ODSC East Boston 2022 - Top 11 Sessions for AI and ML Professionals to Attend
Alexandra Quinn | April 12, 2022
On April 19, 2022 data scientists, data engineers and AI professionals will gather in Boston and virtually, to attend ODSC East. Over the course of three days, from April 19 to April 21, expert speakers will discuss deep learning, ML, MLOps, data visualization, responsible AI, and more. This year, many of the talks focus on governance and security, as well as various interesting use cases.
Since there are so many great sessions and it’s so hard to choose which ones to attend, we’ve put together our recommendations. We will also be there - more details below.
Here are our top 11 recommended sessions for ODSC East:
1. Adversarial Robustness: How to Make Artificial Intelligence Models Attack-proof!
Serg Masís, Climate Data Scientist, Syngenta
April 19, 2PM - 4PM
A security-focused session about evasion attacks, which occur when perpetrators trick classifiers to make false predictions. The session will examine an evasion attack use case and explain two defense methods for ML models. Then there will be a demonstration of a robustness evaluation method and a certification method, to assure the ML model will resist evasion attacks.
2. ODSC Keynote - The Big Wave of AI at Scale
Luis Vargas, Partner Technical Advisor, Microsoft
April 19, 9 AM - 9:40 AM
In this session, the Partner Technical Advisor to the CTO of Microsoft discusses the trend of larger AI models that enable new tasks in language, vision, and multi-modality. He will provide an overview of the research and engineering efforts that comply with the trend and its implication on Microsoft and other companies.
3. Self-supervised Representation Learning for Speech Processing
Abdel-rahman Mohamed, PhD, Research Scientist, Facebook AI Research
April 21, 3:10 PM - 4:10 PM
A talk that introduces the promising self-supervised speech representation learning approaches. These methods are expected to deliver a single universal model to benefit a collection of tasks and domains, as a means to avoid the bottlenecks of deep learning and deep neural models. The talk will include a review of recent benchmarking efforts on learned representations and discuss how representations can be extended to related research areas beyond speech recognition.
4. ODSC Keynote – Is Your ML Secure? Cybersecurity and Threats in the ML World
Dr Hari Bhaskar, PhD, Director - Data Science & AI Platform, Oracle and Jean-Rene Gauthier, PhD, AI Platform Architect, Oracle
April 19, 9:50 AM-10:30 AM
Join this session to learn how to mitigate your ML models’ security risks. The talk will cover the most common types of attacks targeting the integrity, availability, and confidentiality of machine learning models. Then, it will cover best practices for mitigating these risks. The session will end with a Q&A from the audience.
5. AI for Clinical Care Planning and Decision Support
Sadid Hasan, PhD, Executive Director of Artificial Intelligence, CVS Health
April 20, 2:50 PM - 3:20 PM
When planning clinical care and understanding patient health status, clinicians have massive volumes of free text documents to contend with. This session discusses the opportunities and potential of AI-based clinical analytics, leading to better patient outcomes. Hasan will share various real life AI systems developed as part of CVS Health’s advanced care planning initiatives.
6. Introduction to Interpretability in Machine Learning
Andras Zsom, Assistant Professor of the Practice of Data Science and Director of Industry and Research Engagement, Brown University
April 19, 3:30 PM - 4:30 PM
Understanding why a model makes a certain prediction is becoming a hot issue, affecting both research and development stages. When models have significant impact, explanations specific to each data point are a must. This workshop reviews a few methods and tools to calculate explanations, finishes with the Shapley Additive Explanations technique, and includes a github repo.
7. Drift Detection in Structured and Unstructured Data
Keegan Hines, PhD, VP of ML, Adjunct Professor, Chair, ArthurAI, Georgetown, CAMLIS
April 21, 12:40 PM - 1:20 PM
A presentation that covers how to reliably quantify data drift in a variety of different data paradigms. These include Tabular data, Computer Vision data, and NLP data. Attendees of this talk will learn how to think about data stability monitoring in their own models.
8. Feature Engineering on the Modern Data Stack
Andrew Engel, PhD, Chief Data Scientist, Rasgo
April 21, 11 AM - 11:30 AM
Data science is not just science, but also an art: this session shows how to aggregate time series data and calculate moving averages in pandas, directly on a data warehouse. The sessions will use SQL and Rasgo to calculate and publish those features on Snowflake.
9. Evaluating, Interpreting and Monitoring Machine Learning Models
Ankur Taly, PhD, Staff Research Scientist, Google
April 21, 3:25 PM - 4:10 PM
A discussion about the importance of understanding model predictions. This session will answer the question: “Why did the model make this prediction?” by explaining how to attribute predictions to input features with the Integrated Gradients (ICML 2017) method, discussing an evaluation workflow based on feature attributions and talking about how attributions can be used for monitoring models in production.
10. “It worked on my laptop, now what?” Using OS Tool MLRun to Automate the Path to Production
Marcelo Litovsky, Director of Sales Engineering, Iguazio
April 21, 11:40 PM - 12:10 PM
An in-depth intro and explanation of open source MLRun, the MLOps orchestration framework built and maintained by Iguazio. The session by our own Marcelo Litovsky will teach attendees how to get started in 10 minutes, about running local moves in Kubernetes, how Python code can run as a Kubernetes job with no code changes, about tracking experiments, and more.
11. How Enterprises use MLOps Automation to Continuously Roll out New AI Services
Bill Bodei, VP of Sales, Iguazio
April 21, 11:50 PM - 12:30 PM
Our very own Bill Bodei will deep dive into three enterprise case studies where organizations have built automated machine / deep learning pipelines and generated real business value from AI. These include a global payments company for fraud prediction, retail for real-time recommendations and scaling NLP pipelines to make thousands of PDFs searchable and indexable.
Meet Iguazio at OSDC East!
Will we see you at these great sessions? We certainly hope so, including the ones where we’re presenting. We’ll also be at our booth to discuss, provide information or answer any questions you might have. To continue the discussion after these sessions - join us on the MLOps live community on Slack to have meaningful conversations with members of the community and learn more.
See you soon at ODSC East!
