUA, Microsoft Regional Director, Microsoft AI Most Valuable Professional, Forbes Technology Council
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Dmytro Turchyn
Dmytro Turchyn
Dmytro Turchyn
Prague, Czech Republic
Artificial Intelligence Lead at CEE HQ
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Stanislav Lebedenko
Stanislav Lebedenko
Stanislav Lebedenko
Kyiv, Ukraine
Solution Architect at Solidify AB, Microsoft Azure Most Valuable Professional, Microsoft Certified Trainer
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Chander Dhall
Chander Dhall
Chander Dhall
Austin, Texas, United States
CEO at Cazton, Microsoft Most Valuable Professional, Google Developer Expert
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Ihor Leontev
Ihor Leontev
Ihor Leontev
Paris, France
CTO at Dvigunity, Microsoft Regional Director, Microsoft Most Valuable Professional
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Adam Grzywaczewski, Dr.
Adam Grzywaczewski, Dr.
Adam Grzywaczewski, Dr.
Coventry, England, United Kingdom
Senior Deep Learning Data Scientist at NVIDIA
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Kevin McFall
Kevin McFall
Kevin McFall
Munich, Bavaria, Germany
Master Instructor for the NVIDIA Deep Learning Institute, Ph.D.
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Marcin Szeliga
Marcin Szeliga
Katowice, Poland
Machine learning practitioner, Microsoft Most Valuable Professional
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Oleh Misko
Oleh Misko
Oleh Misko
Lviv, Ukraine
Data Scientist at SoftServe
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Catalin Gheorghiu
Catalin Gheorghiu
Catalin Gheorghiu
Timiş, Romania
Solution Architect at EPAM Systems
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Abubakr Karali
Abubakr Karali
Abubakr Karali
Stockholm, Sweden
Senior Solutions Architect at Nvidia, Part-Time PhD, KTH
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Rene Schulte
Rene Schulte
Rene Schulte
Dresden, Germany
Lead of the Spatial Computing Community of Practice at Reply, Metaverse AR VR MR, AI Deep Learning, Quantum Computing
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Michal Marušan
Michal Marušan
Michal Marušan
Prague, Czechia
Senior Cloud Solution Architect for Data and AI at Microsoft
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Harmke Alkemade
Harmke Alkemade
Harmke Alkemade
Amsterdam, North Holland, Netherlands
AI Cloud Solution architect at Microsoft
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Andreas Kopp
Andreas Kopp
Andreas Kopp
Hamburg, Germany
Solution Architect for AI and Data Science
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Schedule

A single registration allows you to participate in all activities except NVIDIA workshops. If you want to attend NVIDIA workshops, please register separately. All dates are in EET (Eastern European Time) timezone.

28 Nov
08:00 - 16:00

Fundamentals of Deep Learning Hands-on workshop

Businesses worldwide are using artificial intelligence to solve their greatest challenges. Healthcare professionals use AI to enable more accurate, faster diagnoses in patients. Retail businesses use it to offer personalized customer shopping experiences. Automakers use it to make personal vehicles, shared mobility, and delivery services safer and more efficient. Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software. In this workshop, you’ll learn how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results.

29 Nov
14:00 - 17:00

MLOps - what it is about and how to get started?

We all know about DevOps. Some of us may hear about GitOps, FinOps, NoOps, etc. Now it is MLOps. It may look like you just need to add Ops at the end of basically anything and boom, you get yourself a new area that you can pioneer. So, is MLOps just a new marketing term for a good old habit of automating your pipelines? Or are there some specifics related to machine learning that will require us to do things differently? Maybe it's something that we are doing already, but we don't know that it is called MLOps? Join me at my session, so we can find those answers together and also check what needs to be done to get started.

30 Nov
19:00 - 20:30

Azure Custom Vision service via Azure Serverless platform

Cognitive services from Microsoft allow you to do quite interesting things, so we will look into the case with the Azure serverless platform with help of the ONNX classification model and Azure Custom Vision service in order to understand what is in the selected image and how we can use our model via application code for back-end processing.

1 Dec
14:00 - 16:00

Accelerating and Scaling Inference with NVIDIA GPUs Hands-on workshop

Learn how to use GPUs to deploy machine learning models to production scale with the Triton Inference Server. At scale machine learning models can interact with up to millions of users in a day. As usage grows, the cost of both money and engineering time can prevent models from reaching their full potential. It’s these types of challenges that inspired creation of Machine Learning Operations (MLOps). Practice Machine Learning Operations by: Deploying neural networks from a variety of frameworks onto a live Triton Server Measuring GPU usage and other metrics with Prometheus Sending asynchronous requests to maximize throughput Upon completion, learners will be able to deploy their own machine learning models on a GPU server.

2 Dec
19:00 - 20:30

Data and AI Certifications from Microsoft: what they are and how to earn one

You will learn about new Microsoft Learn portal, how Microsoft certification program works, AI certifications (Azure AI Engineer, Azure Data Scientist, and Azure Data Engineer), and how to prepare for becoming a certified specialist.

6 Dec
10:00 - 10:15

Welcome


6 Dec
10:15 - 10:45

Azure vs AWS vs GCP AI

In this presentation, awarded Google Developer Expert and Microsoft Most Valuable Professional, compares some of the AI offerings from the major cloud providers. Results are quite surprising. You will learn how to evaluate AI technologies and how to use the best combination of solutions to make the best products. You will learn how to design a great AI based solution.

6 Dec
10:45 - 11:30

End-to-end Edge AI life-cycle management with NVIDIA Metropolis

Edge computing comes with many advantages for AI applications, but it also means a lot of challenges for management, maintenance and optimization of these applications. We'll look at the end-to-end AI life cycle of developing AI applications, deploying them at the edge, sending data and communicating insights for model improvement. We'll show how to leverage Metropolis components; TAO, DeepStream and Fleet Command, to manage the life cycle of AI applications from training deployment at scale.

6 Dec
11:30 - 12:15

Using Databricks jobs to produce near real-time coal mining logistics optimization

In our project we effectively combined Databricks jobs, PySpark and Azure Datalake to perform real-time optimisation of the logistics process on the mining site. The input data comes from a majority of different sources (geolocation, video stream, sensor data) in all kinds of formats. We have implemented an end-to-end solution that takes care about data processing, combination, prediction and saving. Since there is a time lag between data arrival on Datalake and the actual observation time we leveraged the usage of Recurrent Neural Networks, specifically Long-Short Term-Memory (LSTM) Network to accord for this time lag and predict potential sensor values based on a history of observations with low error level.

6 Dec
12:15 - 13:00

Microsoft AI vision: Multimodal and Multilingual foundational models, OpenAI GPT-3, Copilot

Trained on billions of lines of code, GitHub Copilot turns natural language prompts into coding suggestions across dozens of languages. Azure OpenAI Service offers industry-leading coding and language AI models that you can fine-tune to your specific needs for a variety of use cases. Both services are based on OpenAI GPT-3 model and its improvements. In this presentation, you will learn about Microsoft's AI vision.

6 Dec
13:00 - 13:45

Azure Percept (DK) is dead, Long live Azure Percept!

If you think that AI on the Edge is something closer to SF than to production, this session will seed doubt in this perception. The goal of this presentation is a deeper dive on what is Azure Percept as a concept. You will understand what the vision of Edge from Microsoft is. And will look a bit more on where Azure Percept seems to be going after the future demise of Azure Percept DK. But mostly we will see what the ways are to use it in our applications, what are the architectures, what other components we need. All with a focus on practical and usable as in right now.

6 Dec
13:45 - 14:30

Industrial IoT/Industry 4 .0 or where to use the connected objects of where to pass via modern data platform tools

Industry 4.0 became a new buzzword in the world of Manufacturing. All factories are experimenting of connectivity and reporting for their industrial equipment. From technical perspective it means that you need to imagine the communication mechanisms for plc, devices, and sensors installed in the factories. But today, there are a lot of options available. During the talk, we will pass through all of them and try to determine which option is good to use in each case. All discussion will be based on experience feedback from project on connectivity of 3000 devices from all around the world.

6 Dec
14:30 - 15:15

Data Science lifecycle by an example detecting scaphoid fractures with Azure AutoML

The Team Data Science Process is an agile, iterative data science methodology to deliver AI solutions efficiently. In this session, I will show how TDSP was applied to scaphoid fracture detection. In this pro bono project, automated ML was combined with transfer learning to boost data scientist productivity. Join us to learn about the new Azure Machine Learning’s AutoML capabilities in computer vision through a practical example.

6 Dec
15:15 - 16:00

Federated Learning with Azure Machine Learning

A critical success factor for generalizable deep learning is the availability of extensive and heterogeneous training data. A reliable cancer detection model should be trained based on thousands of medical images showing healthy cases and tumors in contrast. This should also represent the real-world range of gender, age, and other demographic properties of patients. The required variety of representation simply cannot be covered by a single institution. Consequently, a collaborative ML development in which many diverse hospitals contribute their own data is obvious. Unfortunately, this often fails in practice due to data privacy or intellectual property protection reasons Federated Learning addresses this issue by allowing multiple parties to collaboratively train a machine learning model without the need of sharing data. Join our session to learn about Federated Learning with AzureML and NVIDIA FLARE. Experience a global Federated Learning setting in action. See how you can create it yourself in less than 1 hour.



6 Dec
16:00 - 16:45

The future of computing with the Metaverse, Spatial Computing and Quantum Computing

The next wave of digital disruption is Spatial Computing, an emerging technology that will change how we interact with computers and the physical world, using intelligent edge devices like a HoloLens and other mobile devices. Mainly driven by AI computer vision and advanced sensors, those devices can spatially sense the world around us and make the spatially aware context part of the experience. With technologies like the AR Cloud, a common spatial map can be shared between heterogeneous devices to build connected, collaborative, and cross-platform Metaverse apps where virtual objects anchored in the physical world can be shared across devices and over time with persistence. During the Spatial Computing era we will also enter the Quantum Computing age. Quantum Computers are still in its infancy with limited Qubits, but Q-Day is coming, the day when Quantum Advantage will be readily available and deprecating today’s cryptography. Even with the hardware infancy, Quantum-inspired computing (QIC) already provides significant advantages for things like Quantum Machine Learning (QML) and Quantum-Inspired Optimization (QIO). In this session Rene will explain the concepts and technologies supporting these transformative computing paradigm changes and will show applied and practical use cases to make these abstracts concepts more feasible.

6 Dec
16:45 - 17:30

Azure Speech Services

Azure Speech Service is one of the top growing AI services which enables wide variety of scenarios from speech transcription or speech synthesis with your own brand neural voices to complex business solutions as Call Center support and Voice assistants. In this session we will introduce capabilities, go through some real customer scenarios and challenges customers were facing and how those were addressed seasoned with demos.

6 Dec
17:30 - 18:15

Azure Cognitive Services for Face recognition

During my session we will take a look at set of online services for face recognition. In no more than 60 minutes we will assemble a prototype of smart lock which will open on close the door based on who is in front of it.

Registration

Registration is closed. See you in 2023!

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