Cloud-Native Data Science Talks and Presentations

Public presentations and discussions about cloud-native and data science. Winder Research presents new ideas, reviews and keynotes at a range of premium conferences around the world.

CloudNativeX Interview: Reinforcement Learning

Apr 2021

Join Lee Razo and Phil Winder for this comprehensive introduction to Reinforcement Learning, an area of machine learning in which problems are tackled with intelligent agents which take actions to maximize a specified reward. Phil (quite literally) wrote the book on this topic and he takes us through the fundamentals of RL, some common use cases as well as tips on how even a small or mid-sized company can get started with and benefit from RL.

InfoQ Podcast: Phil Winder on the History, Practical Application, and Ethics of Reinforcement Learning

Mar 2021

InfoQ · Phil Winder on the History, Practical Application, and Ethics of Reinforcement Learning Charles Humble, friend and editor of InfoQ, was kind enough to ask me for an interview to talk more about my new book, in podcast format. From the blurb: In this episode of the InfoQ podcast Dr Phil Winder, CEO of Winder Research, sits down with InfoQ podcast co-host Charles Humble. They discuss: the history of Reinforcement Learning (RL); the application of RL in fields such as robotics and content discovery; scaling RL models and running them in production; and ethical considerations for RL.

DataTalksClub - Industrial Applications of Reinforcement Learning

Feb 2021

Reinforcement learning (RL), a sub-discipline of machine learning, has been gaining academic and media notoriety after hyped marketing “reveals” of agents playing various games. But these hide the fact that RL is immensely useful in many practical, industrial situations where hand-coding strategies or policies would be impractical or sub-optimal. Following the theme of my new book (https://rl-book.com​), I present a rebuttal to the hyperbole by analysing five different industrial case studies from a variety of sectors.

GOTO Book Club: How to Leverage Reinforcement Learning

Feb 2021

In this episode of GOTO’s book club I speak to Rebecca Nugent, Feinberg professor of statistics and data science at Carnegie Mellon univeristy. We talk, at length, about the application of reinforcment learning, specifically how it could be a way of creating truly personalised teaching curricula. It’s a really interesting discussion and it’s great to get someone of Rebecca’s calibre to bounce ideas off.

A Code-Driven Introduction to Reinforcement Learning

Nov 2020

Notebook link Abstract Reinforcement learning (RL) is lined up to become the hottest new artificial intelligence paradigm in the next few years. Building upon machine learning, reinforcement learning has the potential to automate strategic-level thinking in industry. In this presentation I present a code-driven introduction to RL, where you will explore a fundamental framework called the Markov decision process (MDP) and learn how to build an RL algorithm to solve it.

Keep it Clean: Why Bad Data Ruins Projects and How to Fix it

Jan 2020

Slides Abstract The Internet is full of examples of how to train models. But the reality is that industrial projects spend the majority of the time working with data. The largest improvements in performance can often be found through improving the underlying data. Bad data is costing the US economy an estimated 3.1 trillion Dollars and approximately 27% of data is flawed in the world’s top companies. Bad data also contributes to the failure of many Data Science projects.

Keep it Clean: Why Bad Data Ruins Projects and How to Fix it

Apr 2019

Slides Abstract The Internet is full of examples of how to train models. But the reality is that industrial projects spend the majority of the time working with data. The largest improvements in performance can often be found through improving the underlying data. Bad data is costing the US economy an estimated 3.1 trillion Dollars and approximately 27% of data is flawed in the world’s top companies. Bad data also contributes to the failure of many Data Science projects.

Life and Death Decisions: Testing Data Science

Apr 2018

Abstract We live in a world where decisions are being made by software. From mortgage applications to driverless vehicles, the results can be life-changing. But the benefits of automation are clear. If businesses use data science to automate decisions they will become more productive and more profitable. So the question becomes: how can we be sure that these algorithms make the best decisions? How can we prove that an autonomous vehicle will make the right decision when life depends on it?

AI Panel of Experts

Mar 2018

Join the track speakers and invited guests as they discuss where AI is heading and how it’s affecting software today. Enjoyed fielding questions about #DataScience and #AI today at #QConLondon. Great questions and expert speakers, but SMEs are underrepresented in data science. We need more SMEs speaking! pic.twitter.com/Vasi24z3LY — Phil Winder (@DrPhilWinder) March 6, 2018

The Meaning of (Artificial) Life: A Prelude to What is Data Science?

Nov 2017

Abstract The Hitchhiker’s Guide says the meaning of life is 42. Considering that the field of Data Science is going through a period of exponential growth it too could soon find that the meaning of an artificial life is also 42. But if you are not involved on a day-to-day basis, the expansion can seem bewildering. The story of how disparate disciplines have combined to produce Data Science is fascinating.

Research-Driven Development: Improve the Software You Love While Staying Productive

Oct 2017

Abstract Have you ever wondered which parts of your job you love or hate? Chances are that like most developers you love learning and new problems to solve. You hate monotony and bureaucracy. You’ve probably put strategies in place to mitigate the things you don’t like. An anarchic development process like Agile, to reduce the amount of time in meetings. But have you ever thought about the way in which you approach learning and problem solving?

The Meaning of (Artificial) Life: A Prelude to What is Data Science?

Oct 2017

Abstract The Hitchhiker’s Guide says the meaning of life is 42. Considering that the field of Data Science is going through a period of exponential growth it too could soon find that the meaning of an artificial life is also 42. But if you are not involved on a day-to-day basis, the expansion can seem bewildering. The story of how disparate disciplines have combined to produce Data Science is fascinating.

Secure my Socks: Exploring Microservice Security in an Open-Source Sock Shop - AOTB

Jul 2017

Abstract In this talk, you will discover a reference microservices architecture – the sock shop – which we will abuse in order to investigate microservice security on the Kubernetes orchestrator and Weave Net, a software-defined network. Despite covering a range of topics, it will focus on the demonstration of two key areas: network policy and secure containers. Objective: You will learn how to secure containers and improve network security through the use of a software defined network.

Cloud-Native Data Science: Turning Data-Oriented Business Problems Into Scalable Solutions

Jun 2017

Abstract The proliferation of Data Science is largely due to: ubiquitous data, increasing computational power and industry acceptance that solutions are an asset. Data Science applications are no longer a simple dataset on a single laptop. In a recent project, we help develop a novel cloud-native machine learning service. It is unique in that problems are packaged as containers and submitted to the cloud for processing. This enables users to distribute and scale their models easily.

Secure my Socks: Exploring Microservice Security in an Open-Source Sock Shop - CL

May 2017

Abstract In this talk, you will discover a reference microservices architecture – the sock shop – which we will abuse in order to investigate microservice security on the Kubernetes orchestrator and Weave Net, a software-defined network. Despite covering a range of topics, it will focus on the demonstration of two key areas: network policy and secure containers. Objective: You will learn how to secure containers and improve network security through the use of a software defined network.

Developers _are_ Researchers - Improve the work you love with Research Driven Development

May 2017

Abstract Have you ever wondered which parts of your job you love or hate? Chances are that like most developers you love learning and new problems to solve. You hate monotony and bureaucracy. You’ve probably put strategies in place to mitigate the things you don’t like. An anarchic development process like Agile, to reduce the amount of time in meetings. But have you ever thought about the way in which you approach learning and problem solving?

Monitor My Socks: Using Prometheus in a Polyglot Open Source Microservices Reference Architecture

Apr 2017

Abstract This presentation describes how Prometheus was integrated into a polyglot microservices application. We will use the “Sock Shop”, a cloud-native reference microservices architecture to demonstrate some of the best practices and pitfalls of attempting to unify monitoring in real life. Attendees will be able to use this application as a reference point, or as a real life starting point for their own applications. Specifically, we will cover:

Secure my Socks: Exploring Microservice Security in an Open Source Sock Shop

Nov 2016

Abstract Microservices are often lamented as “providing enough rope to hang yourself”, which gives the impression that microservices are inherently insecure. But if we do microservices right, we can improve security with a range of measures all designed to prevent further intrusion and disruption. In this talk, you will discover a reference microservices architecture - the sock shop - which we will abuse in order to investigate microservice security on the Kubernetes orchestrator and Weave Net, a software-defined networking product from Weaveworks.