20–23 April 2020

Monday, 20 April 2020

9:00

9:00–17:00 Monday, 20/04/2020
TBC

10:00

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9:00–17:00 Monday, 20/04/2020
Training
Secondary topics:  Training
Nikki Rouda (Amazon Web Services)
In this workshop, we will walk you through the steps of building a data lake on Amazon S3 using different ingestion mechanisms, performing incremental data processing on the data lake to support transactions on S3, and securing the data lake with fine grained access control policies. Read more.
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9:00–17:00 Monday, 20/04/2020
Training
Secondary topics:  Training
Thomas Nield (Nield Consulting Group, LLC)
There has been an explosion of tools for machine learning, but two have emerged as practical go-to solutions: Scikit-Learn and Apache Spark. Using Python, we will cover examples in parallel (no pun intended!) for both of these tools and learn how to tackle machine learning at small, medium, and large scales. Read more.
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9:00–17:00 Monday, 20/04/2020
Training
Secondary topics:  Training
Hugo Bowne-Anderson (DataCamp)
In this training, attendees will learn the basics of the math and stats they need to know to do data science and interpret their results correctly (the calculus, linear algebra, statistical intuition, probabilistic thinking, among others) through hands-on examples from machine learning, online experiments and hypothesis testing, natural language processing, data ethics, and more. Read more.
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9:00–17:00 Monday, 20/04/2020
Training
Secondary topics:  Training
Grishma Jena (IBM)
Data Science is rapidly changing every industry. This has resulted in a shift away from traditional software development towards data-driven decision making. In this training, we will be using the popular Python to extract, wrangle, explore, and understand data so that we can leverage it in the real world. Read more.

Tuesday, 21 April 2020

9:00

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9:00–17:00 Tuesday, 21/04/2020
1-day training
Secondary topics:  Training
Nathalie Rauschmayr (Amazon Web Services), Satadal Bhattacharjee (Amazon Web Services), Aparna Elangovan (Amazon Web Services)
In this workshop, attendees will build, train and deploy a deep learning model on Amazon SageMaker and they will learn how to use some of the latest SageMaker features such as SageMaker Debugger and SageMaker Model Monitor. Read more.
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9:00–17:00 Tuesday, 21/04/2020
1-day training
Secondary topics:  Training
Dean Wampler (Anyscale)
Surprisingly, there is no simple way to scale up Python applications from your laptop to the cloud. Ray is an open source framework for parallel and distributed computing that makes it easy to program and analyze data at any scale by providing general-purpose high-performance primitives. This training will show how to use Ray to scale up Python applications, data processing, and machine learning Read more.
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9:00–17:00 Tuesday, 21/04/2020
1-day training
Secondary topics:  Training
Alex Thomas (John Snow Labs), Maziyar Panahi (John Snow Labs)
This is a hands-on training covering applying the latest advances in deep learning for common NLP tasks such as named entity recognition, document classification, sentiment analysis, spell checking and OCR. Learn to build complete text analysis pipelines using the highly performant, high scalable, open-source Spark NLP library in Python. Read more.
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9:00–17:00 Tuesday, 21/04/2020
1-day training
Oliver Hughes (Pivotal), Alberto Calleja (Pivotal)
Today's data engineer needs a deep understanding of the key tools and concepts within the vast, rapidly evolving Kubernetes ecosystem. This training will provide developers with a thorough grounding on Kubernetes concepts, suggest best practices and get hands-on with some of the essential tooling. Topics will include Read more.
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9:00–17:00 Tuesday, 21/04/2020
1-day training
Pramod Singh (Walmart Labs ), Akshay kulkarni (Publicis Sapient)
With the latest developments and improvements in the field of deep learning and artificial intelligence, many demanding natural language processing tasks become easy to implement and execute. Text summarization is one of the tasks that can be done using attention networks. Read more.
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9:00–17:00 Tuesday, 21/04/2020
1-day training
Secondary topics:  Training
Janisha Anand (Amazon Web Services), Nikki Rouda (Amazon Web Services)
Learn how to build a serverless data lake on AWS. In the workshop you'll ingest Instacart's online grocery shopping public dataset to the data lake and draw valuable insights on consumer shopping trends. You’ll build data pipelines, leverage data lake storage infrastructure, configure security and governance policies, create a persistent catalog of data, perform ETL, and run ad-hoc analysis. Read more.

17:00

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17:00–18:00 Tuesday, 21/04/2020
Event
Join us after tutorials on Tuesday in the Expo Hall. Grab a drink and mingle with fellow Strata & AI attendees while you check out all of the exhibitors. Read more.

Wednesday, 22 April 2020

8:15

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8:15–8:45 Wednesday, 22/04/2020
Event
Ready, set, network! Meet fellow attendees who are looking to connect at the Strata Data & AI Conference. We'll gather before Wednesday and Thursday keynotes for an informal speed networking event. Be sure to bring your business cards—and remember to have fun. Read more.

10:45

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10:45–19:05 Wednesday, 22/04/2020
Event
O’Reilly Author Book Signings will be held in the O’Reilly booth on Wednesday. This is a great opportunity for you to meet O’Reilly authors and speakers. Read more.

11:15

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11:15–11:55 Wednesday, 22/04/2020
Session
Case Studies
Flávio Santos (Spotify)
Data has been a first-class citizen at Spotify since the beginning. It is an important component of the ecosystem that allows data scientists and analysts to improve features and develop new products. Events collected from instrumented clients and backends go through a complex system before they are available for internal teams. This talk goes deep into how event delivery is built inside Spotify. Read more.
11:15–11:55 Wednesday, 22/04/2020
TBC
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11:15–11:55 Wednesday, 22/04/2020
Session
Streaming
Shradha Ambekar (Intuit)
Data Analysis at scale with fast query response is critical for business needs.Cassandra is a popular datastore used in streaming applications.Cassandra with Spark integration allows running analytical workload but can be slow.Shradha will describe similar challenges faced at Intuit and solutions her team implemented to improve performance by 100X.She also contributed to spark-cassandra-connector. Read more.
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11:15–11:55 Wednesday, 22/04/2020
Rupert Prescot (Elsevier), Jonathan Warner (Elsevier)
The ultimate purpose of data is to drive decisions, but commonly in the real world things aren’t as reliable or accurate as we would like them to be. The main reason why data gets dirty and often unreliable is simple: human intervention. So how do you maintain the reliability of data that is constantly exposed to and updated by your users? Read more.
11:15–11:55 Wednesday, 22/04/2020
TBC
11:15–11:55 Wednesday, 22/04/2020
TBC
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11:15–11:55 Wednesday, 22/04/2020
Anna Gressel (Debevoise & Plimpton LLP), Meeri Haataja (Saidot), Jim Pastore (Debevoise & Plimpton LLP)
The Canadian Government made waves when it passed a law requiring AI impact assessments for automated decision systems. Similar proposals are pending in the US and EU. Anna Gressel, Meeri Haataja, and Jim Pastore unpack what an AI impact assessment looks like in practice and how companies can get started from a technical and legal perspective, and they provide tips on assessing AI risk. Read more.
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11:15–11:55 Wednesday, 22/04/2020
Session
Case Studies
Conor Sayles (Bank of Ireland)
This session will describe how Bank of Ireland has led out a data value realisation strategy, yielding a return of over €70m and incorporating infrastructure investment, agile management and design thinking. An analytic system including Tableau, Teradata, SAS and Cloudera provides a cornerstone for decision-making across multiple functions. Underlying the success is a growing data community. Read more.
11:15–11:55 Wednesday, 22/04/2020
TBC
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11:15–11:55 Wednesday, 22/04/2020
During the last year, the BBC Datalab team has adopted Apache Airflow to improve its recommendation model lifecycle and data processing pipeline. This talk presents lessons learned and includes practical examples, achievements and challenges. It also promotes critical thinking so the audience can be empowered to decide when to use Airflow. Read more.
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11:15–11:55 Wednesday, 22/04/2020
Luyang Wang (Restaurant Brands International(RBI)), Jiao(Jennie) Wang (Intel)
Drive-thru innovation is the big thing in Quick Serving Restaurant (QSR) industry. This talk shows an effective real-time menu recommendation system in this area leveraging cutting-edge deep learning technologies on big data ecosystems to deliver better guest drive-thru experience based on guest order baskets along with other context factors like time of the day, weather condition, etc. Read more.
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11:15–11:55 Wednesday, 22/04/2020
Ward Van Laer (Ixor)
A machine learning solution is only as good as it is deemed by the end-user. More often than not, we do not think through how results are communicated or measured. If we want business- end end-users to trust and correctly interpret AI models, we might need to make our models transparent and understandable. Read more.
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11:15–11:55 Wednesday, 22/04/2020
Session
AI Engineering
Antje Barth (AWS)
Many machine learning systems focus primarily on training models, but leave the users with the task of deploying and re-training their models. In this talk, we’ll discuss the importance of Continuous Machine Learning for improving model performance, and present practical approaches to building continuous model training pipelines using Kubeflow. Read more.
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11:15–11:55 Wednesday, 22/04/2020
Session
AI at the Edge
The advance of the industrial internet of things (IIoT) promised much, particularly in the area of predictive maintenance. Tristan O'Gorman digs into whether or not those promises have been realized. You'll learn about the particular technical and strategic challenges that organizations seeking to adopt predictive maintenance have to overcome. Read more.

12:05

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12:05–12:45 Wednesday, 22/04/2020
Session
Case Studies
Enterprise *IT* has been delivering *BI* on Hadoop for a few years but frustrated business analysts and data scientists now want self-service data & ML in the cloud, so they can go much faster. This session explores the challenges encountered when enterprise IT teams have to quickly pivot; from caring for an elephant on-premise to farming herds of clusters, pipelines and models in clouds. Read more.
12:05–12:45 Wednesday, 22/04/2020
TBC
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12:05–12:45 Wednesday, 22/04/2020
Session
Streaming
Scott Kidder (Mux)
Learn how the Mux Data service has leveraged Kafka and Go to build stateful stream-processing applications that operate on extremely high-volumes of video-view beacons to drive real-time monitoring dashboards and historical metrics representing a viewer’s quality-of-experience. We’ll also cover fault-tolerance, monitoring, and Kubernetes container deployments. Read more.
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12:05–12:45 Wednesday, 22/04/2020
Andy Petrella (Kensu)
Recent papers from Google and the European Commission emphasized the need for solutions to monitor data quality & lineage. Enforced by our experience, we want to highlight three advantages for monitoring in production: - Boost efficiency of data processes - Increase confidence in models in real-time - Ensure accountability to fulfill policies Read more.
12:05–12:45 Wednesday, 22/04/2020
TBC
12:05–12:45 Wednesday, 22/04/2020
TBC
12:05–12:45 Wednesday, 22/04/2020 TBC
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12:05–12:45 Wednesday, 22/04/2020
Session
Case Studies
Kumar Sambhav (Barclays)
People Analytics has become key to unlocking human resource insights to understand and measure policy effectiveness and implement improvements by embedding intelligent decision making in the processes. In this session, I will talk about the pipeline we have developed, and corresponding controls and governance model implemented to support various People Analytics use-cases in Barclays. Read more.
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12:05–12:45 Wednesday, 22/04/2020
Session
Applied ML
Eitan Anzenberg (Bill.com)
Although the field of optical character recognition (OCR) has been around for half a century, document parsing and field extraction from images remains an open research topic. We utilize an end-to-end deep learning architecture that leverages document understanding to extract fields of interest. Read more.
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12:05–12:45 Wednesday, 22/04/2020
Siyao Meng (Cloudera), Wei-Chiu Chuang (Cloudera)
Distributed tracing is a well known technique for identifying where failures occur and the reason behind poor performance, especially for complex systems like Hadoop which involves many different components. We are small team at Cloudera working on integrating OpenTracing in Hadoop ecosystem. We would like to present a demo of our current work and talk about our future integration plan. Read more.
12:05–12:45 Wednesday, 22/04/2020
TBC
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12:05–12:45 Wednesday, 22/04/2020
Dan Sullivan (New Relic)
ML models may perform as expected from a reliability and scalability perspective, but make poor decisions that cost sales and trust. In worst-case scenarios, decisions may violate policies and government regulations. In this talk, attendees will learn techniques for identifying bias, leveraging explainability methods to measure compliance and incorporating these techniques into DevOps practices. Read more.
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12:05–12:45 Wednesday, 22/04/2020
Session
AI Engineering
Oliver Gindele (Datatonic)
Productionising machine learning pipelines can be a daunting and difficult task for Data Scientists. In this session, we will review some of the newest technologies that address that issue and we explain how we used them to productionise a serverless ML pipeline in an exciting case study. Read more.
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12:05–12:45 Wednesday, 22/04/2020
Session
AI at the Edge
Philip Kendall (Intercept IP)
Philip Kendall offers a look at the challenges involved in training and deploying a unique model to each of tens of thousands of Arduino-class IoT devices to minimize power use and maximize lifetime. The solution involves a high-level simulation of the system on the backend to perform the training and a custom virtual machine on the device to implement the learned model. Read more.

12:45

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12:45–14:05 Wednesday, 22/04/2020
Event
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics. Read more.

14:05

14:05–14:45 Wednesday, 22/04/2020
Session
Case Studies
TBC
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14:05–14:45 Wednesday, 22/04/2020
Ted Dunning (MapR)
Data pipelines are fast becoming a standard fixture in modern systems, but how to build and maintain them isn't nearly as widely known as, say, building a data warehouse. I will describe the core building blocks of such pipelines and how to use tools such as TensorFlow (extended), scikit, Apache Flink and Apache Beam to build, maintain and monitor them. Read more.
14:05–14:45 Wednesday, 22/04/2020
TBC
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14:05–14:45 Wednesday, 22/04/2020
Abhishek Somani (Qubole), Shubham Tagra (Qubole), V Rajkumar (Qubole)
An open-source framework for Apache Hive, Apache Spark and Presto that provides cross engine ACID transactions, and enables performant and cost-effective Updates and Deletes on Big Data Lakes on the cloud. Read more.
14:05–14:45 Wednesday, 22/04/2020
TBC
14:05–14:45 Wednesday, 22/04/2020
TBC
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14:05–14:45 Wednesday, 22/04/2020
Maurice Coyle (Truata)
Is customer trust dead? Trūata’s Chief Data Scientist Dr. Maurice Coyle looks at this question and explores some of the myths around the usage of personal data and consumer privacy. This session will debunk some of the most common data privacy myths as well as sharing valuable insights into the effective use of data for insights-driven organizations. Read more.
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14:05–14:45 Wednesday, 22/04/2020
Session
Case Studies
bhargavi reddy (NETFLIX INC)
This talk will cover the driving forces for effective Data Lifecycle Management (DLM) at Netflix, current state of Netflix’s S3 data warehouse, overview of S3 access logs collection process using SQS and Apache Iceberg, and how the S3 logs are being used for improving the efficiency and security posture of our cloud infrastructure at scale in the DLM realm. Read more.
14:05–14:45 Wednesday, 22/04/2020
Session
Applied ML
TBC
14:05–14:45 Wednesday, 22/04/2020
TBC
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14:05–14:45 Wednesday, 22/04/2020
Alon Nir (Deliveroo)
This talk will introduce network analysis and show what a powerful and impactful tool it is. Using plethora of real world examples and friendly Python syntax, audience members will be equipped - and hopefully inspired - to start their journey with this network analysis! Read more.
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14:05–14:45 Wednesday, 22/04/2020
Richard Sargeant (Faculty)
Firms and government have become more aware of the risk of "black-box" algorithms that "work," but in an opaque way. Existing laws and regulations merely stipulate what ought to be the case and not to achieve it technically. Richard Sargeant is joined by leading figures from law, technology, and businesses to interrogate this subject. Read more.
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14:05–14:45 Wednesday, 22/04/2020
Session
AI Engineering
Robert Drysdale (Accenture The Dock)
A look into building, training & deploying Machine Learning & Deep Learning Models on the main cloud platforms (AWS, Azure, GCP) and agnostically. Read more.
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14:05–14:45 Wednesday, 22/04/2020
Magaly Alonzo (Elter)
Time series are a particular type of data for one purpose, time. Because of this single property, time-series needs a very specific kind of neural network that necessitates memory. This presentation will first make an overview of what time series are and their properties. Finally we’ll have a brief introduction to recurrent neural nets a particular architecture designed for this purpose. Read more.

14:55

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14:55–15:35 Wednesday, 22/04/2020
Session
Case Studies
Gabor Kotalik (Deutsche Telekom), Vaclav Surovec (Deutsche Telekom)
Deutsche Telekom is 4th biggest telecommunication company in the world, and every day millions of our customers are using their mobile services in roaming. This presentation is about how we designed and built our machine learning processes on top of Cloudera Hadoop cluster to support commercial roaming business at Deutsche Telekom Global Carrier. Read more.
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14:55–15:35 Wednesday, 22/04/2020
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Managing production machine learning systems at scale has uncovered new challenges that require fundamentally different approaches to traditional software engineering or data science. Alejandro Saucedo explores ML Ops, a concept that often encompasses the methodologies to continuously integrate, deploy and monitor machine learning in production at massive scale. Read more.
14:55–15:35 Wednesday, 22/04/2020
TBC
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14:55–15:35 Wednesday, 22/04/2020
Jeff Evans (StreamSets)
Spark is a powerful tool for data processing, but can it do slowly changing dimensions? The answer is yes, with some thoughtful use of its capabilities. And thanks to Spark’s built-in features, we aren’t limited to databases when it comes to handling deltas and persisting historical changes in records. Live demos will be included throughout to help reinforce the concepts discussed. Read more.
14:55–15:35 Wednesday, 22/04/2020
TBC
14:55–15:35 Wednesday, 22/04/2020
TBC
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14:55–15:35 Wednesday, 22/04/2020
Daniel Huss (gravityAI)
Many types of algorithms have become commoditized, yet companies continue to use tight resources to try to build these in-house all the time. Considering that according to Gartner, nearly 9 out of 10 (87%) internal data science projects fail to make it into production, it's crazy to focus resources on anything but the most proprietary of projects. How do you decide where to focus? Read more.
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14:55–15:35 Wednesday, 22/04/2020
Session
Case Studies
Martin Goodson (Evolution AI)
Automating mundane back-office tasks has been a long-standing headache for businesses under pressure to increase efficiency. Recent breakthroughs in computer vision and machine learning finally allow the automation of time-consuming document processing tasks. Dr Martin Goodson gives an account of successful AI projects that are automating tasks at RBS and Dun & Bradstreet. Read more.
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14:55–15:35 Wednesday, 22/04/2020
Session
Applied ML
Giacomo Bernardi (Extreme Networks)
Machines talk among them! What can we learn about their behaviour by analysing their "language"? In this talk we present a lightweight approach for securing large IoT deployments by leveraging modern Natural Language Processing techniques. Rather than attempting cumbersome firewall rules, we argue that IoT deployments can be efficiently secured by online behavioural modelling. Read more.
14:55–15:35 Wednesday, 22/04/2020 TBC
14:55–15:35 Wednesday, 22/04/2020
TBC
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14:55–15:35 Wednesday, 22/04/2020
Morgan Gregory (Google)
The adoption of AI is accelerating at an increasing pace. We’re reaping many benefits from the advancement of AI, but we’re also seeing hints of the unintended harm that occurs when responsibility isn’t front and center. It’s critical for us to understand how and why this happens so we can build our future responsibly, with AI that is fair, safe, trustworthy, and green. Read more.
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14:55–15:35 Wednesday, 22/04/2020
Session
AI Engineering
Adam Blum (Auger AI)
First generation AutoML was targeted to business analysts and "citizen data scientists": upload data to the service, watch the leaderboard, pick a winning model. Second generation of AutoML (from Microsoft, Google and updates to earlier AutoML tools) is targeted to developers and covers the full AutoML lifecycle. We show how such tool transform applications by replacing logic with predictions Read more.
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14:55–15:35 Wednesday, 22/04/2020
Jonny Hancox (NVIDIA)
Federated Learning is a relatively new technique pioneered to allow much larger datasets to be used to train machine learning models but without the need to share potentially sensitive data. This makes the technique ideal for the healthcare sector in which patient data is highly sensitive but there is a huge need to increase the amount of training data to get models to clinically-viable levels. Read more.

16:35

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16:35–17:15 Wednesday, 22/04/2020
Session
Case Studies
Ben Sykes (Netflix)
Ensuring a consistently great Netflix experience while continuously pushing innovative technology updates is no easy feat. We'll look at how Netflix turns log streams into real-time metrics to provide visibility into how devices are performing in the field. Including sharing some of the lessons learned around optimizing Druid to handle our load. Read more.
16:35–17:15 Wednesday, 22/04/2020
TBC
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16:35–17:15 Wednesday, 22/04/2020
Session
Streaming
Elias Nema (OLX Group)
OLX Group group includes 20+ brands, more than 350M monthly active users, and millions of new items added to a platform daily. Of course, recommender systems play a crucial part in our platform. This session highlights the data flows and core components used for building, serving and continuously iterating over recommenders in such a dynamic marketplace. Read more.
16:35–17:15 Wednesday, 22/04/2020
TBC
16:35–17:15 Wednesday, 22/04/2020
TBC
16:35–17:15 Wednesday, 22/04/2020
TBC
16:35–17:15 Wednesday, 22/04/2020
TBC
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16:35–17:15 Wednesday, 22/04/2020
Session
Case Studies
Kelly Carmody (Dramatic Solutions), Yaakov Bressler (Dramatic Solutions)
Dynamic pricing, adjusting a price to meet its market value, implemented properly by Broadway, the West End, and smaller Theatres, shows promise of increasing revenue while selling more tickets and lowering prices. Yaakov Bressler and Kelly Carmody discuss their work proving the statistics behind dynamic pricing using probability distributions and a variety of modelling techniques in Python. Read more.
16:35–17:15 Wednesday, 22/04/2020
TBC
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16:35–17:15 Wednesday, 22/04/2020
Wojciech Biela (Starburst), Karol Sobczak (Starburst)
Presto, the open source SQL engine for Big Data, offers high concurrency, low-latency queries across multiple data sources within one query. With Kubernetes, you may easily deploy and manage Presto clusters across hybrid and multi cloud environment with built-in high availability, autoscaling and monitoring. Available now on RedHat OpenShift and Kubernetes Engines from AWS, Google Cloud, Azure. Read more.
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16:35–17:15 Wednesday, 22/04/2020
Ken Johnston (Microsoft), Ankit Srivastava (Microsoft)
Today, normal growth isn't enough—you need hockey-stick levels of growth. Sales and marketing orgs are looking to AI to "growth hack" their way to new markets and segments. Ken Johnston and Ankit Srivastava explain how to use mutual information at scale across massive data sources to help filter out noise and share critical insights with new cohort of users, businesses, and networks. Read more.
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16:35–17:15 Wednesday, 22/04/2020
Hatem Hajri (Institut de recherche technologique SystemX)
Adversarial machine learning studies vulnerabilities of machine learning algorithms in adversarial settings and develops techniques to make learning more robust to adversarial examples. Hatem Hajr outlines adversarial machine learning and illustrates a new approach to address the problem of adversarial examples based on probabilistic techniques. Read more.
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16:35–17:15 Wednesday, 22/04/2020
Julien Simon (AWS)
Julien Simon offers an overview of graph neural networks (GNNs), one of the most exciting developments in machine learning today. You'll discuss real-life use cases for which GNNs are a great fit and get started with GNNs using the Deep Graph Library, an open source library built on top of Apache MXNet and PyTorch. Read more.
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16:35–17:15 Wednesday, 22/04/2020
Session
AI at the Edge
Anthony Joseph (My House Geek)
IoT devices are increasing in power and capability, now allowing developers to use machine learning models on the device. Anthony Joseph analyzes a boxing training session with motion sensors onboard IoT devices using the TensorFlow framework and provides user feedback on technique and speed. Read more.

17:25

17:25–18:05 Wednesday, 22/04/2020
Session
Case Studies
TBC
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17:25–18:05 Wednesday, 22/04/2020
Laura Froelich (DHI Water & Environment), Rasmus Halvgaard (DHI), Shuna Rana Nazari
We combine traditional predictive models with deep learning methods to improve operation of waste water treatment plants. This data-driven approach relies on weather radar data that replaces local and often sparsely located rain gauge sensor stations. Our approach allows for fast and probabilistic forecasts that robustly improve real-time operation of the urban drainage system. Read more.
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17:25–18:05 Wednesday, 22/04/2020
Session
Streaming
Sijie Guo (StreamNative)
Apache Pulsar as a cloud-native event streaming platform gains more and more adoptions in mission critical services due to its stronger consistency and durability guarantees. This presentation deep dives into the technical details driven the Pulsar adoption trend and showcases the real world example on using Apache Pulsar to process billions of transactions every day. Read more.
17:25–18:05 Wednesday, 22/04/2020
TBC
17:25–18:05 Wednesday, 22/04/2020
TBC
17:25–18:05 Wednesday, 22/04/2020
TBC
17:25–18:05 Wednesday, 22/04/2020
TBC
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17:25–18:05 Wednesday, 22/04/2020
Session
Case Studies
Mike Lutz (Samtec)
Netflix proposed a novel best-practice in using Jupyter notebooks as glue for working in the "Big Data"/AI-processing domain - this presentation will follow a manufacturing companies adventure in trying to to implement Netflix's ideas, but on a dramatically smaller scale - working through how their idea can be useful even for the Small Fry. Read more.
17:25–18:05 Wednesday, 22/04/2020
TBC
17:25–18:05 Wednesday, 22/04/2020
TBC
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17:25–18:05 Wednesday, 22/04/2020
Marko Letic (Mozilla)
Did you know that the beginnings of data visualization are strongly tied to solving some of the biggest problems humanity has ever faced? Wouldn’t it be more interesting to say that you’re not a doctor, but you do save lives than to say you’re just a developer? If you want to know more, join me on this trip through time and beyond. Read more.
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17:25–18:05 Wednesday, 22/04/2020
walid daboubi (Richemont)
Traditional cybersecurity processes are by definition reactive, in that they are based on a set of rules. In this session, we will share how we made our cybersecurity approach more proactive by applying machine learning on a set of concrete use cases. Read more.
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17:25–18:05 Wednesday, 22/04/2020
Meher Kasam (Square), Anirudh Koul (Microsoft), Siddha Ganju (NVIDIA)
Meher Kasam, Anirudh Koul, and Siddha Ganju highlight the must-have checklist for everyday AI practitioners to speed up your deep learning training and inference with TensorFlow code examples. Read more.
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17:25–18:05 Wednesday, 22/04/2020
Session
AI at the Edge
Alasdair Allan (Babilim Light Industries)
The future of machine learning is on the edge and on small embedded devices. Over the last year custom silicon, intended to speed up machine learning inferencing on the edge, has started to appear. No cloud needed. We evaluate the new silicon, looking not just at inferencing speed, but also at heating, cooling, and the overall power envelope needed to run it. Read more.

18:05

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18:05–19:05 Wednesday, 22/04/2020
Event
Make your way from booth to booth while you check out all the exhibitors in the Expo Hall on Wednesday after sessions end. Read more.

Thursday, 23 April 2020

8:15

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8:15–8:45 Thursday, 23/04/2020
Event
Ready, set, network! Meet fellow attendees who are looking to connect at the Strata Data & AI Conference. We'll gather before Wednesday and Thursday keynotes for an informal speed networking event. Be sure to bring your business cards—and remember to have fun. Read more.

10:45

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10:45–16:35 Thursday, 23/04/2020
Event
O’Reilly Author Book Signings will be held in the O’Reilly booth on Thursday. This is a great opportunity for you to meet O’Reilly authors and speakers. Read more.

11:15

11:15–11:55 Thursday, 23/04/2020
Session
Case Studies
TBC
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11:15–11:55 Thursday, 23/04/2020
Holden Karau (Independent), Trevor Grant (IBM)
We'll show you a way to get & keep your models in production with Kubeflow. Read more.
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11:15–11:55 Thursday, 23/04/2020
Session
Streaming
Jason Bell (Independent Speaker)
Apache Pulsar gives us the same robust realtime messaging capabilities as Kafka. In this talk Jason Bell looks at the challenges of migrating from an existing Kafka cluster to Apache Pulsar, what considerations to make with brokers, topics, retention, consumers and producers. Read more.
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11:15–11:55 Thursday, 23/04/2020
Session
Governance
Anna Gressel (Debevoise & Plimpton LLP), Jim Pastore (Debevoise & Plimpton LLP), Florian Ostmann (The Alan Turing Institute)
This is a crash course on the emerging ethical and regulatory issues surrounding AI in FinTech. It will offer insights from recent statements by U.S. and U.K. regulators in the banking and financial services industries, and examine their priorities in 2020. It will also provide practical guidance on how companies can mitigate ethical and legal risks and position their AI products for success. Read more.
11:15–11:55 Thursday, 23/04/2020
TBC
11:15–11:55 Thursday, 23/04/2020
TBC
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11:15–11:55 Thursday, 23/04/2020
Simon Lidberg (Microsoft), Benjamin Wright-Jones (Microsoft)
DevOps, DevSecOps, AIOps, ML Ops, Data Ops, No Ops....Ditch your confusion and join Simon Lidberg and Benjamin Wright-Jones to understand what DevOps means for AI and your organization. Read more.
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11:15–11:55 Thursday, 23/04/2020
Session
Case Studies
Andras Szabo (Pivigo), Adam Hill (HAL24K)
Wildfires are a major environmental and health risk, with a frequency that has increased dramatically in the past decade. Early detection is critical, however most often wildfires are only discovered by eye-witness accounts. In this talk we will tell about a data science partnership between HAL24K and Pivigo aimed at building an automated wildfire detection system using NOAA satellite data. Read more.
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11:15–11:55 Thursday, 23/04/2020
Session
Applied ML
Brandy Freitas (Pitney Bowes)
In this session, Brandy Freitas will cover the interplay between graph analytics and machine learning, improved feature engineering with graph native algorithms, and outline the current use of graph technology in industry. Read more.
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11:15–11:55 Thursday, 23/04/2020
Jacques Nadeau (Dremio)
This talk will serve as review of how to build a successful cloud data lake. It will cover key topics such as landing, etl, security cost/performance tradeoffs and access patterns as well as technologies such as Apache Arrow, Iceberg and Spark in the context of real world customer deployments. Read more.
11:15–11:55 Thursday, 23/04/2020
TBC
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11:15–11:55 Thursday, 23/04/2020
Session
AI Engineering
Thunder Shiviah (Databricks), Cyrielle Simeone (Databricks)
Thunder Shiviah and Cyrielle Simeone dive into MLflow, an open source platform from Databricks, to manage the complete ML lifecycle, including experiment tracking, model management, and deployment. With over 140 contributors and 800,000 monthly download on PyPi, MLflow has gained tremendous community adoption, demonstrating the need for an open source platform for the ML lifecycle. Read more.
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11:15–11:55 Thursday, 23/04/2020
Around the world, IKEA has an ever-growing number of loyalty club (Family) members. An important part of IKEA’s ongoing digital transformation is to improve communication with these customers and to inspire them with offers that are most relevant for improving their everyday life. Kim Falk shares IKEA's work on personalizing promotional emails. Read more.

12:05

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12:05–12:45 Thursday, 23/04/2020
Session
Case Studies
Criteo's infrastructure provides capacity and connectivity to host Criteo’s platform and applications. The evolution of our infrastructure is driven by the ability to forecast Criteo's traffic demand. In this talk, we explain how Criteo uses Bayesian Dynamic time series models to accurately forecast its traffic load and optimize hardware resources across data centers. Read more.
12:05–12:45 Thursday, 23/04/2020
TBC
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12:05–12:45 Thursday, 23/04/2020
Session
Streaming
Itai Yaffe (Nielsen)
At Nielsen Marketing Cloud, we leverage Apache Druid to provide our customers (marketers and publishers) real-time analytics tools for various use-cases, including in-flight analytics, reporting and building target audiences. In this talk, we will discuss advanced Druid techniques, such as efficient ingestion of billions of events per day, query optimization, and data retention and deletion. Read more.
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12:05–12:45 Thursday, 23/04/2020
Session
Governance
Dean Wampler (Anyscale)
Production deployment of ML models requires Data Governance, because models are data. This session justifies that claim, then explores its implications and techniques for satisfying the requirements. Using motivating examples, we’ll explore reproducibility, security, traceability, and auditing, plus some unique characteristics of models in production settings. Read more.
12:05–12:45 Thursday, 23/04/2020
TBC
12:05–12:45 Thursday, 23/04/2020
TBC
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12:05–12:45 Thursday, 23/04/2020
Kevin Kim (Socar)
Socar, one of the largest car sharing fleet operator in the world, has been seriously focused on data operations. You can hear how Socar is redefining car sharing industry with data science by experiment-based pricing strategy, machine-learning based demand prediction, optimized car management, accident risk profiling, and many more. Read more.
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12:05–12:45 Thursday, 23/04/2020
Session
Case Studies
Rick Houlihan (Amazon Web Services)
When Amazon decided to migrate thousands of application services to NoSQL, many of those services required complex relational models that could not be reduced to simple key-value access patterns. The most commonly documented use cases for NoSQL are simplistic. this session shows how to model complex relational data efficiently in denormalized structures. Read more.
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12:05–12:45 Thursday, 23/04/2020
Session
Applied ML
Jonathan Leslie (Pivigo)
MADE.com are a furniture and homewares retailer with a unique online-only business model. Given this format, it is crucial that customer service agents are able to respond to queries quickly and accurately. However, it can often be difficult to match the demand of incoming requests. We will tell about a project aimed developing a framework for automated responses to customer queries. Read more.
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12:05–12:45 Thursday, 23/04/2020
Francesco Mucio (francescomuc.io)
Please sit down and play with us the Data Engineering worst practices bingo. From cloud infrastructure to stream processing, from data lakes to analytics come to see what can go wrong and what was the reasoning behind these decision. After collecting stories for almost 20 years, it is finally time to give back. And if you recognize your organization in some of them, well we told you to sit down. Read more.
12:05–12:45 Thursday, 23/04/2020
TBC
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12:05–12:45 Thursday, 23/04/2020
Session
NLP
NLP tasks using supervised ML perform poorly where conversational context is involved. This session will cover the implementation of deep reinforcement learning in NLP as a coherent and better predictor in handling problems like Q&A , dialogue generation ,and article summarisation by simulation of two agents taking turns that explore state-action space and learning a policy. Read more.
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12:05–12:45 Thursday, 23/04/2020
Session
AI Engineering
MELANIE LAFFIN (Booz Allen Hamilton)
Traditional automation is typically limited to clear-cut business rules that can be easily programmed. We expand what automation can do by adding eyes (computer vision), a brain (general AI models) and speech (natural language processing) to our automations to enhance the ability of our automations Read more.
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12:05–12:45 Thursday, 23/04/2020
Miguel Martínez (NVIDIA)
GPU acceleration has been at the heart of scientific computing and artificial intelligence for many years now. Since the launch of RAPIDS last year, this vast computational resource has become available for data science workloads too. The RAPIDS framework is a GPU-accelerated drop-in replacement for utilities such as Pandas, Scikit-Learn, NetworkX and XGBoost. Read more.

12:45

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12:45–14:05 Thursday, 23/04/2020
Event
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics. Read more.

14:05

14:05–14:45 Thursday, 23/04/2020
Session
Case Studies
TBC
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14:05–14:45 Thursday, 23/04/2020
Robert Crowe (Google)
Production ML must address issues of modern software methodology, as well as issues unique to ML. Different types of ML have different requirements, often driven by the different data lifecycles and sources of ground truth. Implementations often suffer from limitations in modularity, scalability, and extensibility. We discuss production ML applications, and review TensorFlow Extended (TFX). Read more.
14:05–14:45 Thursday, 23/04/2020
TBC
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14:05–14:45 Thursday, 23/04/2020
Session
Governance
Ella Fitzsimmons, Sarah Gold (Projects by IF)
People care about how data about them is used. Building trust with consumers will require a change in how services treat data. Since 2016, IF has curated a data patterns catalogue which is used by product teams around the world. We’ll show how patterns help teams build digital services that give people agency over data, build trust and start addressing systemic balances of power. Read more.
14:05–14:45 Thursday, 23/04/2020
TBC
14:05–14:45 Thursday, 23/04/2020
TBC
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14:05–14:45 Thursday, 23/04/2020
Asif Jan (Roche)
Advances in AI/ML are critical to advancing our understanding of the disease and to bring better and more efficacious treatments to patients realising the dream of personalized healthcare. In this talk I will share lessons learnt from building data science teams in Pharma, and outline roadmap for success of AI/ML in Pharma industry. Read more.
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14:05–14:45 Thursday, 23/04/2020
Session
Case Studies
Rainer Hoffmann (EnBW AG), Frank Säuberlich (EnBW AG)
Almost two years ago we at EnBW developed our core beliefs for the role of AI at EnBW and derived concrete actions that need to be taken in order to scale our AI activities. In our talk, we will give an overview on those aspects and will describe the challenges we have been facing on our journey so far. Further, we will describe the particular approaches we took to master these challenges. Read more.
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14:05–14:45 Thursday, 23/04/2020
Session
Applied ML
Davin Kaing (IBM)
What is driving revenue? How can we improve our client experience? These are causal questions that many organizations face. Answering these questions using data can be challenging, especially since in most cases, only observational data are available. We will go through an overview of both traditional and modern causal inference techniques and address their limitations and applications. Read more.
14:05–14:45 Thursday, 23/04/2020
TBC
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14:05–14:45 Thursday, 23/04/2020
Yoav Einav (GigaSpaces)
More enterprises are using big data for better business decision-making but existing infrastructure lacks the needed performance and scale to support growing requirements for real-time analysis and visualization of operational data. This session will propose how enterprises can achieve * BI Visualization on fresh data for real-time dashboards * Low latency response time when generating reports Read more.
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14:05–14:45 Thursday, 23/04/2020
Session
NLP
Nipun Sadvilkar (Episource LLC)
Episource is building a Clinical NLP engine to extract medical facets from medical charts to automate coding in claims submissions. They use medical coder's expertise to review highlighted clinical entities and their auto-suggested ICD10 codes. Nipun will talk about building a key component of Episource's Clinical NLP engine - Clinical NER, from data annotation to models and techniques. Read more.
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14:05–14:45 Thursday, 23/04/2020
Bargava Subramanian (Binaize), Amit Kapoor (narrativeVIZ)
Bargava Subramanian and Amit Kapoor use two real-world examples to show how you can quickly build visual data products using TensorFlow.js to address the challenges inherent in understanding the strengths, weaknesses, and biases of your models as well as involving business users to design and develop a more effective model. Read more.
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14:05–14:45 Thursday, 23/04/2020
Session
Computer vision
Mary Wahl (Microsoft), Ye Xing (Microsoft)
With the increasing availability of massive high-resolution aerial imagery, the geospatial information system community and the computer vision (CV) community joined forces in the new field of "geo AI." Mary Wahl and Ye Xing introduce you to this new field with live demos and sample code for common AI applications to aerial imagery from both commercial and government use cases. Read more.

14:55

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14:55–15:35 Thursday, 23/04/2020
Session
Case Studies
Lukumon Oyedele (University of the West of England)
In this talk, the use of conversational-AI and Augmented Reality to interact with BIM will be presented. The aim is to make it possible for onsite construction workers to seek support from BIM through the use of verbal query and augmented display. Read more.
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14:55–15:35 Thursday, 23/04/2020
Charu Jaiswal (Integrate.ai)
You train ML models and deploy them into the wild. What next? The performance of your models will decrease over time as business operations and customer behaviours change. You may only notice months later, incurring costly results. In this session, the audience will learn how to fight back against performance loss by monitoring, testing, and retraining ML models actively in production. Read more.
14:55–15:35 Thursday, 23/04/2020
TBC
14:55–15:35 Thursday, 23/04/2020
TBC
14:55–15:35 Thursday, 23/04/2020
TBC
14:55–15:35 Thursday, 23/04/2020
TBC
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14:55–15:35 Thursday, 23/04/2020
LOMIT Patel (IMVU)
The future of customer acquisition rests on the shoulders of leveraging intelligent machines, orchestrating complex campaigns across key marketing platforms—dynamically allocating budgets, pruning creatives, surfacing insights, and taking actions powered by AI. Lomit Patel shows you how to use AI and machine learning (ML) to provide an operational layer to deliver meaningful results. Read more.
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14:55–15:35 Thursday, 23/04/2020
Session
Case Studies
Nutsa Abazadze (TBC Bank), Aleksandre Lomadze (TBC Bank)
We will tell you how our failed attempt to build an ML model brought us to discovering institutional problems and kicked off improvement of existing business processes so that we would collect quality data for future modeling; and how we still managed to increase deposit profitability by 20% in the process. Read more.
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14:55–15:35 Thursday, 23/04/2020
Session
Applied ML
Fredrik Schlyter (Nicknamed)
Finvoice started out as a small project consisting of one machine learning engineer and 50 invoices, today Finvoice is used by companies that scan over 80 million invoices per year. This session highlights how machine learning can be used to capture payment information on invoices and how we expanded from a cloud-based API solution to doing the inference directly on customers mobile phones. Read more.
14:55–15:35 Thursday, 23/04/2020
TBC
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14:55–15:35 Thursday, 23/04/2020
Shradha Ambekar (Intuit), Sunil Goplani (Intuit)
Imagine a business metric showing a sudden spike. Debugging data pipelines is non-trivial and finding the root cause can take hours to days! We’ll share how Intuit built a self-serve tool that automatically discovers data pipeline lineage and applies anomaly detection to proactively detect and help debug issues in minutes–establishing trust in metrics and improve developer productivity by 10-100X. Read more.
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14:55–15:35 Thursday, 23/04/2020
Session
NLP
Barbara Fusinska (Google)
Natural language processing (NLP) offers techniques to gain insight from and generate text data. Barbara Fusinska introduces you to NLP concepts and deep learning architectures using document context. You'll see a series of demos with TensorFlow from classification task to text generation. Read more.
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14:55–15:35 Thursday, 23/04/2020
marcel blattner (Tamedia)
We still lack a clear understanding of how deep learning neural networks learn. Theoretical physics can provide some tools to gain more intuition and insights about generalization and model robustness. I this talk I provide an overview of ongoing research and first promising and applicable results. Read more.
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14:55–15:35 Thursday, 23/04/2020
Session
Computer vision
Angus Taylor (Microsoft), Patrick Buehler (Microsoft)
Training and deployment of deep neural networks for computer vision (CV) in realistic business scenarios remains a challenge for both data scientists and engineers. Angus Taylor and Patrick Buehler dig into state-of-the-art in the CV domain and provide resources and code examples for various CV tasks by leveraging the Microsoft CV best-practices repository. Read more.

16:35

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16:35–17:15 Thursday, 23/04/2020
Session
Case Studies
Jennifer Yang (Wells Fargo ECS)
Traditional rule-based data quality management methodology is costly and poorly scalable. It requires subject matter experts within business, data and technology domains. The presentation will discuss a use case that demonstrates how the machine learning techniques can be used in the data quality management on the big data platform in the financial industry. Read more.
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16:35–17:15 Thursday, 23/04/2020
Debasish Ghosh (Lightbend ), Stavros Kontopoulos (Lightbend)
In this talk, we discuss online machine learning algorithm choices for streaming applications. We motivate the discussion with resource constrained use cases like IoT and personalization. We cover drift detection algorithms and Hoeffding Adaptive Trees, performance metrics for online models and practical concerns with deployment in production. We also provide code examples for each technique. Read more.
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16:35–17:15 Thursday, 23/04/2020
Session
Streaming
Naghman Waheed (Bayer Crop Science)
IT information systems have been a key enabler for our business in a very competitive environment. As the complexity of our business has grown so has the need to provide data for real-time business analytics and BI. A unique architecture has been setup to stream data out of our SAP ERP using SAP SLT and KAFKA enabling business to make decision based on real-time events. Read more.
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16:35–17:15 Thursday, 23/04/2020
Session
Governance
Majken Sander (Majken Sander)
Schools and society in general need to focus on educating the citizens to raise their digital awareness. Companies need to be building the data literacy competencies of their employees. And the digital economy strategy of the companies should include data ethics and might also chose to embrace it as a competitive edge gained via branding value. Read more.
16:35–17:15 Thursday, 23/04/2020
TBC
16:35–17:15 Thursday, 23/04/2020
TBC
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16:35–17:15 Thursday, 23/04/2020
Victor Gonzalez (ConCredito)
Victor Gonzalez explores the way the fintech ecosystem comes to change the rules of the finantial services industry in México. The ConCrédito digital transformation driven by data project is the basis for the growth and scope of business objectives, the current business model needed to migrate from the traditional model to digital processes that allow us to be in the hands of our customers. Read more.
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16:35–17:15 Thursday, 23/04/2020
Session
Case Studies
Kim Nilsson (Pivigo), Robert Grieg-Gran (Mindful Chef)
Mindful Chef is a health-focused company that delivers weekly recipe boxes. In order to create a more personalised experience for their customers, they teamed up with Pivigo to develop an innovative recommender system. In this talk we will tell about this project and the development of a novel approach to understanding user taste that had an unexpectedly large impact on recommendation accuracy. Read more.
16:35–17:15 Thursday, 23/04/2020
TBC
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16:35–17:15 Thursday, 23/04/2020
Lambda architecture is a general purpose architecture for data platforms. It has been known for a while, but was always hard to implement. With the release of Delta Lake tables and after Spark Structured Streaming became mature, Lambda architecture has gotten a completely new breath, and can now be done in a much easier way than ever before for various analytical and machine learning use cases. Read more.
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16:35–17:15 Thursday, 23/04/2020
Swasti Kakker (LinkedIn), Manu Ram Pandit (LinkedIn), Navneet Verma (Linkedin)
Come and learn the challenges we overcame to make Darwin (Data Analytics and Relevance Workbench at LinkedIn) a reality. Know about how data scientists, developers, and analysts at LinkedIn can share their notebooks with their peers, author work in multiple languages, have their custom execution environments, execute long-running jobs, and do much more on a single hosted notebooks platform. Read more.
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16:35–17:15 Thursday, 23/04/2020
Session
NLP
Markus Ludwig (Scout24)
Markus Ludwig shares insights from training and deploying a Transformer model that translates natural language to structured search queries. You'll cover the entire journey from idea to product, from teaching the model new tricks to helping it forget bad habits, and iteratively refine the user experience. Read more.
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16:35–17:15 Thursday, 23/04/2020
Session
Computer vision
Tuhin Sharma (Binaize), Pravin Jha (Ameren)
Offline signature verification is one of the most critical tasks in traditional banking and financial industries. The unique challenge is to detect subtle but crucial differences between genuine and forged signatures. This verification task is even more challenging in writer-independent scenarios. Tuhin Sharma and Pravin Jha detail few-shot image classification. Read more.

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