Watch the Top ODSC Europe 2023 Virtual Sessions Here
We had a blast at ODSC Europe and hope all of you who joined us in-person or virtually did as well! For those of you who weren’t able to attend, we wanted to share a few highlights from the virtual conference. Below you’ll find just a few of the many expert-led sessions at ODSC Europe 2023 that attendees loved — and you can view them for yourself here!
AI and Bias: How to Detect It and How to Prevent It
Sandra Wachter, PhD | Professor, Technology and Regulation | Oxford Internet Institute, University of Oxford
In recognition of the extensive biases and inequality that are present in training data, there has been much work done to test for bias in machine learning and AI systems. This session addresses the compatibility of technical fairness metrics and tests used in machine learning against the aims and purpose of EU non-discrimination law and provides recommendations, including a user-friendly checklist for choosing the most appropriate fairness metric for uses of machine learning under EU non-discrimination law.
Probabilistic Machine Learning for Finance and Investing
Deepak Kanungo | Founder and CEO, Advisory Board Member | Hedged Capital LLC, AIKON
This session will introduce you to the reasons why probabilistic machine learning is the next generation of AI in finance and investing. You’ll cover
- Why standard ML systems are inherently unreliable and dangerous in finance and investing
- The three types of errors in all financial models and why they are endemic
- The paramount importance of quantifying the uncertainty of model inputs and outputs
- The three types of uncertainty and different approaches to quantifying them
- Deep flaws in conventional statistics for quantifying uncertainty in financial models
- The probabilistic ML framework and its various components
Why GPU Clusters Don’t Need to Go Brrr? Leverage Compound Sparsity to Achieve the Fastest Inference Performance on CPUs
Damian Bogunowicz | Neural Magic and Konstantin Gulin | Machine Learning Engineer | Neural Magic
This talk will demonstrate the power of compound sparsity for model compression and inference speedup for NLP and CV domains, with a special focus on the recently popular Large Language Models. The session participants will learn the theory behind compound sparsity, state-of-the-art techniques, and how to apply it in practice using the Neural Magic platform.
Apache Kafka for Real-Time Machine Learning Without a Data Lake
Kai Waehner | Global Field CTO, Author, International Speaker
This talk compares a cloud-native data streaming architecture to traditional batch and big data alternatives and explains benefits like the simplified architecture, the ability to reprocess events in the same order for training different models, and the possibility to build a scalable, mission-critical ML architecture for real-time predictions with less headaches and problems.
Time Series Forecasting for Managers — All Forecasts Are Wrong but Some Are Useful
Tanvir Ahmed Shaikh | Data Strategist (Director) | Genentech, Inc
Time series forecasting remains an under-appreciated technique in data science education, often overshadowed by more popular machine learning methods. This talk will explore a variety of time series forecasting techniques (ETS, ARIMA, SARIMA, VAR models, and machine learning models such as XGBoost, Random Forest, and SVR) and their applications in various business contexts.
Getting Up to Speed on Real-Time Machine Learning
Dillon Bostwick | Senior Solutions Architect | Databricks and Avinash Sooriyarachchi | Senior Enterprise Solutions Architect | Databricks
With the introduction of modern streaming platforms, it is much easier for anyone to build reliable streaming pipelines, regardless of their streaming background. This session will use a fraud detection scenario to teach:
- Three important patterns for real-time model inference
- How to prioritize the most common real-time ML use cases in your business
- How to evaluate streaming tools, and why streaming is valuable at any latency
- Operational concerns like monitoring, drift detection, and feature stores
Deep Learning and Comparisons between Large Language Models
Hossam Amer, PhD | Applied Scientist | Microsoft
Watch this talk to learn more about the deep learning fundamentals underpinning large language models. You’ll also discuss different popular large language models and compare the techniques and accuracy of results among different large language models.
What’s next?
You can check out these talks and more here. And don’t miss the chance to join us for our upcoming free virtual Generative AI Summit on July 20th and ODSC West 2023 in San Francisco (October 31st-November 3rd).
Originally posted on OpenDataScience.com
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