VIRTUAL

AI and Machine Learning in the Enterprise

APRIL 20, 2022

Sponsored by

Join DSS Virtual for a day full of knowledge sharing and networking! All sessions will be available on-demand until two weeks after the event.

speakers

on demand

attendees online

speakers

Serg Masis

Serg Masis

Climate and Agronomic Data Scientist at Syngenta

Yasmin Sahaf

Yasmin Sahaf

Staff Machine Learning Engineer at Illumina

Filipa Castro

Filipa Castro

Data Scientist at Continental

Matt Denesuk

Matt Denesuk

SVP, Data Analytics & Artificial Intelligence at Royal Caribbean Group

Andy Ray Terrel

Andy Ray Terrel

VP, Data and Algorithms at Xometry

Charles Irizarry

Charles Irizarry

CEO & Co-Founder at Strata.ai

Anna Coenen

Anna Coenen

Director of Data Science at The New York Times

Amarita Natt

Amarita Natt

Managing Director, Data Science at Econ One Research

Preethi Raghavan

Preethi Raghavan

VP, Data Science Practice Lead at Fidelity Investments

Jiri Dobes

Jiri Dobes

Head of Solutions at John Snow Labs

Schedule

1.00PM – 1.10PM

Introduction & Housekeeping

1:10pm - 1:40pm

Algorithmic Recommendations at The New York Times

Anna CoenenGroup Manager – Director of Data Science at The New York Times

Recommending news brings a unique set of challenges. This talk addresses how The New York Times has developed its technical infrastructure and algorithms to meet these challenges and shares learnings made along the way.

1:40pm - 2:10pm

How XAI will quietly revolutionize AI

We assume that data holds all the answers to how to automate decisions. To this end, we build data pipelines and train and deploy machine learning models that turn inputs into outputs. But it isn’t that simple. Data holds plenty of answers, but the process needs more guidance to yield models that we can trust to replace/enhance human decision-making. To this end, XAI or Interpretable ML has the right toolset. Trust is mission-critical for any technology, so if AI solutions are to supplant software and humans, AI must reach the reliability standards currently expected from software and humans. For that to happen, XAI will be more widely adopted, but also the roles of data scientist and ML engineer will evolve. We will examine examples of XAI methods and discuss how they can revolutionize the way we train, evaluate and deploy machine learning models.

Serg MasisClimate and Agronomic Data Scientist at Syngenta

2:10pm - 2:40pm

Traditional Software vs Machine Learning Software

The development of smart digital products, based on machine learning, brings new challenges to the table. In contrast to traditional software, the results of a ML model are not deterministic. Thus, the scope of the projects requires us to additionally specify target metrics, such as the expected accuracy of the models. Project management frameworks also need to be adapted to fit data teams, due to the uncertainty and exploratory nature of data science projects. Just as humans do, smart products might fail sometimes. It’s then essential to carefully design these applications, so that errors are transparent, well understood and tolerated by the user. More than ever, ethics is a real concern. Topics such as data protection and model bias need to be intensively discussed and taken into account.

Filipa Castro – Data Scientist at Continental

2.40pm - 3.10pm

Automatic mining of adverse drug reactions from social media posts and unstructured chats

It is estimated that adverse drug reactions (ADR) cost around $30 billion per year in the US alone. Yet, most ADRs remain hidden – as only around 5% of ADRs are reported to the regulator. Marketing authorization holders (pharma companies) are required by the regulator to monitor for suspected ADR in all their own communication channels. This includes web pages under their ownership, discussion of patient groups and special diseases groups, social media accounts they own and operate, and chats in mobile and messaging apps. We present a technology for ADR mining in social media posts and unstructured texts. Each document is first classified for the presence of an ADR. The adverse event is then extracted and semantically related to the corresponding drug. The system scales to handle multiple streaming or batch data sources, supports multiple languages, and delivers state-of-the-art accuracy in recent peer-reviewed papers. It is based on Spark NLP for Healthcare, the most widely used NLP library in the industry.

Jiri Dobes – Head of Solutions at John Snow Labs

3.10pm - 3.25pm

Break

3.25pm - 4.10pm

Model Interpretability and How to Create Trust in AI Products

Serg Masis – Climate and Agronomic Data Scientist at Syngenta

Amarita Natt – Managing Director, Data Science at Econ One Research

Preethi Raghavan – VP, Data Science Practice Lead at Fidelity Investments

Charles Irizarry – CEO & Co-Founder at Strata.ai

4.10pm - 4.40pm

Talk by Matt Denesuk

Matt Denesuk – SVP, Data Analytics & Artificial Intelligence at Royal Caribbean Group

 

4.40pm - 5.10pm

Predictive Maintenance at Illumina

This talk provides an overview of predictive maintenance and preventive analysis for hardware. How Illumina is using ML and Applied Analytics for its predictive maintenance system. The main purpose of predictive maintenance is to prevent unexpected equipment failures. Decrease customer downtime and save time and resources in failure repairs. This talk describes benefits and reasons why companies are investing in Predictive Maintenance. More specifically outlining Machine Learning role in improving this field and how AI and ML can help develop more accurate and automated predictive modeling. Illumina Proactive is a secure and remote instrument performance and proactive support service. We are going to discuss our approach at Illumina and when and how we started this effort.

Yasmin SahafStaff Machine Learning Engineer at Illumina

5.10pm - 5.40pm

Using Semantics in your Data Ecosystem

Data Engineering is exploding, but everyone is just hooking up pipes and triggers. When the next person comes on after your tour of duty, they have to unwind use cases and business logic that never belongs in the ETL script to begin with. There is a better way and it was invented for the semantic web, but almost nobody knows it. In this talk, we look at how to bolt on semantics to your data ecosystem helping to produce fewer bugs, better documented code, true separation of concerns, and faster development.

Andy Ray TerrelVP, Data and Algorithms at Xometry

5.40pm - 5.45pm

Wrap Up

Benefits of joining virtually

  • Improve your data skills in engaging virtual presentations and coffee chats
  • E-meet and connect with leading data scientists through intelligent networking apps
  • Learn how to apply state-of-the art AI and machine learning techniques in the enterprise
  • Ask expert speakers questions in live Q&A sessions
  • Access all sessions on-demand until two weeks after the event
  • Access to the DSS Community Platform

What people are saying

Eduardo Arino de la Rubia Domino Data Lab

“Their conferences are smaller, more intimate, with lots of opportunities for workshops and networking, which helps fill that need in the data science community to get together from time to time.”

David Talby

CTO, John Snow Labs

Eduardo Arino de la Rubia Domino Data Lab

“Data Science SALON is a must-attend event for decision-makers across the data science landscape. The combination of high-quality content and power networking creates a unique opportunity to generate business.”

Matt Denesuk

SVP, Data Analytics & AI, Royal Caribbean Cruises

Eduardo Arino de la Rubia Domino Data Lab

“Both Viacom and Data Science Salon are known for being at the forefront of their fields. We’re proud to provide a platform for DSS, and to host the most relevant conversations on data science in media and entertainment.”

Colleen Fahey Rush

Chief Research Officer, Viacom

Eduardo Arino de la Rubia Domino Data Lab

“The Data Science Salon series is the most important new conversation happening in the industry right now.”

Eduardo Arino de la Rubia

Head of Data Science, Instagram

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For any queries regarding Registration or Tickets, please contact us via info@formulated.by 

 

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Save the date

DSS New York: Applying AI & Machine Learning to Finance & Technology

December 7, 2022 | Hybrid