ODSC Webinar Calendar

ODSC’s free webinar series serves to educate our community on the languages, tools, and topics of AI and Data Science

Accelerating AI-driven Innovation in Your Enterprise

October 15, 2019
1 pm – 2 pm EST
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10/15/2019 10:00 AM
America/Los_Angeles
Accelerating AI-driven Innovation in Your Enterprise

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ODSC West Webinar

Pallav Agrawal
Director, Data Science at Levi Strauss & Co.

Accelerating AI-driven Innovation in Your Enterprise

The presentation will focus on best practices to develop ML powered applications that can move the needle on business critical KPIs. We will walk through a rapid prototyping framework to develop effective personalization experiences, the mindsets and skills required to execute on an innovation roadmap, how to evaluate and work with vendors that provide ‘AI-powered’ solutions, and how to design experiments to quickly iterate towards a better experience for customers.

Pallav Agrawal

During daytime, Pallav works as a Data Scientist and tries to extract meaningful signals from the noisy world we live in. As the moon rises and evening sets in all bets are off and one might find Pallav on his bike riding through the Berkeley hills in bright colored lycra or performing never-before-scenes of Dramedy with his Improv troupe.

Pallav is a part-time Human Centered Design Thinking coach and has helped non-profits and early-age startups develop clarity on their mission and recognize growth areas. He moved to the Bay Area in 2010 and somehow managed to acquire a Masters in Structural Engineering after spending two years actually learning how to think.

He is an avid follower of Seth Godin, Ken Robinson, and Nicholas Taleb, and is currently looking at ways to explain algorithms through cute, anthropomorphized animals.


Opening The Black Box - Interpretability In Deep Learning

October 16, 2019
11 am – 12 pm BST
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10/16/2019 03:00 AM
America/Los_Angeles
Opening The Black Box – Interpretability In Deep Learning

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ODSC Europe Webinar

Matteo Manica, PhD
Research Staff Member at Cognitive Health Care & Life Sciences, IBM Research Zürich

Opening The Black Box - Interpretability In Deep Learning

The recent application of deep neural networks to long-standing problems has brought a break-through in performance and prediction power. However, high accuracy often comes at the price of loss of interpretability, i.e. many of these models are black-boxes that fail to provide explanations on their predictions. This webinar will be an introduction to the ODSC Europe 2019’s training, which will focus on illustrating some of the recent advancements in the field of interpretable artificial intelligence. We will show some common techniques that can be used to explain predictions on pretrained models and that can be used to shed light on their inner mechanisms. The training is aimed to strike the right balance between theoretical input and practical exercises. The session has been designed to provide the participants not only with the theory behind deep learning interpretability, but also to offer a set of frameworks, tools and real-life examples that they can implement in their own projects.

Matteo Manica, PhD

Matteo is a Research Staff Member in Cognitive Health Care and Life Sciences at IBM Research Zürich. He’s currently working on the development of multimodal deep learning models for drug discovery using chemical features and omic data. He also researches in multimodal learning techniques for the analysis of pediatric cancers in a H2020 EU project, iPC, with the aim of creating treatment models for patients. He received his degree in Mathematical Engineering from Politecnico di Milano in 2013. After getting his MSc he worked in a startup, Moxoff spa, as a software engineer and analyst for scientific computing. In 2019 he obtained his doctoral degree at the end of a joint PhD program between IBM Research and the Institute of Molecular Systems Biology, ETH Zürich, with a thesis on multimodal learning approaches for precision medicine.


Introduction to Deep Learning Models for Computer Vision

Free recording will be available here


Haidar Altaie
Data Scientist at SAS UK&I

Spiros Potamitis
Data Scientist at SAS

Introduction to Deep Learning Models for Computer Vision

In this Webinar, we will discuss the application of DL models using DLPy focusing on Computer Vision. DLPy is a high-level Python API designed to provide an efficient way to apply Deep Learning functionalities using friendly Keras-like APIs to solve Computer Vision, Natural Language Processing, Forecasting, and Speech Processing problems. We explain how DLPy can be applied to data preparation, data processing, model building, assessment and deployment.

This will be a preview of our more in-depth presentation, specifically focused around Multi-Task Deep Learning For Image Tagging, during ODSC Europe in London this November.

Haidar Altaie

Haidar Altaie is a Data Scientist at SAS UK&I. He joined SAS in September 2018 after graduating with a Mathematics and Statistics degree, and is now passionate to integrate Advanced Analytics, Machine Learning, Forecasting and Computer Vision techniques across various industries to enable customer to solve complex real life problem.

Spiros Potamitis

Spiros Potamitis is a data scientist at SAS, a leading software and services provider in advanced analytics. Having acquired an MSc in Information Management from the University of Manchester, Spiros is specialising in the application and implementation of analytics to drive business outcomes. Prior of joining SAS, Spiros has acquired a wealth of predictive modelling experience while working in advanced analytics positions in Credit Risk, Customer Insights and CRM.


Human Machine Learning

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Matt Cowell
CEO at QuantHub

Nathan Black
Chief Data Scientist at QuantHub

Human Machine Learning

Could we, as humans, improve the way we learn by applying techniques machines use to learn? From early in the field of AI, researchers have been looking to cognitive psychology for inspiration on how to teach machines to learn. The effectiveness of this approach is evidenced by recent advances in, and growing prevalence of, deep learning. However, we’ve reached a point where machine learning methodologies are deviating from the way humans learn and gaining impressive efficiencies as a result. For humans, this issue is amplified by current findings in cognitive psychology which strongly suggests that many of our long-standing learning methods have been largely misguided. In this webinar, we explore the challenges with common studying practices, contrasting those with methods that machines successfully use to learn, and drawing parallels to recent cognitive psychology research.

Matt Cowell

Matt serves as the CEO at QuantHub, spearheading the drive to help companies overcome the extreme analytics talent shortage and build exceptional data science and engineering teams. Matt has a passion for developing authentic relationships with customers to truly understand what drives them, and then crafting creative solutions to their most critical problems. Prior to joining QuantHub, Matt spent the last 15 years running product and tech at PE-backed companies, including building a product and engineering organization at Daxko to deliver 10x revenue growth, 7 acquisitions, and 3 enormously successful recapitalizations in just 10 years. While at Daxko, Matt led the team to deliver the first machine learning/AI solution to the market, predicting customer membership churn and also propensity to donate.

Nathan Black

Nathan Black is a Data Science Professional and AI Researcher with over 5 years of experience leading and working alongside quant teams to develop cutting-edge, end-to-end data solutions in manufacturing, healthcare, food retail, finance, and education industries. Nathan has a proven track record of using data to help people thrive, assisting organizations in capturing value from data and technology through the deployment of BI, Prescriptive Modeling, and Artificial Intelligence applications.


ODSC West 2019 Warm-Up: Machine Learning

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Bill Heffelfinger
Principal Solutions Architect at CloudFactory

Introducing the Data Production Line for Machine Learning

Data operations are the new AI factory model. Data is your raw material, and you have to move it through multiple processing and review steps before it’s ready for machine learning (ML). Developing high-performing ML models requires a strategic combination of people, tools, and process working together to structure massive data with high quality. In this segment, CloudFactory will share best practices for your data production line.

Bill Heffelfinger

Crissman Loomis
AI Engineer at Preferred Networks

Optuna: A Define-by-Run Hyperparameter Optimization Framework

In this workshop, we introduce Optuna, a next-generation hyperparameter optimization framework with new design-criteria: (1) define-by-run API that allows users to concisely construct dynamic, nested, or conditional search spaces, (2) efficient implementation of both sampling and early stopping strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to lightweight experiment conducted in a local laptop machine. Our software is available under the MIT license.

Crissman Loomis

Since his mathematics degree, Crissman has devoted himself to the study of languages, including Spanish, Javascript, German, Python, and Japanese. Previously, Crissman worked on open source projects for automation of game playing systems, including MMORPGs, web-based games, and Pokemon. After finding the limits of rule-based systems, he worked on Deep Learning programs at Preferred Networks, the company that created the AI Python framework Chainer.


AI Infrastructure and Supporting the Rise of Data Science

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Darrin P. Johnson
Global Director of Solution Architectures at NVIDIA

Matt Miller
Director of Product Marketing at WekaIO

Greg Holick
Director of Technology Alliances at Western Digital

AI Infrastructure and Supporting the Rise of Data Science

The rise of data science is often attributed to the exponential growth of data, whether structured or unstructured. While likely true, it is also true that the supporting AI infrastructure has enabled not only the growth of the data but also has become critical to extracting the value from the data explosion. The industry leaders NVIDIA, WekaIO and Western Digital will each bring their perspective to the importance of AI infrastructure to data science. Whether you are a data scientist, IT professional, or C-level decision maker you will learn how thoughtful AI infrastructure can accelerate your time to insight, time to value and increase profit for your business. You will take away techniques to overcome common challenges and barriers to successful data science in development and in production. Come ready with your questions for the panel to help accelerate your data science.

Darrin P. Johnson

Darrin is the Global Director of Solution Architecture for Enterprise at Nvidia. He and his team lead all DGX, OEM, and storage reference architecture initiatives. Darrin’s experience spans 25 years of leadership in O/S, high performance systems, networking, storage, I/O and most recently AI/Deep Learning technologies with companies such as Cray, SGI, Adaptec, Sun Microsystems, Oracle and now NVIDIA. He is a certified Deep Learning trainer for NVIDIA as well.

Matt Miller

Matt Miller is the Director of Product Marketing for WekaIO, responsible for marketing strategy and positioning. Matt has spent nearly 20 years in the storage industry in both product management and product marketing roles, for companies such as HPE, Nimble Storage, NetApp, Sun Mircosystems and Veritas.

Greg Holick

Greg Holick is a senior technologist with over 15 years of experience in the data storage industry. Throughout his tenure, Greg has engineered software solutions, architected complex storage environments, been the product manager on private cloud solutions, and guided customers and partners on some of the most challenging storage infrastructures in the industry.


Deployment of Strategic AI in the Enterprise: Crossing the Chasm

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Fernando Núñez Mendoza
Founder and CEO at fonYou

Deployment of Strategic AI in the Enterprise

The talk “Deployment of Strategic AI in the Enterprise” in the Accelerate AI track of the ODSC West 2019 conference, will argue that the best way to effectively overcome these obstacles is to choose the most critical parts of the business as those in which AI shall be deployed first. This webinar focuses on one key aspect of the strategic AI deployment approach proposed in the talk:  how organizations can cross the chasm that separates AI awareness from AI readiness.

Instead of launching a broad transformational approach to attain AI readiness, it is much more effective to laser focus on critical aspects of the digital value creation network and launch an agile task-force initiative to improve them with AI. By increasingly choosing more complex and ambitious targets, the AI-ready stage should be finally achieved.

Fernando Núñez Mendoza

Fernando Núñez Mendoza, a serial technology entrepreneur and disruptor, is founder, chief executive officer, and chief technology officer of fonYou, a fast-growing international company born in Barcelona, Spain. fonYou’s mission is to build the mobile carrier of the future powered by AI. Before fonYou, he was a management consulting partner at Accenture and Diamond Cluster International helping global telecommunications, technology, and financial services firms embrace the internet and thrive in the brave new digital world. In his earlier career, Fernando worked for the European Space Agency and lectured and performed research in computer engineering and neural networks.

Fernando holds, MSEE and Ph.D. degrees in Electrical and Computer Engineering from the Polytechnic University of Catalonia (Spain), was an invited Visiting Scholar at Purdue University and is alumni of Stanford University Graduate School of Business.


Telling Human Stories With Data

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Alan Rutter
Founder of Fire Plus Algebra

Telling Human Stories With Data

Robust data analysis underpins every business decision, public sector project and non-profit initiative. But data in its raw form often fails to convince crucial lay audiences – either due to its complexity, or due to suspicion and mistrust. And you can’t help guide the world in the right direction if you alienate key decision-makers or the public.

This talk, delivered by journalist and data visualization specialist Alan Rutter, will cover an audience-centered approach to visualizing data. It will introduce tried-and-tested techniques for communicating data-driven stories effectively to people from a broad range of backgrounds, and deal with some of the common problems that practitioners encounter.

Alan Rutter

Alan Rutter is the founder of consultancy Fire Plus Algebra, and is a specialist in communicating complex subjects through data visualisation, writing and design. He has worked as a journalist, product owner and trainer for brands and organisations including Guardian Masterclasses, WIRED, Time Out,the Home Office, the Biotechnology and Biological Sciences Research Council and Liverpool School of Tropical Medicine.


Dumb & Dumber vs Ocean’s 11: Tackling evolving, sophisticated fraud with AI

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Sathya Chandran, PhD
Security Research Scientist at DataVisor

Dumb & Dumber vs Ocean’s 11: Tackling evolving, sophisticated fraud with AI

Sophisticated fraud attacks that are extensively planned, hard to detect, and highly scalable are becoming the new normal for online platforms. Learn more about the spectrum of fraud attacks – from “dumb & dumber” to “ocean’s 11″– and why Unsupervised Machine Learning is the key to detecting attacks before they inflict damage.

Sathya Chandran, PhD

Sathya is an expert in applying big data and unsupervised machine learning to fraud detection, specializing in the financial, e-commerce, social, and gaming industries. Sathya holds PhD in CS from the University of South Florida and has previously worked at HP Labs and Honeywell.


When Holt-Winters is better than Machine Learning for Time Series Data

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Anais Dotis-Georgiou
Developer Advocate at InfluxData

When Holt-Winters is better than Machine Learning for Time Series Data

Machine Learning is all the rage, but when does it make sense to use it for forecasting? How do statistical forecasting methods compare? In this presentation, Developer Advocate Anais Dotis-Georgiou will show you how the Holt-Winters forecasting algorithm works. Then we’ll use the HOLT_WINTERS() function with InfluxData to make our own time series forecast.

Anais Dotis-Georgiou

Anais Dotis-Georgiou is a Developer Advocate for InfluxData with a passion for making data beautiful with the use of Data Analytics, AI, and Machine Learning. She takes the data that she collects, does a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty. When she is not behind a screen, you can find her outside drawing, stretching, boarding, or chasing after a soccer ball.


Kubeflow, MLFlow and beyond - augmenting ML delivery

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Stepan Pushkarev
CTO in Provectus

Kubeflow, MLFlow and beyond - augmenting ML delivery

Reproducible ML pipelines in research and production with monitoring insights from live inference clusters could enable and accelerate the delivery of AI solutions for enterprises. There is a growing ecosystem of tools that augment researchers and machine learning engineers in their day to day operations. Still, there are big gaps in the machine learning workflow when it comes to training dataset versioning, training performance and metadata tracking, integration testing, inferencing quality monitoring, bias detection, concept drift detection and other aspects that prevent the adoption of AI in organizations of all sizes.

In this webinar, we’ll design a reference machine learning workflow. We’ll review open source tools that contribute to this workflow and are applicable to build reproducible automation of it.

Presenter bio

Stepan Pushkarev is a CTO of Provectus. His background is in the engineering of data platforms. He spent the last couple of years building continuous delivery and monitoring tools for machine learning applications as well as designing streaming data platforms. He works closely with data scientists to make them productive and successful in their daily operations.

Free access to ODSC talks and content is available at our

AI Learning Accelerator

ODSC EAST | Boston

– April 30th – May 3rd, 2019 –

The World’s Largest Applied Data Science Conference

ODSC EUROPE | London

– Nov 19th – 22nd, 2019 –

Europe’s Fastest Growing Data Science Community

ODSC WEST | San Francisco

– Oct 29th – Nov 1st, 2019 –

The World’s Largest Applied Data Science Conference

Accelerate AI

Business Conference

The Accelerate AI conference series is where executives and business professionals meet the best and brightest innovators in AI and Data Science. The conference brings together top industry executives and CxOs that will help you understand how AI and data science can transform your business.

Accelerate AI East | Boston

– April 30th – May 1st, 2019 –

The ODSC summit on accelerating your business growth with AI

Accelerate AI Europe | London 

– Nov 19th – 20th, 2019 –

The ODSC summit on accelerating your business growth with AI

Accelerate AI West | San Francisco 

– Oct 29th – 30th, 2019 –

The ODSC summit on accelerating your business growth with AI