ODSC West 2022

 Schedule

more sessions added weekly

REGISTER NOW

ODSC West 2022

 Schedule

more sessions added weekly

REGISTER NOW
Please Note: In-Person attendees will have access to virtual sessions. If you have a virtual pass, please note that we will not live-stream any in-person sessions. Only virtual sessions will be recorded.  The schedule overview is available HERE.

50+ Sessions Added

100+ more coming soon.

Bootcamp/Pre-Bootcamp

Pre-Botocamp: Introduction to Data Course

Pre-Bootcamp: Introduction to Programming with Python Course

Pre-Bootcamp: Data Wrangling with Python Course

Pre-Bootcamp: Introduction to SQL Course

Pre-Bootcamp: Introduction to AI Course

All sessions are scheduled in PST time zone (Pacific Time)
 
Please Note: In-Person attendees will have access to virtual sessions. If you have a virtual pass, please note that we will not live-stream any in-person sessions. Only virtual sessions will be recorded.  The schedule overview is available HERE.

 

The most up-to-date schedule you can check:

– for in-person:  Download the ODSC EventX Ai app (the login details will be shared in email/calendar invite)

– for virtual: live.odsc.com (agenda section)

150+ Sessions Added

West Keynotes / Talks / AI X Talks
---Tuesday, 31st October
--Wednesday, 1st November
-Thursday, 2nd November
West Trainings / Workshops
----Monday, 30th October
---Tuesday, 31st October
--Wednesday, 1st November
-Thursday, 2nd November
West Bootcamp
----Monday, 30th October
--Pre-Bootcamp live training warm up
---Tuesday, 31st October
--Wednesday, 1st November
-Thursday, 2nd November
----Monday, 30th October
---Tuesday, 31st October
--Wednesday, 1st November
-Thursday, 2nd November
----Monday, 30th October
--Pre-Bootcamp live training warm up
09:00 - 09:30
ODSC Keynote- Session Title by Jepson Taylor Coming Soon!

In-Person | Keynote

09:00 - 09:40
ODSC Keynote – Neural Networks Make Stuff up. What Should We do About it?

In-person | Keynote | Machine Learning | All Levels

 

In this talk, I’ll discuss some ways that we might cope with and address the unreliability of neural network models. As an initial coping strategy, I will first discuss a technique for detecting whether some content was generated by a machine learning model, leveraging the probability distribution that the model assigns to different content. Next, I will describe an approach for enabling neural network models to better estimate what they don’t know, such that they can refrain from making predictions on such inputs (i.e. selective classification). I will lastly describe methods for adapting models with small amounts of data to improve their accuracy under distribution shift…more details

ODSC Keynote – Neural Networks Make Stuff up. What Should We do About it? image
Chelsea Finn
Assistant Professor | Stanford University
09:00 - 09:40
ODSC Keynote – Human Centered AI

In-person | Keynote | Machine Learning | All Levels

 

We have seen amazing technical progress in AI applications in recent years. This talk considered the human side rather than the technical side: how can we gain confidence that our applications will be fair, just, truthful, beneficial, and well-stirred for their users, the other stakeholders, and society at large…more details

ODSC Keynote – Human Centered AI image
Peter Norvig
Engineering Director | Education Fellow | Google | Stanford Institute for Human-Centered Artificial Intelligence (HAI)
09:30 - 10:00
How to Build a Domain-Aware Assistant with LLM Agents: Combining Reasoning and Knowledge with RAG

In-Person | AI X Talk

 

Generative models like GPT-4, Claude, and PaLM have high levels of coherence and amazing reasoning capabilities, but they model knowledge “implicitly,” making them vulnerable to breaking down in out-of-domain settings. To build a conversational assistant / chatbot that is aware of new, domain-specific knowledge, we will explain the necessity of Retrieval Augmented Generation (RAG) and the role of domain-adapted hybrid semantic search. We will also explain how RAG approaches also protect against the privacy and sensitivity implications of fine-tuning generative models, by applying permissions at the appropriate input layer. We will also overview how LLM Agents and a Tool framework build off of RAG to increase the space of solvable workflows and intents.

How to Build a Domain-Aware Assistant with LLM Agents: Combining Reasoning and Knowledge with RAG image
Eddie Zhou
Head of AI and ML | Glean AI
09:45 - 10:30
Protecting Sensitive Data Throughout the ML Pipeline using Confidential Computing

In-person | Track Keynote | AI Safety/Security | Beginner-Intermediate

 

In this talk, I will describe the powerful engine of confidential computing, which promises to address these problems through a combination of hardware advances and cryptographic techniques. I will discuss our research from UC Berkeley as well as its tech transfer to industry. The concept is a powerful one: the servers can compute on data without seeing it. The attendees will understand what confidential computing is, what problems it can solve in their sensitive data lifecycle, and which tools they can use today for this purpose…more details

Protecting Sensitive Data Throughout the ML Pipeline using Confidential Computing image
Raluca Ada Popa, PhD
Computer Security Professor | Co-Founder | UC Berkeley | Opaque Systems
10:00 - 10:30
Generative AI in Enterprises: Unleashing Potential and Navigating Challenges

In-person | Ai X Talk | Generative AI

 

In this session you will learn about some early experiences, and best practices of deploying Generative AI solutions in enterprises…more details

Generative AI in Enterprises: Unleashing Potential and Navigating Challenges image
Rama Akkiraju
VP AI/ML for IT | NVIDIA
10:00 - 10:30
Accelerate your AI/ML Initiatives and Deliver Business Value Quickly by Implementing Practical and Innovative Approaches

In-person | Ai X Talk | Intermediate

 

Critical success factor for enterprise level AI adoption and sponsorship is the ability to rapidly deploy AI technologies, solve use cases quickly, and deliver iterative business value to stakeholders. In this session, we will discuss the challenges of AI adoption, share some pragmatic approaches that enterprises can adopt to accelerate their AI / ML initiatives and deliver quick and iterative business value. Examples of some approaches would include leveraging pre-integrated AI frameworks and automation techniques such as AutoML, along with ready-to-use industry-specific AI solutions. The faster and more incremental the delivery of business value through AI, the higher the likelihood of successful adoption and implementation of AI within enterprises…more details

Accelerate your AI/ML Initiatives and Deliver Business Value Quickly by Implementing Practical and Innovative Approaches image
Durga Kota
Chief Technology | Officer Fujitsu North America, Inc.
10:30 - 11:00
Graph Algorithms

In-Person | Book Author

 

 

Get your copy of Amy Hodler‘s book signed at our book-author event! We will have a limited number of copies available for free.

Graph Algorithms image
Amy Hodler
Founder, Consultant | GraphGeeks.org
10:30 - 11:00
Hands-On Data Analysis with Pandas – Second Edition: A Python Data Science Handbook for Data Collection, Wrangling, Analysis, and Visualization

In-Person | Book Author

 

 

Get your copy of Stefanie Molins’s book signed at our book-author event! We will have a limited number of copies available for free.

Hands-On Data Analysis with Pandas – Second Edition: A Python Data Science Handbook for Data Collection, Wrangling, Analysis, and Visualization image
Stefanie Molin
Software Engineer, Data Scientist, Chief Information Security Office, Author of Hands-On Data Analysis with Pandas | Bloomberg
10:30 - 11:00
Effective Pandas : Patterns for Data Manipulation

In-Person | Book Author

 

Get your copy of Matt Harrison’s book signed at our book-author event! We will have a limited number of copies available for free.

Effective Pandas : Patterns for Data Manipulation image
Matt Harrison
Python & Data Science Corporate Trainer | Consultant | MetaSnake
10:40 - 11:10
Representation Learning on Graphs and Networks

Virtual | Talk | Machine Learning | Intermediate

 

In this talk, I will attempt to provide several “bird’s eye” views on GNNs. Following a quick motivation on the utility of graph representation learning, I will derive GNNs from first principles of permutation invariance and equivariance. We will discuss how we can build GNNs that are not strictly reliant on the input graph structure…more details

Representation Learning on Graphs and Networks image
Dr. Petar Veličković
Staff Research Scientist | Affiliated Lecturer | DeepMind | University of Cambridge
10:40 - 11:10
A Semi-Supervised Anomaly Detection System Through Ensemble Stacking Algorithm

Virtual | Talk | Machine Learning | Intermediate

 

Many retail giants are experiencing huge inventory loss and shrinkage problems because they process a huge number of transaction activities every day across the U.S. and offer a liberal shopping policy to provide a convenient customer shopping and return experience. Due to the facts that 1) they have highly imbalance and complex transaction data set as there are enormous transaction data while different types of anomalies are exceedingly rare; 2) there are seldom predefined labels available as it is not feasible to have human experts manually review every transaction and identify anomalies, it is a challenging task to investigate customers’ return behaviors and prevent fraudulent activities…more details

A Semi-Supervised Anomaly Detection System Through Ensemble Stacking Algorithm image
Chuying Ma
Senior Data Scientist | Walmart
10:40 - 11:10
Towards Explainable and Language-Agnostic LLMs

Virtual | Talk | NLP & LLMs | Intermediate

 

Large language models (LLMs) have achieved a milestone that undeniably changed many held beliefs in artificial intelligence (AI). However, there re-mains many limitations of these LLMs when it comes to true language un-derstanding, limitations that are a byproduct of the underlying architecture of deep neural networks. Moreover, and due to their subsymbolic nature, whatever knowledge these models acquire about how language works will always be buried in billions of microfeatures (weights), none of which is meaningful on its own, making such models hopelessly unexplainable…more details

Towards Explainable and Language-Agnostic LLMs image
Walid S. Saba
Senior Research Scientist | Institute for Experiential AI at Northeastern University
10:40 - 11:10
Connecting Large Language Models – Common Pitfalls & Challenges

Virtual | Talk | NLP & LLMs | Beginner

 

 

Generative models have demonstrated how helpful they can be on general knowledge, helping students on their writing assignments. But as soon as you want to run it in a professional setting with prompts like “what are the three main feature requests from our largest customers?”, they demonstrate their lack of knowledge. In this session, I will introduce how Large Language Models (LLMs) can be connected to your data via semantic search. As I will present, there are many pitfalls and challenges. Some can be solved, when using the right technologies, others are still open problems…more details

Connecting Large Language Models – Common Pitfalls & Challenges image
Nils Reimers
Director of Machine Learning | cohere.ai
11:00 - 11:45
Understanding the Landscape of Large Models

In-person | Talk | NLP & LLMs | GenAI | Intermediate

 

There seems to be a new large ML model grabbing headlines every week. Whether it’s OpenAI’s big releases like GPT-3, Dalle-2 or Whisper, or one of the many open source projects generating state-of-the-art models, like Stable Diffusion, OpenFold or Craiyon, these models have found their way into the mainstream. Lukas will map the landscape for you and share how these teams use W&B to accelerate their work…more details

Understanding the Landscape of Large Models image
Lukas Biewald
Founder | Weights & Biases
11:00 - 11:45
AI and Video Games : The Evolution

In-person | Talk | Beginner

 

 

Little is known about the history of Neural Networks. For instance, “”Artificial Intelligence.”” The history goes back to around 1946 when researchers noticed that the mathematics involved with linear algebra wherein materials were stretched that the neighboring atoms were affected, was best modeled with a branch of Math known as tensors. Tensors are used today to create neural networks. Neural networks are run on a powerful Graphics Processing Unit (GPU.) GPUs came about because of video games and entertainment. Thus we can say that video games laid the groundwork for AI…more details

AI and Video Games : The Evolution image
Jack McCauley
Board Trustee at University of California, Berkeley, Former co-founder and Engineer, Oculus VR. Faculty Member Jacobs Institute, McCauley Chair in Drug Policy Innovation at RAND Corporation, MSRI Trustee | Black Lab LLC
11:00 - 11:45
From Nodes to Natural Language: Grounding LLMs with Graphs & Graph Data Science

In-Person | Talk | All Levels

From Nodes to Natural Language: Grounding LLMs with Graphs & Graph Data Science image
Alison Cossette
GDS Developer Advocate - Data Scientist | Neo4j, Inc.
11:00 - 11:45
Beyond the Buzz: Decoding Popularity Bias in Large-Scale Recommender Systems

In-person | Talk | Deep Learning | Machine Learning | Responsible AI | Intermediate – Advanced

 

In this talk, we’ll delve into the intricate landscape of popularity bias in billion-scale recommender systems and review existing approaches to detect, quantify and mitigate it. We will also identify some of the open challenges in this area and offer a glimpse into the exciting future directions that are currently being explored…more details

Beyond the Buzz: Decoding Popularity Bias in Large-Scale Recommender Systems image
Amey Porobo Dharwadker
Engineering Manager, Machine Learning | Meta
11:00 - 11:45
Completing Knowledge Discovery Fast at High Quality with AI

In-person | Talk | Machine Learning | All Levels

 

In this presentation, our speaker will commence by conducting a comprehensive review of a list of common failure factors. Additionally, they will present an AI-driven ecosystem approach for knowledge discovery and discuss a real-world test of this approach involving 14 knowledge discovery projects. Through this, attendees will gain valuable insights into the crucial connection between the success of data-driven knowledge discovery projects and advancements in AI technology. Participants will also grasp how this ecosystem approach can yield remarkable gains in speed, enhanced quality, and effective risk mitigation for knowledge discovery initiatives. Furthermore, the session will highlight additional advantages brought about by the implementation of AI, such as the utilization of diverse data forms, the application of various algorithmic approaches, and the improvement of communication among project stakeholders. To conclude the presentation, the speaker will review the history of AI development. Subsequently, they will engage in a discussion of insights gleaned from this historical review and explore future trends in the field. The session will conclude with a question-and-answer segment open to all participants.more details

Completing Knowledge Discovery Fast at High Quality with AI image
Alex Liu, Ph.D.
Founder and Director | RMDS Lab
11:00 - 11:45
Implementing Gen AI in Practice

In-person | Track Keynote | Generative AI | All Levels

 

 

Generative AI has taken over the world by storm, but building Gen AI applications for production comes with a unique set of challenges. Questions around cost performance, risk, simplifying implementation for production, setting guardrails, adding automation where possible and leveraging CI/CD for ML all become even more important when Gen AI is involved…more details

Implementing Gen AI in Practice image
Yaron Haviv
Co-Founder and CTO | Iguazio (acquired by McKinsey & Company)
11:00 - 12:00
The Power and Promise of Synthetic Data

In-Person | AI X Panel

 

 

Join us for an engaging discussion with industry leaders as we delve into the dynamic realm of synthetic data. Our distinguished panelists will explore the intricacies of generating synthetic datasets and the profound impact synthetic data is having on machine learning, privacy, and innovation. Discover how synthetic data is revolutionizing AI research and applications, and gain insights into its potential to drive progress in diverse fields, from healthcare to autonomous vehicles.

The Power and Promise of Synthetic Data image
Alex Watson
Co-Founder | Gretel AI
11:00 - 11:45
Troubleshooting Large Language Models in Production with Embeddings and Evals

In-person | Talk | MLOps | Data Engineering & Big Data | Machine Learning | Deep Learning | Intermediate

 

In this presentation, Amber Roberts, Machine Learning Engineer at Arize AI, will present findings from research on ways to measure vector/embedding drift for image and language models. With lessons learned from testing different approaches (including Euclidean and Cosine distance) across billions of streams and use cases, Roberts will dive into how to detect whether two unstructured language datasets are different — and, if so, how to understand that difference using techniques such as UMAP…more details

Troubleshooting Large Language Models in Production with Embeddings and Evals image
Amber Roberts
Data Scientist, Growth Lead | Arize AI
11:00 - 11:45
Building a Data-Driven Workforce

In-person | Talk | Data Visualization & Data Analysis | All Levels

 

Save time and money by equipping everyone with fundamental data literacy and analytics skills. It sounds great, but most organizations bite off more than they can chew. Instead of trying to turn everyone into a data analyst, teach people the most vital descriptive analytics skills, and eliminate the flow of tedious work requests to your data experts…more details

Building a Data-Driven Workforce image
Dominic Bohan
Co-Founder | StoryIQ
11:20 - 11:50
Causality and LLMs

Virtual | Talk | Machine Learning | LLMs | Intermediate

 

A highlight of this workshop is the introduction to the “causal LLM”, a pioneering concept where LLMs are designed using foundational causal principles. By the end of this session, participants will be equipped with the skills and knowledge to employ LLMs effectively in discerning complex causal models and pioneering advancements in the field of causal AI…more details

Causality and LLMs image
Robert Osazuwa Ness, PhD
Senior Researcher | Microsoft
11:20 - 11:50
Attribution and Moral Rights in Generative AI

Virtual | Talk | Generative AI | All Levels

 

 

Human creators have provided the works (whether prose writing, source code, or visual works) that act as the basis for training huge DNNs, arguably create an obligation for the AI outputs. This feels most evident when promps ask AIs to create something “in the style of such-and-such-human.” While such outputs are often flawed in interesting ways, they are also usually recognizable in their connection to the prompted human creator. What rights should those source humans have to control those uses, including the moral right simply to be formally recognized as the source? Few laws exist governing attribution and moral rights in generative AI, but many will come to exist soon. Laws and technical standards may follow good or bad principles, both ethical and technical…more details

Attribution and Moral Rights in Generative AI image
David Mertz, Ph.D.
Director of Epistemology | KDM Training
11:20 - 11:50
Building Robust and Scalable Recommendation Engines for Online Food Delivery

Virtual |Talk | Machine Learning | Beginner – Intermediate

 

In this training session, we will delve into the intricacies of building robust and scalable recommendation engines specifically tailored for online food delivery services. We will introduce a newly released dataset called the Delivery Hero Recommendation Dataset (DHRH) and understand how this can be used for training different recommendation models.We will explore the challenges faced in this domain and discuss the techniques and best practices to overcome them, ensuring our recommendation systems can handle large-scale operations and adapt to changing customer preferences…more details 

Building Robust and Scalable Recommendation Engines for Online Food Delivery image
Raghav Bali
Staff Data Scientist | Delivery Hero
Building Robust and Scalable Recommendation Engines for Online Food Delivery image
Vishal Natani
Manager, Data Science | Delivery Hero
12:00 - 12:45
How to Deliver Contextually Accurate LLMs

In-person | Talk | LLMs | Machine Learning | Intermediate

 

 

In the realm of advanced computational linguistics, the efficacy of Large Language Models (LLMs) is intrinsically tied to their contextual precision. In this session presented by Jake from Cloudera (not State Farm), we’ll navigate the complexities of ensuring LLMs yield contextually accurate results, a necessity in today’s intricate data environments. Crucially, attendees will be treated to a live demonstration showcasing the utilization of RAG (Retrieval-Augmented Generation) and PEFT (Parameter Efficient Fine-Tuning) techniques, two of the leading approaches for this task that underpin the success of Cloudera’s Applied ML Prototypes (AMPs)…more details

 

How to Deliver Contextually Accurate LLMs image
Jake Bengtson
Sr. Product Marketing Manager | Cloudera
12:00 - 12:45
From Raw Data through Vectors to a Comprehensive Recommendation Model

In-person | Talk | Intermediate-Advanced

 

In today’s data-driven world, recommendation systems have become ubiquitous, driving user engagement and increasing revenue for businesses across various domains. This talk will take you on a journey from the raw data to vectorization techniques, ultimately culminating in the creation of a robust recommendation system. Whether you’re a seasoned data scientist or just starting your journey into recommendation systems, this presentation will provide valuable insights and practical takeaways for building powerful recommendation engines…more details

From Raw Data through Vectors to a Comprehensive Recommendation Model image
Hudson Buzby
Solution Architect | Qwak
12:00 - 12:30
The Crucial Role of Digital Experimentation and A/B Testing in the AI Landscape

Virtual | Talk | Responsible AI | Intermediate

 

 

Digital experimentation and A/B testing are invaluable methodologies within the AI domain, facilitating the validation and continuous improvement of models, solutions, and systems. This talk will delve into the intricate role of these testing mechanisms in the AI landscape.
We will start by introducing the concept of digital experimentation and A/B testing, elaborating on their integral role in testing hypotheses and making data-driven decisions. The discussion will further touch upon the traditional uses of these methodologies in the digital marketing sphere, enabling businesses to optimize their online content and increase user engagement…more details

 

The Crucial Role of Digital Experimentation and A/B Testing in the AI Landscape image
Alessandro Romano
Senior Data Scientist | Kuehne+Nagel
12:00 - 12:30
Attack on Machine Learning, Defend with MLOps

Virtual | Talk | MLOps and Data Engineering | Intermediate

 

 

With the wide adoption of generative artificial intelligence (AI), more than ever, ensuring the robustness of machine learning models is becoming crucial. One of the most concerning security threats to machine learning (ML) is the potential for adversarial attacks, a technique to exploit vulnerabilities of models to cause incorrect output. They are unapparent to humans but sufficient for machine learning models to misclassify the data, potentially harming the end users. Therefore, including adversarial training in the ML lifecycle is important to consider as you build out your model and prepare it for production usage. Join this talk to learn how the symbiosis between the ML security open source projects like Adversarial Robustness Toolbox and an ML operations (MLOps) project, Kubeflow, can streamline your machine learning workflow and improve your model’s robustness and security. With numerous benefits of the MLOps to assist in generating and defending your model, accelerate your development of secure machine learning models…more details

Attack on Machine Learning, Defend with MLOps image
Anna Jung
Sr. ML Open Source Engineer | VMware
12:00 - 12:45
General and Efficient Self-supervised Learning with data2vec

In-Person | Talk | NLP & LLMs | GenAI | Advanced

 

In this talk, I will present data2vec, a framework for general self-supervised learning that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches…more details