On this episode of WeAreDevelopers Live, let's talk about one of the most trending topics in the tech world: Machine Learning. We’ll kick if this day with something really cool: Automated Driving and how to master one of its main challenges by using Deep Learning algorithms. In the next session, we’ll get some good advice on building Recommendation Systems that assist us in decision-making in several fields like Math, Classification, and Matrix Factorization. After that, let’s dive deep into some data preparation, training, and also deployment challenges and get an overview of the end-to-end process for building large-scale language models. Have you ever wondered how to integrate ML applications without the need to build an entire Platform team? Then the next talk is a perfect match, as we'll get an experience report including (miss-)decisions that will help you to elevate your next projects. And to end this day we'll gain some detailed insights into how AI-enabled wearable technology can help us in healthcare, especially for vulnerable populations. So don't forget to tune in and enjoy the show!
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.
Advanced driver-assistant systems and automated driving functions have to deal with increasing complexity as their application evolves into several areas such as multi-lane highway junctions or urban areas. One of the main challenges is the assessment of interactions between traffic participants. In order to master such complex environments, the usage of deep learning algorithms is promising. In this session I will present how we at ZF use such algorithms in order to anticipate the future behavior of traffic participants and thus, improve comfort and road safety and finally enable advanced automated driving functions.
I studied Mechatronics and Information Technology at the Karlsruhe Institute of Technology and conducted my Master Thesis at the Fraunhofer IOSB. After finishing my studies, I joined the newly founded ZF AI Lab in Saarbrücken where quickly after my path into deep learning research for ADAS / AD began.
We've all heard about AI, ML, Data Classification, etc. All cool!But what about building decision-making assistants AKA Recommendation Systems? Ever heard of them? No? Great! We'll cover the basics of Math, then proceed with a brief overview of Classification, and finally, discuss how we can use Matrix Factorization techniques to build state-of-the-art Rec. Systems! Prerequisite to this presentation is a familiarity with Data Classification Systems. Anything else is not really required as we will introduce it through a series of examples.
Software Architect by choice, Engineering Manager by accident, and REBT practitioner by need.Deeply interested in exploring both the breadth and depth of the subjects and, once explored - enjoys talking about his learnings!
Recent advances in natural language processing demonstrate the capability of large-scale language models (such as GPT-3) to solve a variety of NLP problems with zero shots shifting from supervised fine-tuning to prompt engineering/tuning. However, building large language models raises challenges on data preparation, training, and deployment. In addition, while the process is well-established for a few dominant languages such as English, its execution on localized languages remains limited. We'll give an overview of the end-to-end process for building large-scale language models, discuss the challenges of scaling, and describe some existing solutions for efficient data preparation, distributed training, model optimization, and distributed deployment. We'll use examples on localized languages such as French or Spanish using NVIDIA Nemo Megatron, a framework for training large NLP models optimized for SuperPOD hardware infrastructure.
Miguel Martínez is a senior deep learning data scientist at NVIDIA, where he concentrates on Recommender Systems, NLP and Data Analytics. Previously, he mentored students at Udacity's Artificial Intelligence Nanodegree. He has a strong background in financial services, mainly focused on payments and channels. As a constant and steadfast learner, Miguel is always up for new challenges.
Machine learning models are becoming obsolete and must be retrained - this is the current widespread tenor. After we have taken a closer look at this thesis, we will show how this can be done. Which components does a CI/CD pipeline for Machine Learning really need - and which are optional. How can the whole thing be implemented without building an entire ML Platform team. And which challenges are still difficult to solve.An experience report including (mis-)decisions that will help to take the right path with your own challenges. In addition, it gives an overview of the essential components of a functioning machine learning platform that focuses on solving business challenges through high automation and low vertical integration.
Matthias Niehoff works as Head of Data & AI and Data Architect for codecentric AG and supports customers in the design and implementation of data architectures. His focus is not so much on the ML model, but rather on the necessary infrastructure and organization to help data science projects succeed.
The current standard for monitoring patients’ health conditions and predicting complications is discontinuous and suffers from inter-observer variations, which can lead to over- or under-treatment. Therefore, it is critical to address the shortcomings of the current standard and develop continuous and more standardized tools. In this talk, I will introduce AI-based technologies that can be used to monitor health and predict complications of vulnerable patient populations including infants and minority groups. I will also discuss how these technologies are designed to be lightweight to enable its use in portable and wearable devices. I will also show how the performance of these technologies was comparable to that of trained health professionals. Finally, I will conclude by presenting pressing challenges and several future directions for AI-enabled wearable technology in healthcare.
Ghada is currently working as a research scientist in the National Institutes of Health (NIH). She focuses on using computational sciences and engineering techniques toward advancing healthcare of vulnerable populations (e.g., infants, minority groups). She has published papers in top tier conferences and journals. She served in the program committee of several top conferences, chaired several academic workshops and events in her area of interests. Ghada received different prestigious awards such as NIH Institutional Board of Regents (BoR) Award (2022) and MIT Innovator under 35 (2021).