You are viewing content from a past/completed QCon Plus - November 2020

Track Overview

Machine Learning for the Software Engineer

Discover what it takes to start and deploy a machine learning project. We cover the machine learning pipeline by looking at data collection, model training, and model deployment. Training a machine learning model is just the tip of the iceberg of a successful project. In this track we will also look at how to set up a data collection process, and what features to select for your own project. We will answer practical questions of software engineers, among which “but how do I deploy this trained model now”, and “why does it take so long to train my model”. We will also cover what machine learning models to train, and what the latest machine learning techniques are that you have to apply to get good scores on ML benchmarks.


From this track

Session

Designing Better ML Systems: Learnings From Netflix

Tuesday Nov 10 / 01:00PM EST

Data Science usage at Netflix goes much beyond our eponymous recommendation systems. It touches almost all aspects of our business - from optimizing content delivery to making our infrastructure more resilient to failures and beyond. Our unique culture of freedom & responsibility affords our...

Savin Goyal

Engineer on the ML Infrastructure team @Netflix

Session

Data-Driven Development in the Automotive Field

Tuesday Nov 10 / 01:50PM EST

In the new era of new mobility where solving many challenging tasks of autonomous driving is not possible with classical software development. It is due to the fact that we cannot write every rule by hand and thus we would like to learn it from a huge amount of data recorded by different driving...

Toshika Srivastava

AI expert @Audi

Session

Scaling & Optimising the Training of Predictive Models

Tuesday Nov 10 / 02:40PM EST

Modern Machine Learning has brought with it countless advances, both algorithmically and with respect to tooling; there is relentless growth on all fronts. Nevertheless, we are faced with a multitude of challenges when trying to pull all these threads of progress together in a meaningful way,...

Nicholas Mitchell

Machine Learning Engineer at @argoai

PANEL DISCUSSION

Panel: The Purpose of Machine Learning

Tuesday Nov 10 / 03:30PM EST

In the machine learning world, there is no shortage of buzzwords. Each trend follows the next in rapid succession, during the panel we will discuss how and when our panelists decide to pick up on a trend and when to focus on proven technology. We delve into these decisions based on their...

Thomas van Heyningen

Data Science Consultant at NAVARA

Nicholas Mitchell

Machine Learning Engineer at @argoai

Jendrik Jördening

CTO @Nooxit

Diana Hu

Startup Advisor and former CTO for Escher Reality


Speakers from this track

Savin Goyal

Engineer on the ML Infrastructure team @Netflix

Savin is an engineer on the ML Infrastructure team at Netflix. He focuses on building generalizable infrastructure to accelerate the impact of data science at Netflix.

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Toshika Srivastava

AI expert @Audi

I am an AI technical team lead at AUDI responsible for AI products development for safety relevant automated driving functions. The scope of my work focus on end-to-end development and deployment of safety-relevant AI modules with Systematic process, method, and tools.  I have 6 + experience...

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Nicholas Mitchell

Machine Learning Engineer at @argoai

Nicholas is a Machine Learning Engineer at Argo AI, solving the task of perception for autonomous vehicles. He is also a part-time researcher, completing a Ph.D. in cross-domain applications of AI.

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Thomas van Heyningen

Data Science Consultant at NAVARA

Thomas is an enthusiastic and practical data scientist. He is always finding ways to integrate data science into business processes. Approaching each use case with a fresh sense of curiosity, figuring out the process and problem at hand. Working as a data science consultant in The Netherlands,...

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Jendrik Jördening

CTO @Nooxit

Jendrik is CTO at Nooxit. He formerly worked at Aurubis and Akka Germany on Data Science and Deep Learning in the field of industry 4.0 and autonomous machines.At the same time he took part in the Udacity Self-Driving Car Nanodegree, participating with a group of other Udacity student in the...

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Find Jendrik Jördening at:

Diana Hu

Startup Advisor and former CTO for Escher Reality

Diana currently is advising startups and angel investing. Previously she was the head of the AR platform at Niantic. She led the AR teams building the core AR tech used in games like Pokemon Go and Harry Potter Wizards Unite. Previously, she was the Cofounder and CTO of Escher Reality, a...

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Track Date

Tuesday Nov 10 / 12:00PM EST

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Track Host

Roland Meertens

Product Manager @annotell

Roland has a passion for Artificial Intelligence, and is specialised in robotics projects. He set up machine learning projects all the way from sensor selection, data collection and labelling to deploying the model in production. Please approach him to talk about deep learning and neural networks.

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