Discover 5 Exciting AI/ML Events to Attend in April
Hey there!
I’m excited to let you know about some really cool events happening in April that you might find useful and interesting.
Making AI Easy with YOLO v8
- Date: April 3, 16.00 CET
- Speaker: Glenn Jocher, founder & CEO of Ultralytics
- Organizer: Data Phoenix
- Registration: https://www.eventbrite.com/e/webinar-making-ai-easy-with-yolov8-tickets-578331333587
After two years of continuous research and development, we are thrilled to introduce Ultralytics YOLOv8. Building on the success of countless experiments and previous architectures, we’ve created models that are best in the world at what they do: real-time object detection, classification, and segmentation. Faster, more accurate, and simpler, YOLOv8 places the power of AI in the hands of everyone. In this webinar, we will discuss what Ultralytics YOLOv8 is, its architecture and training process, as well as the ways of using the Ultralytics YOLOv8 in real-world scenarios.
Introduction to Graph Neural Networks
- Date: April 6, 16.00 CET
- Speaker: Ekaterina Sirazitdinova, Senior Data Scientist at NVIDIA
- Organizer: Data Phoenix
- Registration: https://www.eventbrite.com/e/webinar-introduction-to-graph-neural-networks-tickets-578344823937
Graph Neural Networks (GNNs) are AI models designed to derive insights from unstructured data described by graphs. For different segments and industries, GNNs find suitable applications such as molecular analysis, drug discovery, prediction of developments in stock market, thermodynamics analysis, and even modeling of human brain. Unlike conventional CNNs, GNNs address the challenge of working with data in irregular domains. In this talk, I will provide an introductory overview of the theory behind GNNs, take a closer look at the types of problems that GNNs are well suited for, and discuss several approaches to model unstructured problems as classification or regression at various levels.
Intro to AI Observability: Monitoring ML Models & Data in Production
- Date: April 12, 4 pm PDT
- Speaker: Sage Elliott, Technical Evangelist at WhyLabs
- Organizer: WhyLabs
- Registration: https://www.eventbrite.com/e/intro-to-ai-observability-monitoring-ml-models-data-in-production-tickets-574953269707
Join this hands-on workshop to get started with data and machine learning model monitoring.
If you want to build reliable pipelines, trustworthy data, and responsible ML models, you need to validate and monitor your data.
In this workshop we’ll cover how to use validation and monitoring techniques to implement your own AI observability solution from start to finish. Once completed you’ll also receive a certificate!
This workshop will cover:
- Data quality validation
- Detecting data drift
- Measuring model drift
- Monitoring model performance
LLMs in Production Conference
- Date: April 13, 8:00 AM, PDT
- Organizer: MLOps Community
- Registration: https://home.mlops.community/public/events/llms-in-production-conference-2023-04-13
Large Language Models have taken the world by storm. But what are the real use cases? What are the challenges in productionizing them?
In this event, you will hear from practitioners about how they are dealing with things such as cost optimization, latency requirements, trust of output and debugging.
You will also get the opportunity to join workshops that will teach you how to set up your use cases and skip over all the headaches.
Effective Domain-Driven Design for Machine Learning Products
- Date: April 19, 4:30 PM CET
- Speaker: Larysa Visengeriyeva, Senior Consultant at INNOQ
- Organizer: DataTalks.Club
- Registration: https://www.eventbrite.de/e/effective-domain-driven-design-for-machine-learning-products-tickets-575102435867
In this hands-on workshop, we’ll learn how to find and verify AI/ML use cases and get the right tools and procedures for product finalization. We use effective DDD methodology (no previous DDD knowledge necessary)
In this workshop, we’ll cover the following:
- Find out which problems and use cases are suitable for ML
- Define and prioritize problems and opportunities for ML in business areas and projects
- Learn how to use the Data Landscape Canvas
- Learn the knowledge crunching method of event storming used in DDD in a case study and apply it yourself
- Learn how to use the Machine Learning Canvas to structure ML projects
By the end of this workshop, you’ll be able to apply Domain-Driven Design methodology to effectively structure machine learning projects.
Thank you for taking the time to read my post! I’m really excited about these events, and I hope they catch your interest too. I’d love to see you there if you decide to attend.
Also, if you’re interested in staying up-to-date with the latest trends and developments in the world of Data & AI, you might want to consider subscribing to my digest. I write about upcoming events and provide a summary of the top research papers, articles, and news in this exciting field. It’s a great way to stay informed and keep growing your knowledge!
