Jonas Bonér, CTO of LightBend and creator Akka, discusses using Akka when developing distributed systems. He talks about the Actor Model, and how every Microservice needs to be viewed as a system to be successful. 

Why listen to this podcast:

 - Akka is JVM-based framework design for developing distributed systems leveraging the Actor Model - an approach for writing concurrent systems that treat actors as universal primitives and the most successful model with abstraction has been streaming
 - Circuit breakers in Akka are a backup and retry policy; they protect you by capturing failure data and allow you to roll back 
 - Every Microservice needs to be viewed as a system, it needs to have multiple parts that run on different machines in order to function and be fully resilient - thus is a Microsystem
 - Two different trends have emerged when it comes to hardware and environments: one is the trend toward Multi-core, the is a movement toward virtualized environments and the cloud
 - Saga pattern of managing long running transactions in a distributed system fits very well with messaging style architectures

Notes and links can be found on: http://bit.ly/2kwB2eB

Akka
The Actor Model
When Akka and the Actor Model is the perfect choice
Circuit breakers patterns in distributed systems
Two trends toward Multi-core
Reactive Manifesto
Event Driven vs. Message Driven
Reactive Programming and Streams
Microliths to Microsystems
What do you have to get right before you start trying to deploy a distributed systems?
Working with ML / AI at Lightbend to understand tracing through distributed system
Saga Pattern

More on this:
Quick scan our curated show notes on InfoQ http://bit.ly/2kwB2eB

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InfoQ Podcast

Show Notes

Jonas Bonér on the Actor Model, Akka, Reactive Programming, Microservices and Distributed Systems

Today on the InfoQ Podcast, Wes speaks with ThirdLove’s Megan Cartwright. Megan is the Director of Data Science for the personalized bra company. In the podcast, Megan first discusses why their customers need a more personal experience and how their using technology to help. She focuses quite a bit of time in the podcast discussing how the team got to an early MVP and then how they did the same for getting to an early machine learning MVP for product recommendations. In this later part, she discusses decisions they made on what data to use, how to get the solution into production quickly, how to update/train new models, and where they needed help. It’s a real early stage startup story of a lean team leveraging machine learning to get to a practical recommendations solution in a very short timeframe.

Why listen to this podcast:

 - The experience for women selecting bras is poor experience characterized by awkward fitting experiences and an often uncomfortable product that may not even fit correctly. ThirdLove is a company built to serve this market.
 - ThirdLove took a lean approach to develop their architecture. It’s built with the Parse backend. The leveraged Shopify to build the site. The company’s first recommender system used a rules engine embedded into the front end. After that, they moved to a machine learning MVP with a Python recommender service that used a Random Forest algorithm in SciKit-Learn. 
 - Despite having the data for 10 million surveys, the first algorithms only need about 100K records to be trained. The takeaway is you don’t have to have huge amounts of data to get started with machine learning.
 - To initially deploy their ML solution, ThirdLove first shadowed all traffic through the algorithm and then compared it to what was being output by the rules engine. Using this along with information on the full customer order lifecycle, they validated the ML solution worked correctly and outperformed the rules engine.
 - ThirdLove’s machine learning story shows that you move towards a machine learning solution quickly by leveraging your own network and using tools that may already familiar to your team.

More on this: Quick scan our curated show notes on InfoQ https://bit.ly/2G9RnQn
You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq

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Megan Cartwright on Building a Machine Learning MVP at an Early Stage Startup

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