ODSC Europe is still a few months away, coming this June 14th-15th, but we couldn’t be more excited to announce our first group of sessions. Our expert speakers will cover a wide range of topics, tools, and techniques that data scientists of all levels can apply in their work. Check a few of them out below.
The Next Generation of Low-Code Machine Learning
Devvret Rishi | Co-founder and Chief Product Officer | Predibase
In this session, you’ll explore declarative machine learning, a configuration-based modeling interface, which provides more flexibility and simplicity when implementing cutting-edge machine learning. You’ll hear how declarative machine learning has been essential to the speedy adoption at leading institutions such as Apple and Meta, as well as learn about Ludwig, the open-source declarative machine learning framework.
Autoencoders – a Magical Approach to Unsupervised Machine Learning
Oliver Zeigermann | Blue Collar ML Architect | OPEN KNOWLEDGE GmbH
In this workshop, you will see two different ways autoencoders can be used: taking the latent representation that contains the abstract pattern of the inputs and using the reconstruction error to measure how well something fits the learned concept. You will use the same example to explore both approaches utilizing TensorFlow in a Colab notebook.
Botnets Detection at Scale – Lesson Learned from Clustering Billions of Web Attacks into Botnets. Learn about the flow, difficulties, and tools for performing ML clustering at scale
Ori Nakar | Principal Engineer, Threat Research | Imperva
Given that there are billions of daily botnet attacks from millions of different IPs, the most difficult challenge of botnet detection is choosing the most relevant data. In this session, you will explore the flow of Imperva’s botnet detection, including data extraction, feature selection, clustering, validation, and fine-tuning, as well as the organization’s method for measuring the results of unsupervised learning problems using a query engine.
Implementing Generative AI in Organisations: Challenges and Opportunities
Heiko Hotz | Senior Solutions Architect for AI & Machine Learning | AWS
This talk will focus on the challenges and opportunities of implementing generative AI, which saw significant advances in the past year, in organizations. The talk will consist of three parts:
(1) An overview of the latest generative AI models and how they work
(2) Best practices and techniques for training and deploying generative AI models
(3) Ethical considerations in generative AI
You will be able to use the insights gained from this session and apply them immediately in your organization, as well as, train and deploy open-source generative AI models.
ML Governance: A Lean Approach
Ryan Dawson | Principal Data Engineer | Thoughtworks
Meissane Chami | Senior ML Engineer | Thoughtworks
During this session, you’ll discuss the day-to-day realities of ML Governance. Some of the questions you’ll explore include How much documentation is appropriate? Should you have manual sign-offs? If so, when and who should perform them? And, Most importantly, what is the point of all this governance, and how much is too much?
Scaling AI/ML Workloads with Ray
Kai Fricke | Senior Software Engineer | Anyscale Inc.
To solve the challenges that make production machine learning systems difficult to use, the Ray community has built Ray AI Runtime (Ray AIR), an open-source toolkit for building large-scale end-to-end ML applications.
- In this session, you’ll explore the following questions
- Why Ray was built and what it is
- How AIR, built atop Ray, allows you to easily program and scale your machine learning workloads
- AIR’s interoperability and easy integration points with other systems for storage and metadata needs
- AIR’s cutting-edge features for accelerating the machine learning lifecycle such as data preprocessing, last-mile data ingestion, tuning and training, and serving at scale
Avoiding Crisis With Model Explainability
Ed Shee | Head of Developer Relations | Seldon
Join this session to explore the questions: What is explainability? Why is it important? What techniques are there and how do they work?
In this session, you will learn how explainability can help you identify poor model performance or bias, as well as discuss the most commonly used algorithms, how they work, and how to get started using them.
Probabilistic Machine Learning for Finance and Investing
Deepak Kanungo | Founder and CEO at Hedged Capital LLC | Advisory Board Member at AIKON
During this session, you will learn why probabilistic machine learning is the future of AI in finance and investing.
You will discuss a variety of topics, including
- 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
Fast Option Pricing Using Deep Learning Methods
Chakri Cherukuri | Senior Quantitative Researcher | Bloomberg LP
In this talk, you will explore how deep learning can be used to build fast option pricers by utilizing a large set of representative training data and deep neural networks. You will also discuss the interactive plots and tools that help you better understand the learning process in real-time and understand how the models work out-of-sample.
Register for ODSC Europe today
And that’s just the start! We’ll be adding more sessions as we get closer to the conference, so be sure to keep an eye out. And remember to get your pass soon. Our limited-time offer of 70% off any ODSC Europe in-person or virtual pass won’t last forever.