Another round for Rafael – Type-safe builder pattern in Scala revisited

Abstract: It is almost 10 years since a blog writer named Rafael Ferreira wrote a post entitled “Type-safe Builder Pattern in Scala”. In this post Rafael shows how to use certain features of the Scala type system to implement the well-known “Builder Pattern” but with static compile-time safety checks. Since the original post, others have been inspired through the years to take the same example and show alternative approaches for the type-safe build pattern in Scala.

This talk continues in the same vein and aims to show the sheer variety of approaches that Scala enables, while offering perhaps a different perspective on some interesting Scala techniques for type-safety. (Some keywords: Implicit parameters, type constraints, Phantom types, Abstract type members, the Aux pattern)

Category Theory: in theory and practice

Abstract: Writing an abstract is hard, kind of like composing your thoughts for a concise but yet meaningful stand-up update keeping in relevant information without the low-level details. Maybe that statement is a little bit “meta”, but it’s not just in practice that we look for abstraction, even the mathematics behind what we do is about that.

In this talk, I’ll quickly introduce some basic concepts from Category Theory and then compare some of those theoretical concepts to the ones we know from practice in Cats and Scalaz.

The Power of AI to Revolutionise The 100 Year old Bond

Having worked in Investment Banking Trading Technology for 4 years before co-founding 9fin in late 2016, Huss and the 9fin team work on tools and systems to liberate information from financial documents. He loves Python, and loves PostgreSQL even more. 9fin is AI powered financial data extraction for the bond market. 9fin’s technology helps search, filter and analyse the world’s fixed income financial data, allowing professionals to make better investment decisions.

High Throughput ML Pipelines and Predictions in Production Systems

Ravelin is a smart fraud detection and prevention platform that helps companies stop online payment fraud by examining customer behaviour data and spotting fraudsters while there is still time to block them. The company imports a client’s visitor, registration, and payment data in real time, via an API, inspects data using an AI, identifies and blocks fraudsters, and enables systems to prevent such crimes in future.

Ravelin runs a go microservices platform that makes around 1,000 predictions a second. This talk will give a walk through our multiple attempts to scale and optimise model deployment. A journey that will take use through Docker, CoreOS Rkt, Pickel, Protobuf, Sidecars, Python and Go.

Click here to watch this talk with slides

Thud: Why it’s not failure you should be afraid of

“Thud” is the sound a bowling ball makes when dropped onto damp earth. But it’s also the sound that most of our software makes when it hits the market. We’re great at celebrating our wild successes, and finding people to blame for catastrophic failures.

This talk is about how we spend most of our work trying to figure out which we have on our hands: a success or a failure. Jeff will share stories of how we use discovery work to identify when we’ve got a “thud” on our hands. And, how the hardest thing to do is recognize and let go of our thuds.

Click here to watch this talk with slides

For more info about this talk with Jeff Patton see the meetup page