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

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

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Show Notes

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

Victor Dibia is a Research Engineer with Cloudera’s Fast Forward Labs. On today’s podcast, Wes and Victor talk about the realities of building machine learning in the browser. The two discuss the capabilities, limitations, process, and realities around using TensorFlow.js. The two wrap discussing techniques like Model distillation that may enable machine learning models to be deployed in smaller footprints like serverless.
 - While there are limitations in running machine learning processes in a resource constrained environment like the browser, there are tools like TensorFlow.js that make it worthwhile. One powerful use case is the ability to protect the privacy of a user base while still making recommendations. 
TensorFlow.js takes advantage of the WebGL library for its more computational intense operations.
 - TensorFlow.js enables workflows for training and scoring models (doing inference) purely online, by importing a model built offline with more tradition Python tools, and a hybrid approach that builds offline and finetunes online.
To build an offline model, you can build a model with TensorFlow Python (perhaps using a GPU cluster). The model can be exported into the TensorFlow SaveModel Format (or the Keras Model Format) and then converted with TensorFlow.js into the TensorFlow Web Model Format. At that point, the can be directly imported into your JavaScript. 
 - TensorFlow Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models and was made available by the Google AI team. It can give developers a quick jumpstart into using trained models.
 - Model compression promises to make models small enough to run in places we couldn’t run models before. Model distillation is a process where a smaller model is trained to replicate the behavior of a larger one. In one case, BERT (a library almost 500MB in size) was distilled to about 7MB (almost 60x compression).

More on this: Quick scan our curated show notes on InfoQ https://bit.ly/32rWnab
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Victor Dibia on TensorFlow.js and Building Machine Learning Models with JavaScript

Software engineers, architects and team leads have found inspiration to drive change and innovation in their team by listening to the weekly InfoQ Podcast. They have received essential information that helped them validate their software development map. We have achieved that by interviewing some of the top CTOs, engineers and technology directors from companies like Uber, Netflix and more. Over 1,200,000 downloads in the last 3 years.

Victor Dibia on TensorFlow.js and Building Machine Learning Models with JavaScript

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