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An article by Jordan Irabor about processing unstructured textual data using Natural language processing for Node.js.

Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

Modern computer systems can make sense of natural languages using an underlying technology called NLP (natural language processing). This technology can process human language as input and perform one or more of the following operations:

• Sentiment analysis (Is it a positive or negative statement?)
• Topic classification (What is it about?)
• Decide on what actions should be taken based on this statement
• Intent extraction (What is the intention behind this statement?)

The article then takes on the NLP journey:

• Prerequisite and installation of natural library
• Tokenization – demarcating and possibly classifying sections of a string of input characters
• Stemming – refers to the reduction of words to their word stem
• Measuring the similarity between words (string distance)
• Classification – also known as text tagging is the process of classifying text into organized groups
• Phonetic matching

.. and much more. All the code is included and available in this GitHub repo. Also plenty of links to further reading.

Understand new capabilities and migration options for Helm 3, the package manager for Kubernetes. By Martin Hickey on IBM Developer Blog.

Helm 3 is one of the most eagerly anticipated releases for the last year or so. This latest version of Helm, the package manager for Kubernetes, is now available.

The excitement for this release was heightened by the promise of removing Tiller, the in-cluster component of Helm that interacted directly with the Kubernetes API server to install, upgrade, query, and remove Kubernetes resources. However, Helm 3 goes far beyond removing Tiller.

New capabilities in Helm 3:

• Client only architecture
• XDG base directory specification
• No need to initialize Helm
• Release storage changed
• Modifications to charts
• Chart repository status
• Improved release upgrade strategy
• Simplified CRD support
• Helm test framework updates
• Helm 2 interface still supported
• Helm Go library overhauled
• Other changes

… each of this is described in detail in the article. There are a lot of new capabilities worth trying out in Helm 3. Give it a go!

Martin Heinz put together this article on building serverless OpenFaaS application with Go. OpenFaaS which is open source Function as a Service tool for developing cloud-native serverless applications.

You will learn how to setup (IMHO) the ideal project and workflow for developing OpenFaaS functions, as well as creating your first function using Golang.

You might be asking, why - out of all the frameworks like AWS Lambda, Google Cloud Run, OpenWhisk etc. - would I choose OpenFaaS?

• No Vendor-Lock
• Cloud Native
• Any Language
• Performance

To run the functions we need a cloud environment and considering that we will be using local machine, we should probably choose the most lightweight option possible, here author will use Ranchers k3s (alternatively you could also use KinD).

The article then dives into detailed steps how to build your own version of OpenFaas and your first Go function, including set up, unit tests, automation with Taskfile for function deployment, simple CI/CD. Excellent!

Ian Miell wrote this essay about Toyota’s way. The Toyota Way is a set of principles and behaviors that underlie the Toyota Motor Corporation’s managerial approach and production system.. His advice is simple: Ignore the noise, go to the signal.

Once author started reading The Toyota Way, he realised that it was the real deal, and was articulating better many of the things I’d done to make change in business before.

One of the most obvious objections to anyone that foists The Toyota Way (TTW) on you is that its lessons apply to manufacturing, which is obviously different from a knowledge industry. How can physical stock levels, or assembly line management principles apply to what is done in such a different field?

The author points out that the book explains:

• The Toyota Way is a philosophy, not a set of rules
• Waste and pipelines are universal
• How flow is not achieved: Failure to ‘Go and See’

Some excellent points done in this review. A rather shocking amount of time can be spent in knowledge industries producing information that doesn’t get used. Fantastic!

Matt Safford wrote this guide for tomshardware.com about choosing CPU for your PC. Whether you’re building or upgrading a PC, the processor matters a lot. We’ll help you choose the right one for your needs.

Higher clock speeds and core counts can make a major difference in overall performance, providing a snappier system, smoother gameplay and faster completion of intensive tasks such as video editing and transcoding.

Plus, the CPU you choose will also dictate your motherboard options, as each processor only works with a specific CPU socket and set of chipsets.

This article will guide you through:

• AMD or Intel: Which Should You Get?
• What do you want to do with your CPU?
• What generation CPU do you need?
• How do you read the model names and numbers?
• Should you overclock?
• What are the key CPU specs and which should I care about?
• What do you need more: clock speed, cores or threads?
• What socket does my motherboard need for this CPU?

You can’t lose with AMD or Intel: For mainstream users, so long as you’re considering current-generation parts (AMD Ryzen 3000 or Intel 9th Generation Core), this debate is basically a wash. Nice one!

“What gets measured gets managed.” This quote – wrongly attributed to Peter Drucker – tells us that when we monitor information about something, we can make decisions about what it is we’re monitoring and where to go next. An article about monitoring in Elixir by Samuel Mullen.

The article deals with:

• The Value of Monitoring
• Lies, Damned Lies, and Vanity Metrics
• Getting Started with Telemetry

Telemetry is a library giving us a dynamic dispatching library for metrics and instrumentations.” It’s a simple library providing a standardized interface for capturing and handling metrics from monitored events.

Telemetry isn’t a revolutionary idea and the maintainers don’t claim it is. What they do claim is that it provides a simple and flexible means for capturing data. Good read!

CMO asks the experts for some practical tips for better customer experience management and optimisation. An article by Vanessa Mitchell (www.cmo.com.au).

Does CX begin with the customer? Or the product? Is it just customer service, or does it need to feature throughout the customer journey, beyond purchase? Or perhaps it even begins before all this?

It is well known brands concentrate on CX are more profitable, and enjoy more customer loyalty. But even with the statistics, switching a product-focused company to a customer-focused one takes a lot of work and stakeholder agreement.

Some practical tips to make it work for brands mentioned:

• Discover first
• Embrace the emotion
• Define your strategy
• Invest in technology
• Build a bigger picture

… for more follow the link to the article. Some good advice here!

Guide to web components by Vernes Pendić. Web components are one of the most useful, built-in, standardized (W3C), browser features added recently. This article will guide you through understanding what they are and how to use them.

Web components contain 4 sub-specifications that describe the available possibilities regarding DOM element manipulation. The first two, Custom elements and Shadow DOM, are more important and we’ll talk about them further.

The article content is split into:

• Custom elements ⚙️
• Shadow DOM 👻
• Additional functionalities

As such not all browser vendors have taken advantage of all of its capabilities (list of browsers that support the specification) but with the help of polyfills, their use can be extended on the majority of browsers. There is also plethora of links to further resources and reading. Good read.

James Le wrote this article in which he wants to show 3 powerful tools to simplify and scale up machine learning development within an organization by making it easy to track, reproduce, manage, and deploy models.

Managing a machine learning workflow is hard. Beyond the usual challenges in software engineering, machine learning engineers also need to think about experiment tracking, reproducibility, model deployment, and governance.

The article discusses the following:

• Using version control with DVC
• Tracking experiments with Weights & Biases
• Deploying Models with Docker

DVC is a handy tool built to make machine learning models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code. Using emerging platforms can keep your projects organized and make you more productive as a machine learning engineer. Nice one!

What is federated learning? How does it differ from traditional large-scale machine learning, distributed optimization, and privacy-preserving data analysis? Learn from the blog post by Carnegie Mellon University.

Mobile phones, wearable devices, and autonomous vehicles are just a few of the modern distributed networks generating a wealth of data each day. Due to the growing computational power of these devices—coupled with concerns about transmitting private information—it is increasingly attractive to store data locally and push network computation to the edge devices.

The article deals with:

• What is federated learning?
• What are the challenges in federated learning?
• Federated Learning @ CMU
• Future Directions

Much more detail in the article. These challenging problems (and more) will require collaborative efforts from a wide range of research communities. Good read!