ODSC Webinar Calendar

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

ODSC India 2019 Warm-Up: Data Science Kick Starter

July 13th, 2019
11 am – 12:30 pm IST
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07/13/2019 10:00 PM
America/Los_Angeles
ODSC India 2019 Warm-Up

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ODSC Webinar

Kavita D. Chiplunkar
Data Science Head, Infinite-Sum Modelling Inc.

Nirav Shah
Founder of OnPoint Insights

Building a Scorecard using Python

This webinar will tell you the importance of Credit Scorecards in Banking /Financial Institutions , how they are used to measure the credit worthiness of a customer and how Machine Learning Algorithms are helping built better scorecards than traditional algorithms.We plan to briefly discuss the key data elements that would be required to build such scorecards.We will talk at high level about various steps in building a scorecard .We will also share a brief snapshot of what to expect out of our session at ODSC and how this session can benefit Data Science Enthusiasts and Banking professionals.

Kavita D. Chiplunkar

Kavita is an Analytics leader with 12 + years of core hands on experience having an excellent track record on Presales, Partner Management, Analytics Delivery and Team management across domains in World Class Organizations. Currently, she is heading the Data Science function at Infinite Sum Modeling. She is a Chemical Engineer by education followed by a Masters (Eco) from IGIDR. She is a seasoned analytics professional with work experiences across companies like Fair Isaac, Experian, Accenture, Infosys and Vodafone. Her vast experience in domains like Banking, Insurance, Telecom, Fraud and Risk Management give her the right kind of diversification. She has published papers in areas of Financial Econometrics and Social Media Analytics. She has been an esteemed speaker at various national seminars on Analytics.

Nirav Shah

Nirav Shah is the Founder of OnPoint Insights, a data analytics, software services and staff augmentation consultancy based in Boston. He has 15 years of industry experience – mainly in consulting on data analytics, big data modeling, control systems, process analytics and software tools, off-line and real-time data solutions, and training customers in data analytics,dashboards and data visualization. He is an expert in Dashboards and Visualization using Tableau and other Multivariate Data Analytics software.

Ramanathan Ramakrishnamoorthy
Director & Co-Founder of Zentropy Technologies

Gurram Poorna Prudhvi
Machine Learning Engineer at mroads

Time Series analysis in Python

Time series analysis has been around for centuries helping us to solve from astronomical problems to business problems and advanced scientific research around us now. Time stores precious information, which most machine learning algorithms don’t deal with. But time series analysis, which is a mix of machine learning and statistics helps us to get useful insights. Time series can be applied to various fields like economy forecasting, budgetary analysis, sales forecasting, census analysis and much more. In this workshop, We will look at how to dive deep into time series data and make use of deep learning to make accurate predictions.

Ramanathan Ramakrishnamoorthy

Co-Founder, Director & Head of Research & Development at Zentropy Technologies. Before finding Zentropy, Ram worked with a leading hedge fund as a Project Manager responsible for building tools and technologies required by the middle and the back office. He was instrumental in delivering some of the most mission-critical strategic projects that helped in the overall business of the firm.

Gurram Poorna Prudhvi

Prudhvi is working as a machine learning engineer at mroads. He is interested in NLP research, Opensource, Public Speaking, and Python. In his free time he explores and tries to understand different dimensions of life. He is also a core team member of Hyderabad Python Community.


Kubeflow, MLFlow and beyond - augmenting ML delivery

July 16th, 2019
1 pm – 2 pm EST
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07/16/2019 10:00 AM
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Kubeflow, MLFlow and beyond – augmenting ML delivery

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ODSC Webinar

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.

Takeaways:

– A deeper view on traps and pitfalls on each stage of ML lifecycle.

– Reference implementation and automation of ML Workflow.

Prerequisite knowledge:

– Understanding of core Data Science methods, frameworks and libraries.

– An image of what Docker and Kubernetes are.

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.


ODSC West 2019 Warm-Up: Machine Learning

July 24th, 2019
1 pm – 2:30 pm PST
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07/24/2019 01:00 PM
America/Los_Angeles
ODSC West 2019 Warm-Up

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ODSC Webinar

Vinod Bakthavachalam
Data Scientist at Coursera

Causal Inference & Machine Learning

Lots of data science problems, especially towards informing business and product strategy, involve understanding causal relationships. The standard way to measure these is through AB testing, but many times that is infeasible, requiring alternative techniques from causal inference that are an essential component of any data scientist’s toolkit. The talk will walk through these techniques, some applications, and recent work at the intersection of causal inference and machine learning to handle large data sets.

Vinod Bakthavachalam

Vinod Bakthavachalam is a data scientist working with the Content Strategy and Enterprise teams where his work has recently focused on understanding the skills landscape around the world using Coursera data (see the Global Skills Index Coursera recently published for some of his work). Prior to Coursera, he majored in Economics, Statistics, and Molecular and Cell Biology at UC Berkeley, and worked in quantitative finance.

Dipanjan Sarkar
Principal Data Scientist at Red Hat

Real-ish Time Predictive Analytics with Spark Structured Streaming

In 20 short minutes learn what becomes possible when you add Spark into your analytics pipeline. Learn how to effectivley solve common Data Engineering problems with compile-time guarenttes – like how to ingest, normalize, transform and join datasets in realtime. Learn how to add insights on top of your streaming data with simple filters and pre-trained models.

Scott Haines

Scott Haines is a distributed systems engineer focused on real-time, highly available, trust- worthy analytics systems. He works at Twilio where he is a Principal Software Engineer on the Voice Insights team where he helped drive spark adoption, streaming pipeline architecture best practices, as well as a massive stream processing platform. Prior to Twilio, he worked writing the backend Java API’s for Yahoo Games, as well as the real- time game ranking/ratings engine (built on Storm) to provide personalized recommendations and page views for 10 million customers. He finished his tenure at Yahoo working for Flurry Analytics where he wrote the alerts/notifications system for mobile.

Jane Adams
Data Visualization Artist at University of Vermont Complex Systems Center

Data Art: Seeing the Future of Exploratory Analysis

The landscape of data visualization tools is expansive and growing. Data artist Jane Adams gives a scintillating teaser of the myriad methods for interactive visual analytics through a cursory demonstration of a project structure and creative workflow. Jane reviews one project’s development process: from paper & pencil exercises in user experience stories and user interface wireframing, to prototyping visualizations in Python using Plotly, building an API in React, and developing a customized visualization user interface in D3.js.

Jane Adams

Jane Adams is an emergent media artist, working at the intersection of visual expression and scientific inquiry. As the Data Visualization Artist in Residence at the University of Vermont Complex Systems Center, Jane builds engaging, interactive, web-based visualizations of high-dimensional data for exploratory analysis. Her visualization research topics include social network lexical analysis, healthcare morbidity and mortality modeling, and geospatial temporal dynamics, all through a lens of complexity science. In her spare time, Jane experiments with music-color synesthesia, machine learning for computational creativity, self-sustaining aquaponic sculpture, and citizen science. She is the lead community organizer of Vermont Women in Machine Learning and Data Science (WiMLDS), and holds a MFA in Emergent Media. Stay in touch on Twitter @artistjaneadams


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

Aug 15th, 2019
1 pm – 2 pm EST
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08/15/2019 10:00 AM
America/Los_Angeles
When Holt-Winters is better than Machine Learning for Time Series Data

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ODSC Webinar

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.


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

Aug 29th, 2019
1 pm – 2 pm EST
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08/29/2019 10:00 AM
America/Los_Angeles
Dumb & Dumber vs Ocean’s 11: Tackling evolving, sophisticated fraud with AI

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ODSC Webinar

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.


Previous Webinars


Check out our previous AI talks at learnai.odsc.com below


ODSC India 2019 Warm-Up: Machine Learning & Deep Learning

Free recording will be available here

Dr. C.S.Jyothirmayee
Sr. Scientist at Novozymes South Asia Pvt Ltd

Usha Rengaraju
Principal Data Scientist at Mysuru Consulting Group

Vijayalakshmi Mahadevan
Faculty Scientist at Institute of Bioinformatics and Applied Biotechnology (IBAB)

Deep learning powered Genomic Research

The event disease happens when there is a slip in the finely orchestrated dance between physiology, environment and genes. Treatment with chemicals (natural, synthetic or combination) solved some diseases but others persisted and got propagated along the generations. Molecular basis of disease became prime center of studies to understand and to analyze root cause. Cancer also showed a way that origin of disease, detection, prognosis and treatment along with cure was not so uncomplicated process. Treatment of diseases had to be done case by case basis (no one size fits).
With the advent of next generation sequencing, high through put analysis, enhanced computing power and new aspirations with neural network to address this conundrum of complicated genetic elements (structure and function of various genes in our systems). This requires the genomic material extraction, their sequencing (automated system) and analysis to map the strings of As, Ts, Gs, and Cs which yields genomic dataset. These datasets are too large for traditional and applied statistical techniques. Consequently, the important signals are often incredibly small along with blaring technical noise. This further requires far more sophisticated analysis techniques. Artificial intelligence and deep learning gives us the power to draw clinically useful information from the genetic datasets obtained by sequencing.

Dr. C.S.Jyothirmayee

As Senior Technology Innovation Specialist,work on exploring innovative technologies in the field of biology. Before Novozymes, worked on comparative genomics of H. Pylori, mutational analysis of cataract protein and developing human model for cancer studies at prestigious national laboratories at CDFD, CCMB (Hyderabad) and NCCS, Pune respectively.

Additionally, I am a registered patent agent. Combining my domain knowledge in Biological science and application oriented patent analytics (PatInformatics) and  work one three areas:
a. Using Patent & Literature data for deriving technology evolution insights for future project planning
b. Pitching new ideas and exploring their feasibility
c. Networking with new ventures and exploring new areas for organization opportunities.

Usha Rengaraju

I am a polymath and unicorn data scientist with strong foundations in Economics, Finance, Business Foundations, Business Analytics and Psychology. I specialize in Probabilistic Graphical Models, Machine Learning and Deep Learning. I have completed Financial Engineering and Risk Management program from Columbia University with top honors, micromasters in Marketing Analytics from UC Berkeley and statistical analysis in Life Sciences specialization from Harvard. I am chapter lead/Co-Organizer of Women in Machine Learning and Data Science Bengaluru Chapter and Core oganizing team member at WIDS Bengaluru .I have around 6 years of technical experience working in various companies like Infosys, Temenos, NeoEYED and Mysuru Consulting Group. I am part of dedicated group of experts and enthusiasts who explore Coursera courses before they open to the public, an ambassador at AIMed (an initiative which brings together physicians and AI experts), part time Data science instructor, mentor at GLAD (gladmentorship.com), mentor at JobsForHer and volunteer at Statistics without Borders. I developed the course curriculum for Probabilistic Graphical Models @ Upgrad which is taught by Professor Srinivasa Raghavan from IIIT Bangalore.

Vijayalakshmi Mahadevan

With a background in Physics and Electronics from the Bharathidasan University,Trichy, Dr.Vijayalakshmi Mahadevan completed her Ph.D. from the National Centre for Biological Sciences- Tata Institute of Fundamental Research( NCBS-TIFR), Bangalore. She was an Assistant Professor in the School of Electrical and Electronics Engineering at SASTRA Deemed University in Thanjavur and a TCS Chair Professor of Bioinformatics and Associate Dean of the School of Chemical & Biotechnology.She was the Group Lead of the Chromatin and Epigenetics group also headed the Department of Bioinformatics from 2008 to 2016 besides being affiliated to the Centre for Nanotechnology and Advanced Biomaterials (CeNTAB) at SASTRA.

Dr.Vijayalakshmi was also a Research Mentor in the National Network for Mathematical and Computational Biology (NNMCB), India from 2013 and was a Research Mentor – Research Science Initiative (RSI) of the IIT Madras, Chennai Mathematical Institute, SASTRA University, Thanjavur, PSBB Group of Schools, Chennai and Centre for Excellence in Education, McLean,USA to promote scientific research among school children.

Dipanjan Sarkar
Principal Data Scientist at Red Hat

Anuj Gupta
S
cientist at Intuit

A Hands-on Introduction to Natural Language Processing

Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any data scientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive case- studies and hands-on examples to master state-of-the-art tools, techniques and frameworks for actually applying NLP to solve real- world problems. We leverage Python 3 and the latest and best state-of- the-art frameworks including NLTK, Gensim, SpaCy, Scikit-Learn, TextBlob, Keras and TensorFlow to showcase our examples. You will be able to learn a fair bit of machine learning as well as deep learning in the context of NLP during this bootcamp.

The intent of this workshop is to make you a hero in NLP so that you can start applying NLP to solve real-world problems. We start from zero and follow a comprehensive and structured approach to make you learn all the essentials in NLP. We will be covering the following aspects during the course of this workshop with hands-on examples and projects!

Dipanjan Sarkar

Dipanjan (DJ) Sarkar is a Data Scientist at Red Hat, a published author, and a consultant and trainer. He has consulted and worked with several startups as well as Fortune 500 companies like Intel. He primarily works on leveraging data science, advanced analytics, machine learning and deep learning to build large- scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He is also an avid supporter of self-learning and massive open online courses. He has recently ventured into the world of open-source products to improve the productivity of developers across the world.
Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, statistical methods and deep learning. Having a passion for data science and education, he also acts as an AI Consultant and Mentor at various organizations like Springboard, where he helps people build their skills on areas like Data Science and Machine Learning. He also acts as a key contributor and Editor for Towards Data Science, a leading online journal focusing on Artificial Intelligence and Data Science. Dipanjan has also authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing, and Deep Learning.
Dipanjan’s interests include learning about new technology, financial markets, disruptive start-ups, data science, artificial intelligence and deep learning. In his spare time he loves reading, gaming, watching popular sitcoms and football and writing interesting articles on https://medium.com/@dipanzan.sarkar and https://www.linkedin.com/in/dipanzan. He is also a strong supporter of open-source and publishes his code and analyses from his books and articles on GitHub at https://github.com/dipanjanS.

Anuj Gupta

I am part of Intuit AI team. Prior to this, I was heading ML efforts for Huawei Technologies, Freshworks, Chennai and Airwoot, Delhi. I did my masters in theoretical computer science from IIIT Hyderabad and I dropped out of my Phd from IIT Delhi to work with startups. 

I am a regular speaker at ML conferences like Pydata, Nvidia forums, Fifth Elephant, Anthill. I have also conducted a bunch of workshop attended by machine learning practitioners. I am also the co-organizer for one of the early Deep Learning meetups in Bangalore.  I am also Editor of “Anthill-2018” – deep learning focused conference by HasGeek.


Model-based Reinforcement Learning for Atari

Free recording will be available here


Błażej Osiński
Senior Data Scientist at deepsense.ai

Model-based Reinforcement Learning for Atari

Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction – substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works and predict which actions will lead to desirable outcomes.

In this webinar we will explore:

  • How video prediction models can be used to improve the sample efficiency of reinforcement learning?

  • How to create a model capable of predicting future in Atari games?

  • How to train the RL agent within “dreams” of another neural network?

Presenter bio

Błażej Osiński is a researcher at deepsense.ai working on reinforcement learning. His professional experience includes working at Google, Google Brain, Microsoft and Facebook. He was also the first software engineer at Berlin-based startup Segment of 1. Błażej holds a Masters Degree in Computer Science and Bachelors in Mathematics, both from the University of Warsaw.


OmniSci and RAPIDS: An End-to-End Open-Source Data Science Workflow

Free recording will be available here


Randy Zwitch
Senior Developer Advocate at OmniSci

OmniSci and RAPIDS: An End-to-End Open-Source Data Science Workflow

In this session, attendees will learn how the OmniSci GPU-accelerated SQL engine fits into the overall RAPIDS partner ecosystem for open-source GPU analytics. Using open bike-share data, users will learn how to ingest streaming data from Apache Kafka into OmniSci, perform descriptive statistics and feature engineering using both SQL and cuDF with Python and return the results as a GPU DataFrame. By the end of the session, attendees should feel comfortable that an entire data science workflow can be accomplished using tools from the RAPIDS eco-system, all without the data ever leaving the GPU.

Topics to be highlighted:
– What is RAPIDS? (discussion of NVIDIA open-source RAPIDS project, how it relates to Apache Arrow, etc.)
– What is OmniSci and how does it fit into the RAPIDS eco-system
– Example:
– Ingesting a data stream from Apache Kafka into OmniSci
– Using pymapd (Python) to query data from OmniSci and do basic visualizations
– Use cudf to do data cleaning and feature engineering
– Show how cudf dataframes can be passed to machine learning libraries like Tensorflow, PyTorch or xgboost.

Presenter bio

Randy Zwitch is a Senior Developer Advocate at OmniSci, enabling customers and community users alike to utilize OmniSci to its fullest potential. With broad industry experience in Energy, Digital Analytics, Banking, Telecommunications and Media, Randy brings a wealth of knowledge across verticals as well as an in-depth knowledge of open-source tools for analytics.


Quantum Machine Learning: The future scope of AI

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Dr. Santosh Kumar Nanda
Asst. General Manager (Lead Data Scientist) in Analytics Center of Excellence, (R & D), FLYTXT Mobile Solution Pvt. Ltd., Trivandrum, India

Quantum Machine Learning: The future scope of AI

Over the past half-century, the rapid progression in computing devices, availability of high-performance computing devices helps a researcher to do more research with high volume data. Recently IBM successfully developed quantum processor-based computing devices which very faster than the current computing devices. In general, quantum computing based computing devices integrated with a quantum bit which is faster than a binary bit. Therefore, quantum computing based computer can able to read and process high volume data in a very faster way to compare with conventional 64-bit computing devices. In a similar way, the available classical machine learning algorithms based on binary bit operation has slow performance in high volume data. It is also predicted after commercialization of quantum processor based computer, it will help many industries with maximum benefit and the field of quantum machine learning will widely open to new innovation for solving of future complex problems. This presentation representing the quantum machine learning concepts, architectures and model development with quantum bit operations.

Presenter bio

Dr. Santosh Kumar Nanda is working as Asst. General Manager (Lead Data Scientist) in Analytics Center of Excellence, (R & D), FLYTXT Mobile Solution Pvt. Ltd., Trivandrum, India.  He completed his Ph.D. from National Institute of Technology, Rourkela. His research interests are Computational Intelligence, Artificial Intelligence, Machine Learning, Statistics and Data Science, Mathematical modeling, Pattern Recognition. He has more than 60 research articles in reputed International Journals and International conferences etc. He is now Editor-in-Chief of Journal of Artificial Intelligence, Associate Editor in International Journal of Intelligent System and Application. He is a member of World Federation Soft Computing, USA.


ODSC East 2019 Warm-Up: DataOps

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Haftan Eckholdt, Ph.D.
Chief Data Science & Chief Science Officer, Understood.org

Making Data Science: AIG, Amazon, Albertsons

Developing an internal data science capability requires a cultural shift, a strategic mapping process that aligns with existing business objectives, a technical infrastructure that can host new processes, and an organizational structure that can alter business practice to create a measurable impact on business functions. This workshop will take you through ways to consider the vast opportunities for data science to identify and prioritize what will add the most value to your organization, and then budget and hire into commitments. Learn the most effective ways to establish data science objectives from a business perspective including recruiting, retention, goal setting, and improving business.

Presenter bio

Haftan Eckholdt, PhD. is Chief Data Science Office at Understood.org. His career began with research professorships in Neuroscience, Neurology, and Psychiatry followed by industrial research appointments at companies like Amazon and AIG. He holds graduate degrees in Biostatistics and Developmental Psychology from Columbia and Cornell Universities. In his spare time, he thinks about things like chess and cooking and cross country skiing and jogging and reading. When things get really really busy, he actually plays chess and cooks delicious meals and jogs a lot. Born and raised in Baltimore, Haftan has been a resident of Kings County, New York since the late 1900s.

Christopher P. Berg

CEO, Head Chef, DataKitchen

The DataOps Manifesto

The list of failed big data projects is long. They leave end-users, data analysts and data scientists frustrated with long lead times for changes. This presentation will illustrate how to make changes to big data, models, and visualizations quickly, with high quality, using the tools analytic teams love. We synthesize DevOps, Demming, and direct experience into the DataOps Manifesto.
To paraphrase an old saying: “It takes a village to get insights from data.” Data analysts, data scientists, and data engineers are already working in teams delivering insight and analysis, but how do you get the team to support experimentation and insight delivery without ending up failing? Christopher Bergh presents the seven shocking steps to get these groups of people working together. These seven steps contain practical, doable steps that can help you achieve data agility.
After looking at trends in analytics and a brief review of Agile, Christopher outlines the steps to apply DevOps techniques from software development to create an Agile analytics operations environment, including how to add tests, modularize and containerize, do branching and merging, use multiple environments, parameterize your process, use simple storage, and use multiple workflows deploy to production with W. Edwards Deming efficiency. They also explain why “don’t be a hero” should be the motto of analytic teams—emphasizing that while being a hero can feel good, it is not the path to success for individuals in analytic teams.
Christopher’s goal is to teach analytic teams how to deliver business value quickly and with high quality. They illustrate how to apply Agile processes to your department. However, a process is not enough. Walking through the seven shocking steps will demonstrate how to create a technical environment that truly enables speed and quality by supporting DataOps.

Presenter bio

Christopher Bergh is a Founder and Head Chef at DataKitchen.
Chris has more than 20 years of research, engineering, analytics, and executive management experience. Previously, Chris was Regional Vice President in the Revenue Management Intelligence group in Model N. Before Model N, Chris was COO of LeapFrogRx and analytics software and service provider. Chris led the acquisition of LeapFrogRx by Model N in January 2012. Prior to LeapFrogRx Chris was CTO and VP of Product Management of MarketSoft (now part of IBM) an Enterprise Marketing Management software vendor. Prior to that, Chris developed Microsoft Passport, the predecessor to Windows Live ID, a distributed authentication system used by 100s of Millions of users today. He was awarded a US Patent for his work on that project. Before joining Microsoft, he led the technical architecture and implementation of Firefly Passport, an early leader in Internet Personalization and Privacy. Microsoft subsequently acquired Firefly. Chris led the development of the first travel-related e-commerce web site at NetMarket. Chris began his career at the Massachusetts Institute of Technology’s (MIT) Lincoln Laboratory and NASA Ames Research Center. There he created software and algorithms that provided aircraft arrival optimization assistance to Air Traffic Controllers at several major airports in the United States. Chris served as a Peace Corps Volunteer Math Teacher in Botswana, Africa. Chris has an M.S. from Columbia University and a B.S. from the University of Wisconsin-Madison. He is an avid cyclist, hiker, reader, and father of two teenagers.


Ethical Large-Scale Artificial Intelligence within Sports

Free recording will be available here


Aaron Baughman
AI Architect, Master Inventor, IBM

Ethical Large-Scale Artificial Intelligence within Sports

Unintended bias and unethical Artificial Intelligence (AI) technologies can be detected by fairness metrics and corrected with mitigation techniques. Fair computational intelligence is important because AI is augmenting human tasks and decisions within every facet of life. As a core component of society, sports and entertainment are becoming driven with machine learning algorithms. For example, over 10 million ESPN fantasy football players use Watson insights to pick their roster week over week. A fair post processor ensures NFL players, irrespective of the team assignment, are projected for an impartial boom in play so that owners avoid basing their team roster decisions on biased insights. This is critically important because users spent over 7.7 billion minutes on the ESPN Fantasy Football platform during the 2018 season. In another example, automated video highlight generation at golf tournaments should be contextually fair. Golf player biographical data, game play context and weather information should not skew deep learning excitement measurements. An overall player video highlight excitement score that includes gesture, crowd noise, commentator tone, spoken words, facial expressions, body movement and 40 situational features is continually debiased. The resulting highlights are pulled into personalized highlight reels and stored on a web accelerator tier. Throughout the talk, I will show examples of using an open source library called IBM AI Fairness 360 and the IBM OpenScale cloud service to provide highly veracious insights.

Presenter bio

Aaron K. Baughman is a Principal AI Architect and 3x Master Inventor within IBM Interactive Experience focused on Artificial Intelligence for sports and entertainment. He has worked with ESPN Fantasy Football, NFL’s Atlanta Falcons, The Masters, USGA, Grammy Awards, Tony Awards, Wimbledon, USTA, US Open, Roland Garros and the Australian Open.He led and designed the ESPN Fantasy Football with Watson that has over 2 billion hits per day. Aaron worked on Predictive Cloud Computing for sports that have been published in IEEE and INFORMS. He was a Technical Lead on a DeepQA (Jeopardy!) project and an original member of the IBM Research DeepQA embed team. Early in his career, he worked on biometrics (face, iris, and fingerprint), software engineering and search projects for US classified government agencies. He has published numerous scientific papers and a Springer book.    Aaron holds a B.S. in Computer Science from Georgia Tech, an M.S. in Computer Science from Johns Hopkins, 2 certificates from the Walt Disney Institute and a Deep Learning certificate from Coursera. Aaron is a 3-time IBM Master Inventor, IBM Academy of Technology member, Corporate Service Corps alumni, a lifelong INFORMS Franz Edelman laureate, global Awards.ai winner and a AAAS-Lemelson Invention Ambassador. He has 101 granted patents with over 150 pending.

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