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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Show Notes

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

Probabilistic Programming has been discussed as a programming paradigm that uses statistical approaches to dealing with uncertainty in data as a first class construct. On today’s podcast, Wes talks with Mike Lee Williams of Cloudera’s Fast Forward Labs about Probabilistic Programming. The two discusses how Bayesian Inference works, how it’s used in Probabilistic Programming, production-level languages in the space, and some of the implementations/libraries that we’re seeing.

Key Takeaways
* Federated machine learning is an approach of developing models at an edge device and returning just the model to a centralized location. By taking the averages of the edge models, you can protect privacy and distribute processing of building models.
*Probabilistic Programming is a family of programming languages that make statistical problems easier to describe and solve.
*It is heavily influenced by Bayesian Inference or an approach to experimentation that turns what you know before the experiment and the results of the experiment into concrete answers on what you should do next.
* The Bayesian approach to unsupervised learning comes with the ability to measure uncertainty (or the ability to quantify risk).
* Most of the tooling used for Probabilistic Programming today is highly declarative. “You simply describe the world and press go.”
* If you have a practical, real-world problem today for Probabilistic Programming, Stan and PyMC3 are two languages to consider. Both are relatively mature languages with great documentation.
* Prophet, a time-series forecasting library built at Facebook as a wrapper around Stan, is a particularly approachable place to use Bayesian Inference for forecasting use cases general purpose.

Mike Lee Williams on Probabilistic Programming, Bayesian Inference, and Languages like PyMC3

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