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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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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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