Description

Processing Big Data was 2015. 2018 is the year of using tools, algorithms, and platforms for machine learning to find adaptive solutions to complex problems.

As developers we’re tasked with applying the latest technologies to attack problems. And while it’s important to know how to implement the programs, it’s just as important to know when it’s not the right time to use the resources. AMLDD is not only about the hows but the “whys”, the “ifs”, and the results.

Our speakers will share strategies and lessons learned on the following:
- Platforms
- Tools
- Use cases & business cases
- Conveying Insights

Agenda:

12:00pm Registration and Networking

12:30pm Session #1: On Keeping Things Simple
Angela Bassa, Director of Data Science, iRobot
In the field of data science, we are constantly bombarded with innovative approaches and methods. These fresh new tools promise to yield impressively accurate results—and, like all Faustian bargains, can come at a cost that all too often stays hidden until it’s too late. Those of us who gravitate towards this discipline can get easily seduced by the promise of cutting-edge precision. We can even fall prey to our impulses of following the latest and coolest at the expense of our objectives. When does it make sense to deploy complex solutions into production environments? And how should we assess the pros and cons of doing so? The end-goal of this talk is to minimize the chance of adding unneeded complexity to our already constrained systems. To do so, we will review common pitfalls, a few strategies for making complexity assessments, and a framework to make it more likely that the simplest solution possible is implemented to meet every team's objectives.

1:20pm Session #2: Issues in Image Classification
Eric Breck, Software Engineer, Google & D. Sculley, Tech Lead, Google
Image classification has advanced substantially in recent years, in part by exploiting large publicly available datasets like ImageNet and OpenImages.  In this talk, we review the recent history of computer vision and the role of these datasets.  We then investigate representation bias found in these datasets, and show early results as to the effect of the data bias on classifiers learned from that data.

2:10pm Session #3 Buzzword Noncompliant: Secrets of Productive Data Science
Simeon Simeonov, Founder & CTO, Swoop
“In theory, there is no difference between theory and practice. But, in practice, there is.” In the world of data science, it’s hard to argue with this saying, especially since many of the most interesting and commercially valuable domains for the application of ML & AI exhibit wonderfully nasty idiosyncrasies. Take a tour through the trenches of an optimization environment with petabyte scale dirty data, no counterfactual evidence, variable feedback cycles and a multitude of competing objectives.

2:55pm Networking Break

3:15pm Session #4 How to Train Your Product Owner
David Murgatroyd, Machine Learning Chapter Lead, Spotify
Machine Learning is transforming every industry with innovative techniques receiving deserved attention. But turning innovation into value requires integrating into practical technology products, often with the leadership of product managers. We'll talk about how to help your friendly neighborhood Product Owner: identify where ML can make a difference, develop metrics to validate and refine it, identify data to feed it, prioritize work to develop it, and structure teams to deliver it in a satisfying way.

4:05pm Session #5 The Art of Communicating (Data) Science
Vinod Kumar, Head of Analytics & Intelligence, Salesforce Commerce Cloud
Experienced ML Engineers and Data Scientists know that extracting insights from massive amounts of data is only part of the job. Real value is delivered in conveying those insights to business users in a manner that compels action. In this session, we will talk about the perspective ML engineers and data scientists will need to take to inspire business users to take action based on the insights they receive.

Speakers:

Angela Bassa Director of Data Science iRobot	Eric Breck Software Engineer Google	Vinod Kumar Head of Analytics & Intelligence Salesforce Commerce Cloud
David Murgatroyd Machine Learning Chapter Lead Spotify	D. Sculley  Tech Lead Google	Simeon Simeonov Founder & CTO Swoop

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