Ethical Machine Learning And Data Science

Come join our team of data scientists and machine learning experts as we discuss ethical machine learning at DAML (Data Analytics Machine Learning ) at Redfin. Our presentation will be followed by Josh Poduska is a Senior Data Scientist in HPE’s Big Data Software Group. Who will be discussing Machine Learning on Distributed Systems.

We are very excited for the opportunity to present and can’t wait to see you guys there! It is 100% free and food is provided. Free data science and machine learning talks + free food? What more do you need!

Click Here To RSVP to DAMLs Machine Learning Talk on August 24th For Free

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Abstracts:

Ethical Machine Learning

Non-technical companies are slowly finding ways to increase their business value using the increased speed of computing and statistics. The problem is, business has always been more concerned about increasing the bottom line, vs. social impact. It is one thing when we joke about large e-commerce sites selling us that extra toaster. But what about when companies that have products that have been proven harmful reach out to data scientists and attempt to have them develop systems that increase the profit for a product that has a negative social impact, or when companies use data science to manipulate the customer, rather than benefit them. Should we? Is it right to forget about the social impact just to make an extra dollar?

Machine Learning on Distributed Systems

Most real-world data science workflows require more than multiple cores on a single server to meet scale and speed demands, but there is a general lack of understanding when it comes to what machine learning on distributed systems looks like in practice. Gartner and Forrester do not consider distributed execution when they score advanced analytics software solutions. Many formal machine learning training occurs on single node machines with non-distributed algorithms. In this talk we discuss why an understanding of distributed architectures is important for anyone in the analytical sciences. We will cover the current distributed machine learning ecosystem. We will review common pitfalls when performing machine learning at scale. We will discuss architectural considerations for a machine learning program such as the role of storage and compute and under what circumstances they should be combined or separated.

Feel free to read some of our other blog posts as well!

Best Python Libraries for Machine Learning

Automating Your Data Science Workflow

​Should We Start A Data Science Team?