Filtering by: Day1

[Keynote] State of Automated Machine Learning in AdTech - Claudia Perlich (Dstillery)
Oct
10
4:40 PM16:40

[Keynote] State of Automated Machine Learning in AdTech - Claudia Perlich (Dstillery)

Claudia Perlich is Chief Scientist at Dstillery (the former Media6Degrees) where she designs, develops, analyzes and optimizes the machine learning that drives digital advertising to prospective customers of brands. She was selected as member of the Crain’s NY annual 40 Under 40 list.

Twitter: @claudia_perlich - Linkedin - Website

 

 

 

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Patient Health Condition Prediction and Monitoring — Yan Zhang (Microsoft)
Oct
10
3:30 PM15:30

Patient Health Condition Prediction and Monitoring — Yan Zhang (Microsoft)

This talk showcases a patient health condition prediction and monitoring system, with goal to perform predictive care on patients and identify the risk factors before the condition become more serious. We illustrate the end-to-end process to build this application, including how the machine learning model is trained and deployed, how to automate the data scoring process, and how to consume these results using a reporting/visualization tool.

Dr. Yan Zhang is a Sr. Data Scientist in Algorithm and Data Science team in data group, Cloud & Enterprise, Microsoft. She builds predictive analytics models and generalizes machine learning solutions on Cloud machine learning platform. Her recent research include cost prediction/fraud claim detection in healthcare domain and predictive maintenance in IoT applications. She is a viewer for book "Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition" published in September 2015.

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Enterprise API Security Requirements for using Predictive APIs — Jason Macy (Forum Systems)
Oct
10
2:30 PM14:30

Enterprise API Security Requirements for using Predictive APIs — Jason Macy (Forum Systems)

Enterprise business analysts can no longer ignore the value of using public cloud-based machine learning solutions. The cost, quality, ease-of-use and rapid development in predictive APIs enables corporations to use such public cloud services for effectively modelling their private data. Within such hybrid public-private cloud environments, the need for API security is greater than ever. Building a proper API security infrastructure without a long-term API strategy can be a challenge, and over time become both costly and expose your organization to serious security risks.

JJason Macy is the Chief Technical Officer responsible for innovation and product strategy for global operations at Forum Systems. Jason has been a leading visionary for enterprise architecture design and successful deployment API identity and security technology. With hundreds of deployments worldwide, Jason’s unique ability to pragmatically solve complex, industry use cases and provide sustained engineering initiatives continues to forge the leadership role of Forum Systems product technology. Drawing from experience from virtually every industry sector, Jason has helped to evolve the Forum Sentry technology platform to be the global leader in FIPS 140-2 API security and identity.

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Meta Data Science: When all the world's data scientists are just not enough — Chalenge Masekera (Salesforce)
Oct
10
2:30 PM14:30

Meta Data Science: When all the world's data scientists are just not enough — Chalenge Masekera (Salesforce)

What if you had to build more models than there are data scientists in the world? Well, enterprise companies serving hundreds of thousands of businesses often have to do precisely this. In this talk, I'll describe our general purpose machine learning platform that automatically builds per-company optimized models for any given predictive problem at scale, beating out most hand tuned models.

Chalenge Masekera is a data scientist at Salesforce, where he builds machine learning models and analytics tools that enable real time monitoring of system infrastructure, machine learning models and executive dashboards ensuring scalable machine learning pipelines. Previous experience also includes business intelligence consultancy. He has a Masters in Information Management and Systems from the University of California, Berkeley.

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Bringing the power of Spark to all data analysts — Clément Stenac (Dataiku)
Oct
10
2:00 PM14:00

Bringing the power of Spark to all data analysts — Clément Stenac (Dataiku)

A few years ago, Hive brought SQL to Hadoop and enabled its widespread adoption by data analysts. Today, Spark has become the tool of choice for data engineers, who can build powerful data pipelines. However, Spark is fairly complex. Using it efficiently requires some understanding of the inner workings (shuffler, caching, memory, …). We will cover the challenges we faced in bringing Spark to an audience of less technical users, some of the solutions (like auto-tuning), and how improvements to Spark (memory management, statistics, new APIs, …) help bring its power to every data citizen.

Clément Stenac is a passionate software engineer, CTO of Dataiku. We are the makers of DSS, an integrated development environment that helps data analysts, scientists and engineers collaborate to build and run data applications. Clément was previously head of development at Exalead, leading the design and implementation of large-scale search engine software. He also has extended experience with open source software, as a former developer of the VideoLAN (VLC) and Debian projects.

Twitter: @ClementStenac - Linkedin

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Accelerating Model Development and Deployment with the right API Abstraction — Dallin Akagi (DataRobot)
Oct
10
2:00 PM14:00

Accelerating Model Development and Deployment with the right API Abstraction — Dallin Akagi (DataRobot)

The amount of value that we can get out of our data depends on both the accuracy of the models built around them and the speed with which these can be built, tested, and deployed. In this talk we present the API of DataRobot, focusing on the reasons why we focus on a higher-level modeling abstraction than other APIs. We will also share a use case illustrating how this abstraction level makes it possible to accelerate model deployment development.

Dallin is a data scientist and engineer at DataRobot, building a REST API for automated machine learning. He previously worked in a computer vision lab for the Department of Defense studying neural networks and deep learning. He studied Computer Science at CalTech.

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Automating Machine Learning — Poul Petersen (BigML)
Oct
10
1:30 PM13:30

Automating Machine Learning — Poul Petersen (BigML)

In just the last few years, Machine Learning has gone from something barely known outside of academic circles, to becoming now a critically important tool for optimizing business operations. Assuming an organization even has a small team of ML experts, as the number of ML applications explodes, the pressure on these teams and their hand tailored solutions brings innovation to a halt.

As a result, many organizations are beginning to realize that the solution is to bring ML to everyone as a standardized platform. So, what should you be looking for in a platform? As easy as it it to make a wishlist of features, it's equally easy to overlook the importance of automation. ML tasks are iterative by nature and automation of the tasks and workflows is essential. In this talk you will see how WhizzML is making automation easy, reducing the need for experts, and putting the Machines back into Machine Learning.

Poul is Chief Infrastructure Officer at BigML. He has an MS degree in Mathematics as well as BS degrees in Mathematics, Physics and Engineering Physics. With 20 plus years of experience building scalable and fault tolerant systems in data centers, Poul currently enjoys the benefits of programmatic infrastructure, hacking in python to run BigML with only a laptop and a cloud.

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[Tutorial] Deploying Python Models As an API — Henri Dwyer (Dataiku)
Oct
10
1:30 PM13:30

[Tutorial] Deploying Python Models As an API — Henri Dwyer (Dataiku)

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Predict what color of shoes your customer wants to buy next! — Bas Nieland (Datatrics)
Oct
10
11:40 AM11:40

Predict what color of shoes your customer wants to buy next! — Bas Nieland (Datatrics)

How to use predictive APIs for 'Next Best Action'-marketing based on various datasets, predictive APIs and BigML's infrastructure.

Datatrics makes predictive marketing accessible, actionable and easy to use. With Datatrics, small and medium-sized enterprises can easily integrate their data, gain valuable insights and get actionable results that help them - and their team - to reach marketing goals. As Chief Technology Officer, Bas is responsible for the strategic development of the platform. Previously, Bas has worked in similar roles for Green Orange Digital Marketing and the financial analytics startup StockFluence.

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[Tutorial] Evaluating Failure Prediction Models for Predictive Maintenance — Shaheen Gauher (Microsoft)
Oct
10
11:40 AM11:40

[Tutorial] Evaluating Failure Prediction Models for Predictive Maintenance — Shaheen Gauher (Microsoft)

Predictive Maintenance is about anticipating failures and taking preemptive actions. In the realm of predictive maintenance, the event of interest is an equipment failure. Modelling for Predictive Maintenance falls under the classic problem of modelling with imbalanced data when only a fraction of the data constitutes failure. This kind of data poses several issues. In this talk I will highlight some of the pitfalls and challenges of building a model with such data and describe ways to circumvent the problems using real use cases and examples.

Shaheen Gauher, PhD, is a Data Scientist in Information Management and Machine Learning at Microsoft. She develops end to end data driven advanced analytic solutions for external customers working across all verticals.

Twitter: @Shaheen_Gauher - Linkedin

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[Tutorial] Beyond Churn Prediction: An Introduction to Uplift Modelling — Pierre Gutierrez (Dataiku)
Oct
10
11:00 AM11:00

[Tutorial] Beyond Churn Prediction: An Introduction to Uplift Modelling — Pierre Gutierrez (Dataiku)

In several industries (e-business, telcos…), a common approach to diminishing user churn is to use machine learning to score individual customers by churn probability and target them with specific messaging or offers. However, this approach may be ineffective since it does not optimize what is called "true lift" or "uplift": the effect of an action on churning probability. This talk aims at introducing uplift modeling in a tutorial like format. We’ll cover the basics of the theory as well as how to make it work in practice. We will illustrate the talk with examples from real life.

Pierre Gutierrez is a senior data scientist at Dataiku. As a data science expert and consultant, Pierre has worked in diverse sectors such as e-business, retail, insurance or telcos. He has experience in various topics such as fraud detection, bot detection, recommender systems, or churn prediction.

Twitter: @prrgutierrez - Linkedin

 

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[Tutorial] Predicting Customer Behavior — Vinny Senguttuvan (Metis)
Oct
10
11:00 AM11:00

[Tutorial] Predicting Customer Behavior — Vinny Senguttuvan (Metis)

We will look at ways of applying data science and machine learning to better understand customers and improve their user experience. From a practical industry-application perspective, we will discuss the following: measuring popularity, statistical significance in A/B testing, survival analysis, predictive lifetime value and recommendation systems. We will review the concepts and some of the math behind these, while also addressing the real world challenges faced by many of these implementations.

Vinny is a Senior Data Scientist and Professor at Metis. Previously, he was a Lead Data Scientist at High 5 Games and an R&D Programmer at Blue Sky Studios (the animation company that made Ice Age, Rio and Peanuts).

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[Panel Discussion] Building intelligent applications with ML
Oct
10
9:50 AM09:50

[Panel Discussion] Building intelligent applications with ML

Panel discussion moderated by Andy Thurai, Program Director at IBM and Local Chair at PAPIs '16 (Twitter - Linkedin)

Panelists:

 

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[Keynote] Regulating Greed Over Time: An Important Lesson For Practical Recommender Systems — Cynthia Rudin (Duke & MIT)
Oct
10
9:20 AM09:20

[Keynote] Regulating Greed Over Time: An Important Lesson For Practical Recommender Systems — Cynthia Rudin (Duke & MIT)

There is an important aspect of practical recommender systems that we noticed while competing in the ICML Exploration-Exploitation 3 data mining competition. The goal of the competition was to build a better recommender system for Yahoo!'s Front Page, which provides personalized new article recommendations. The main strategy we used was to carefully control the balance between exploiting good articles and exploring new ones in the multi-armed bandit setting. This strategy was based on our observation that there were clear trends over time in the click-through-rates of the articles. At certain times, we should explore new articles more often, and at certain times, we should reduce exploration and just show the best articles available. This led to dramatic performance improvements. As it turns out, the observation we made in the Yahoo! data is in fact pervasive in settings where recommender systems are currently used...

Cynthia Rudin is an associate professor of statistics at the Massachusetts Institute of Technology associated with the Computer Science and Artificial Intelligence Laboratory and the Sloan School of Management, and directs the Prediction Analysis Lab. Her interests are in machine learning, data mining, applied statistics, and knowledge discovery (Big Data). Her application areas are in energy grid reliability, healthcare, and computational criminology.

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