Get ready for exciting insights and best practices about Machine Learning - the probably most interesting field of tech. From AI over deep learning to computer vision and even using the cloud to be even more productive - we have curated some of the most recent topics for you to become an ML Pro! Interested? Then get yourself comfortable and join us at the Machine Learning Day!
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Save Your SpotTogether with BOSCH we invite you to a full day of learning more about the intersection of mobility and code. Get to know more about how modern mobility is defined by an intricate interplay of hardware and software and how cars are not only connected to the road, but also to the cloud.
Coding the Future of Mobility features a variety of talks and a workshop, that give you valuable insights into the world of mobility - wether you join in-person or online.
Together with Bosch we invite you to a full day of learning more about the intersection of mobility and code. Get to know more about how modern mobility is defined by an intricate interplay of hardware and software and how cars are not only connected to the road, but also to the cloud.
Coding the Future of Mobility features a variety of talks and a workshop, that give you valuable insights into the world of mobility - wether you join in-person or online.
Multimodal generative AI has recently seen significant advancements, enabling the creation of realistic images, videos, and audio from textual or other inputs. However, due to the complexity of these models, understanding how they function and how to apply them in practical settings can be challenging. During this talk, Ekaterina will shed light on the inner workings of multimodal generative AI models by discussing key concepts and techniques used in their development. She will also explore various applications and use cases of this technology. The talk is intended for anyone interested in the current state of AI and its potential to produce realistic and immersive multimedia experiences.
Ekaterina specializes in end-to-end AI productization, encompassing the entire process from development to optimized deployment of discriminative and generative AI solutions. Formerly a research engineer in medical image analysis, she holds a Ph.D. in Computer Science and has authored peer-reviewed publications in image-based 3D reconstruction, localization and tracking.
This session will focus on the application of OpenAI’s powerful language models in developing AI-powered applications. OpenAI embeddings provide a rich representation of natural language that can be used for a wide range of Natural Language Processing (NLP) tasks, including sentiment analysis, named entity recognition, and text classification. The session will begin with an overview of OpenAI embeddings and their capabilities. Next, we will build an end-to-end sample using OpenAI and Azure Search. By the end of the session, attendees will have a comprehensive understanding of how OpenAI embeddings can be used to develop intelligent applications and the benefits of incorporating them into their development workflows.
Entrepreneur, speaker, mentor at CoderDojo Linz, teacher, developer, software architect. Passionate about Rust, .NET, Web Development, AI, Cloud Computing (in particular Azure), running, and mountain biking.
What comes after ChatGPT? Vector database projects like Weaviate, Pinecone, and Chroma recently got millions of dollars of funding for their projects. But what are vector databases? And why will they be so important in the future? Let us see how Vector Databases can help you define and run your machine learning business use cases. We will explore some real-world use cases and try to understand the potential of vectors and vector databases. Not exactly sure what a vector is ? No worries, you will learn everything you need about vectors . A brief hands-on demonstration just using open source will give you an idea, of how to use the new generation of databases in praxis. We will also cover how vector databases can work together with chatGPT and helps you to overcome some limitations of chatGPT.
Erik is a Java and machine learning Enthusiast - Java -Coach, -Expert, -Consultant and Software Architect - who loves to talk about elegant software solutions.He spends his energy, enthusiasm and research time in machine learning and vector databases.Formerly a Open Source Contributor & Community Lead for the Deep Java Learning ML Framework (Amazon AWS), he is on a mission to transform machine learning from the research labs into real-world applications.As a musician and Indy Film Maker, he knows about creativity and art and the importance of these skills as a software engineer.
Generative AI has taken the hard part of AI out of the lifecycle of AI products. There is so much abstraction, that it gets harder to decide what to do first, when the only programming language need is English. What should Product managers of large enterprises have to keep in mind, while evaluating models, and building for themselves Generative AI applications for 100,000 employees How Product managers can go about researching and building the best products for their teams and businesses Which methods worked for AI and Data product building at Google and at Telus.
Julian is an AI Product Manager with over 7+ years of experience in the field. She has worked with esteemed startups, including Fractal Analytics (now a Unicorn), Somnoware Health Systems, Google Cloud, and currently at TELUS, where she serves as a Senior Product Manager of Generative AI Platforms Engineering.