Machine learning at scale and in research: how different are they and what can they learn from each other?

Date
24 April 2019 - 12:00-13:00
Location
COM-G12-Main Lewin
Speaker
Tania Allard, Microsoft

Abstract: The last decade has seen the largest growth in the areas of theoretical and applied machine learning, deep learning, and artificial intelligence of all history. This is in part due to the widespread availability of better, more powerful and cheaper computational resources than ever before. Another factor leading to this growth is the embedding of research machine learning groups in larger corporations. As a consequence, we have seen the emergence of ‘data powered’ services and applications in almost any industry, from retail and fintech to highly regulated areas such as healthcare and security.

Although the practice of embedding research scientists in startups and companies providing machine learning services has become more common there is still the belief that production and research and development ML are fundamentally different. When the truth is, they are both different sides of the same coin. But there is yet another question to ask: how different is industrial to academic research (in machine learning and data science) and what practices can and should be cross-pollinated from one environment to the other?

In this talk, I will cover the infrastructure and software practices which make it possible to serve ML services to the wider population at scale. I will then compare the industrial and academic machine learning research practices and provide insight into industry practices that could be adapted by researchers to improve their outputs, workflows, foster reproducible and transparent science and work collaboratively to solve global challenges.

By the end of the talk, the attendees will be better informed about the technologies that could improve and boost machine learning and data science research and practices.

Bio: Tania is a Developer Advocate with vast experience in academic research and industrial environments. Her main areas of expertise are within data-intensive applications, scientific computing, and machine learning. One of her main areas of expertise is the improvement of processes, reproducibility, and transparency in research, data science and artificial intelligence. Over the last few years, she has trained hundreds of people on scientific computing reproducible workflows and ML models testing, monitoring and scaling and delivered talks on the topic worldwide.

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