AI, Machine Learning, Reinforcement Learning, and MLOps Articles

Learn more about AI, machine learning, reinforcement learning, and MLOps with our insight-packed articles. Our AI blog delves into industrial use of AI, the machine learning blog is more technical, the reinforcement learning blog is industrially renowned, and our mlops blog discusses operational ML.

Cloud-Native Data Science: Turning Data-Oriented Business Problems Into Scalable Solutions

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Abstract The proliferation of Data Science is largely due to: ubiquitous data, increasing computational power and industry acceptance that solutions are an asset. Data Science applications are no longer a simple dataset on a single laptop. In a recent project, we help develop a novel cloud-native machine learning service. It is unique in that problems are packaged as containers and submitted to the cloud for processing. This enables users to distribute and scale their models easily.

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Monitor My Socks: Using Prometheus in a Polyglot Open Source Microservices Reference Architecture

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Abstract This presentation describes how Prometheus was integrated into a polyglot microservices application. We will use the “Sock Shop”, a cloud-native reference microservices architecture to demonstrate some of the best practices and pitfalls of attempting to unify monitoring in real life. Attendees will be able to use this application as a reference point, or as a real life starting point for their own applications. Specifically, we will cover: Integrating Prometheus in Java/Go/Node.

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How to use Javascript Promises to lazily update data

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Last week I was working on a simple implementation updating a shopping cart for a site, the frontend was written in html/javascript. The brief - when the quantity of an item in the cart was modified the client could press an update cart button which would update the cart database, after which it was necessary to recalculate the total values of the order and refresh the page with the new totals.

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What is the Cloud?

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Dr. Phil Winder
CEO

The terms “Cloud” or “Cloud Services” have become so laden with buzz that they would be happy to compete with Apollo 11 or Toy Story. But the hype often hides the most important aspects that you need to know. Like how it works, or what you can do with it. This is the first of several introductory pieces that focus on the very basics of modern applications.

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Surprise at CPU Hogging in Golang

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Dr. Phil Winder
CEO

In one of my applications, for various reasons, we now have a batch like process and a HTTP based REST application running inside the same binary. Today I came up against an issue where HTTP latencies were around 10 seconds when the batch process was running.

After some debugging, the reason for this is that although the two are running in separate Go routines, the batch process is not allowing the scheduler to schedule the HTTP request until the batch process has finished.

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Exploring Microservice Security in an Open-Source Sock Shop

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Slides Abstract Microservices are often lamented as “providing enough rope to hang yourself”, which gives the impression that microservices are inherently insecure. But if we do microservices right, we can improve security with a range of measures all designed to prevent further intrusion and disruption. In this talk, you will discover a reference microservices architecture - the sock shop - which we will abuse in order to investigate microservice security on the Kubernetes orchestrator and Weave Net, a software-defined networking product from Weaveworks.

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Gocoverage - Simplifying Go Code Coverage

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Dr. Phil Winder
CEO

Go introduced vendoring into version 1.5 of the language. The vendor folder is used as a dependency cache for a project. Because of the unique way Go handles dependencies, the cache is full code from an entire repository; worts and all. Go will search the vendor folder for its dependencies before it searches the global GOPATH. Tools have emerged to corral the vendor folder and one of my favourites is glide.

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How to Test in a Microservices Architecture

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Dr. Phil Winder
CEO

The testing of microservices is inherently more difficult than testing monoliths due to the distributed nature of the code under test. But distributed applications are worth pursuing because by definition they are decoupled and scalable.

With planning, the result is a pipeline that automatically ensures quality. The automated assurance of quality becomes increasingly important in larger projects, because no one person wants to or is able to ensure the quality of the application as a whole.

This article provides some guidelines that I have developed whilst working on a range of software with microservice architectures. I attempt to align the concepts with best practice (references at the end of this article), but some of the terminology is my own.

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