3 episodes

Julien's Tech Bites is a podcast where we will learn about a specific #tech topic, one bite at a time!
We will investigate a new subject every month from the ground up, with the help of two guests!
Wanna learn about #reactive, #ai, #kotlin, #machinelearning or #sre, join us!

Julien's Tech Bites Julien Lengrand-Lambert

    • Technology

Julien's Tech Bites is a podcast where we will learn about a specific #tech topic, one bite at a time!
We will investigate a new subject every month from the ground up, with the help of two guests!
Wanna learn about #reactive, #ai, #kotlin, #machinelearning or #sre, join us!

    Episode 3 : Machine Learning and Data Science, from model to production!

    Episode 3 : Machine Learning and Data Science, from model to production!

    This time we'll dive into AI and machine learning! We'll explore why is their usage growing, how to put them to production, and why should engineers be interested in them, but also what ethical problems they bring. So come along!

    To help me discover the challenges of Machine learning, I'll be joined by 


    Maike Fischer, Machine Learning engineer at ING and one of the creators of the 'Introduction to Machine Learning' workshop there.
    Vaidas Kurlianskas, Chapter Lead Data Science and Data Scientist at ING. Vaidas knows the challenges of developing models, but also managing the expectations of stakeholders !

    During this episode, we'll get into the details of the terminology : Data Science, Machine Learning, Artificial Intelligence : How are those different? We'll also look into how machine learning engineers interact with product teams to help them improve their software. Then, we'll look at the different stages of building a model, from requirements all the way up to production! Finally, we'll look into the ethical issues that machine learning bring and how those impact our society.

    And of course, just like each time, we'll cover the best resources to get you started; and find some potential problems to solve and learn at the same time. 

    Some additional links mentioned during the episode : 


    Andrew Ng's Machine Learning course on Coursera : One of the most completed Coursera courses of all time.
    Kaggle : A website with datasets and challenges to solve, with practical implications.
    TensorFlow.js : A library to do Machine Learning on the frontend.



    This podcast is hosted by me, Julien Lengrand-Lambert. Subscribe to this podcast on your preferred platform or follow Julien's Tech Bites on Twitter to learn more about our future episodes!

    • 1 hr
    Episode 2 : In-memory data grids!

    Episode 2 : In-memory data grids!

    In this episode, we will discuss CAP Theorem, distributed caching and more. In-memory data grids are on the menu!

     

    Just like last time, we will be guided by two experts:


    David Follen is Developer and Chapter Lead at ING Belgium, and has several years experience building applications using data grids.
    Nicolas Fränkel is Developer Advocate for Hazelcast, one of the leading in-memory data grids on the market.


    During this hour, we'll be covering all the basics of in-memory data grids. What are they and what can they help you achieve. What are their advantages, but also drawbacks. In what situations can they be used to their maximum potential and to what extent. We'll also look into how to get started with in-memory data grids using available open-source projects. I hope you'll enjoy the ride!

    Some additional resources :


    A definition of in-memory data grids from Hazelcast's website.
    Wikipedia's page of the CAP Theorem
    Hazelcast's main Open-Source repository, and the Getting Started.
    Apache Ignite's Getting Started


    This podcast is hosted by me, Julien Lengrand-Lambert. Subscribe to this podcast or follow Julien's Tech Bites on Twitter to learn more about our future episodes!

    • 1 hr
    Episode 1 : Getting reactive, reactive Programming and Reactive Systems!

    Episode 1 : Getting reactive, reactive Programming and Reactive Systems!

    In this first episode, we will dive head first into the wonderful world of reactive systems, and how to create them!

    To help us in this quest, we will be guided by two experts. Both have been working for multiple years and involved in large, high throughput projects. 


    Vincent Free is a JVM developer who works on ING's global distributed tracing platform, handling up to one billion of events a day
    Alessandro Vermeulen is the engineering lead of the global application platform of ING,  and helped create the Reactive Center of expertise at ING.



    We will start by looking into the basics of reactive systems. We'll discuss why they are getting so relevant lately, and what are the trade-offs of using them. We'll also check when they are best used, and how to build them.  Finally, we'll look into the tooling and the must haves when starting with reactive. 



    Some additional resources : 


    The reactive manifesto, listing the properties of reactive systems.
    Event loops, where it all started
    Domain Driven Design and Event Sourcing
    Back pressure and the reactor pattern
    Akka and Akka streams
    Kafka 



    This episode is hosted by me, Julien Lengrand-Lambert. Follow Julien's Tech Bites to learn more about our future episodes!

    • 47 min

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