Learn Cloudflare, Natural and Ruby

Cloudflare Workers let you write JavaScript Service Workers that run directly in Cloudflare's 200+ edge locations around the world.

With a Cloudflare Worker, you can modify your site's HTTP requests and responses, make parallel requests, and even reply directly from the edge.

Cloudflare Workers are written against a similar API to the W3C Service Workers standard. However, while Service Workers run inside the end user's browser, Cloudflare Workers run on Cloudflare's servers. This means that Cloudflare Workers can implement trusted code (which the client cannot tamper with), and will work no matter what browser the user is running.

In this course, Kristian Freeman will teach you how to build and deploy a Serverless API with Cloudflare Workers. By learning how to build your own serverless JSON APIs, you can effectively manage a highly-available backend for your projects, without needing to learn DevOps or manage services.

Cloudflare secures and ensures the reliability of your external-facing resources such as websites, APIs, and applications. It protects your internal resources such as behind-the-firewall applications, teams, and devices. And it is your platform for developing globally-scalable applications.

cloudflare

Build a Serverless API with Cloudflare Workers

Cloudflare Workers is a serverless application platform that enables you to create and deploy functions to Cloudflare's Edge Network.

Unlike other serverless providers that only have regional data centers, Cloudflare's Edge Network consists of hundreds of servers all over the world.

Cloudflare Workers is a powerful platform, and this course serves as a great introduction for what it can do.

Follow along with Kristian Freeman as you build a localization engine that renders data based on the Edge location nearest to the application's user.

After this course, you'll be ready to start experimenting with your own Cloudflare Workers projects. *Need an idea?* *[Check out the suggested Portfolio Project for this course.](https://egghead.io/projects/introduction-to-cloudflare-workers)*

Introduction to Cloudflare Workers

The “Edge” is where you write and store your code where your data is being produced. This allows for a quick and efficient data transfer, eliminating or severely reducing lag. 

Supabase is a suite of open-source tools wrapping a PostgreSQL database. It provides the building blocks of an app - database hosting, auth, file storage, real-time, and edge functions - so we can focus on what makes our app unique.

Cloudflare Workers are serverless functions that run at the Edge! In this course, we will use the Wrangler CLI tool for interfacing with your Cloudflare account and creating or publishing your Workers.

KV Storage is a cache that Cloudflare makes available to our Workers. It replicates across multiple CDN nodes, making it super performant to cache data.

Combined, these three things give you the benefits of that reduced lag in a smooth and streamlined process. 

Throughout this course, Jon will teach you how to create a small blog that hits the key points of how to:

- Query Supabase from Cloudflare Workers
- Read and write KV Storage cache from Cloudflare Worker
- Cache Supabase response at the Edge with KV storage

And more! 

Cache Supabase data at the Edge with Cloudflare Workers and KV Storage

In this course we’ll work through Natural’s API for natural language processing in JavaScript. We’ll look at how to process text: learning how to break up language strings, find the word roots, work with inflectors, find sequences of words, and tag parts of speech. We’ll learn how to find important stats about a body of text: how to compare strings, how to classify text with machine learning, how to use the tf-idf tool to find relevant words. We’ll look at some of the extra tools Natural gives us, including the dictionary/thesaurus of WordNet, a phonetics comparer that lets us see if two words sound the same, and a spellcheck feature. We’ll also look at tries and digraphs, two data structures that help us better analyze bodies of text.

Natural is a general natural language facility for nodejs. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported.

natural

Natural Language Processing in JavaScript with Natural

The Edge is one of the most exciting new topics in web development. At the front of the pack is Cloudflare, whose network boasts servers located in hundreds of cities all over the world instead of in regions like "East" or "West".

More servers in more locations mean a faster experience for your users.

But what about the data part of the equation?

If the application you're working on skews more toward "reading" rather than "writing" data, Cloudflare Workers KV is a perfect fit.

Once you create a KV namespace— the "KV" stands for "key-value"— Cloudflare keeps track of your application data. The most commonly accessed data will be propagated around the world to the various Edge servers, while less popular data will be stored in more central locations.

Kristian Freeman has produced courses that introduce how to build and deploy sites and JSON APIs powered by Cloudflare Workers.

Today, he will show you how Workers KV can be integrated into a Cloudflare Workers project through building a Todo App.

## The Project

![todo-app-screenshot](https://res.cloudinary.com/dg3gyk0gu/image/upload/v1639014074/course-resources/Build-data-driven-applications-on-the-edge-with-Workers-and-Workers-KV/CleanShot_2021-12-08_at_16.46.39_2x.png)

Before you groan about a bland Todo App, think about it this way: 

The Todo App is the perfect example of the CRUD operations. Cloudflare KV has its own nuances for reading and writing data through a concept called Binding that is easier to grok when you understand the larger example as a whole.

As the course progresses, Kristian shares examples of how you can store more complex key-values, along with data operations like editing and filtering.

One of the coolest features that Cloudflare KV allows for is user-specific data that is tied to the IP address the request originates from. In other words, you'll be able to share your deployed Todo app with anyone around the world, and they will only see their personal items.

## **Gain these Skills**

- Manipulating data using Cloudflare Workers KV
- Make data unique to each user without logging in
- Effectively store lightweight data in Cloudflare Workers KV
- Setting up a key-value store and connected projects
- Reading and writing to a serverless key-value store
- Storing complex values in a Cloudflare KV store

## **Prerequisites**

You'll need to have a basic understanding of what Cloudflare Workers is recommended but not necessary. This course is relatively beginner-friendly into the world of Workers KV that you can get by with no knowledge on Cloudflare Workers. 

But if you want to get the most out of this course, we would recommend taking [Introduction to Cloudflare Workers](https://egghead.io/courses/introduction-to-cloudflare-workers-5aa3) to get a better understanding of how Workers KV comes into play. 

## **What's Next? (You might also like these resources:)**

Follow along with Kristian Freeman as you build a localization engine that renders data based on the Edge location nearest to the application's user. → [Introduction to Cloudflare Workers](https://egghead.io/courses/introduction-to-cloudflare-workers-5aa3) 

Learn how to build your own serverless APIs so you can manage a highly available backend for your projects. No Learning DevOps or managing services necessary. →  [Build a Serverless API with Cloudflare Workers](https://egghead.io/courses/build-a-serverless-api-with-cloudflare-workers-d67ca551)

Using Cloudflare Pages and Cloudflare Workers, it's easy to create dynamic front ends that you can deploy in just seconds from GitHub. → [Deploy Dynamic Frontend Applications with Cloudflare Pages](https://egghead.io/courses/deploy-dynamic-frontend-applications-with-cloudflare-pages-6aaa00a1)

Build Data-Driven Applications on the Edge with Workers and Workers KV

Rails is a web-application framework that includes everything needed to create database-backed web applications according to the Model-View-Controller (MVC) pattern.

rails

Expanding on [Part 1](https://egghead.io/lessons/angularjs-rails-todo-api-part-1), this lesson will look at how to bootstrap AngularJS in a rails application as part of the asset pipeline. With Angular available, you'll see how to communicate with the Rails API using $resource.

Rails Todo API Part 2

We will learn about “stemming,” the process of finding the root of words, often in order to group words by a common base root. We will look at the Porter and Lancaster Stemmers, briefly touch on Natural’s support for Russian and Spanish stemmers, and introduce the function to stem and tokenize at the same time.

Find the roots of words using stemming in Natural

Inflectors are the modifiers of a word that indicate grammatical categories. While Natural’s coverage of inflectors is not comprehensive, we will show how Natural can pluralize/singularize nouns and count numbers. 

Pluralizing nouns and counting numbers with inflectors in Natural

N-grams are sequences of words, where the 'n' stands for the number of words in the sequence. In this lesson, we will see how to find bigrams (2-grams), trigrams (3-grams), and any other length n-gram in a body of text. 

Find sequences of words (n-grams) using Natural

We will learn how to compare how similar two strings are to each other, examining three algorithms: Jaro-Winkler, Levenshtein, and Dice’s Coefficient.  

You should note that none of these algorithms are inherently better than the others. Instead, it's important to choose the one that best fits your text data. 

Compare similarity of strings through string distance in Natural

In this lesson, we will learn how to train a Naive Bayes classifier or a Logistic Regression classifier - basic machine learning algorithms - in order to classify text into categories.

Classify text into categories with machine learning in Natural

In this lesson, we will learn how to train a Naive Bayes classifier and a Logistic Regression classifier - basic machine learning algorithms - on JSON text data, and classify it into categories.

While this dataset is still considered a small dataset -- only a couple hundred points of data -- we'll start to get better results.

The general rule is that Logistic Regression will work better than Naive Bayes, but only if there is enough data. Since this is still a pretty small dataset, Naive Bayes works better here. Generally, Logistic Regression takes longer to train as well. 

This uses data from Ana Cachopo: http://ana.cachopo.org/datasets-for-single-label-text-categorization

Classify JSON text data with machine learning in Natural

Tries are a data structure that provide an efficient way to search for the existence of a word or phrase in a body of text, or to search by prefix. 

Search more efficiently with tries using Natural

In this lesson, we’ll see how to use Natural’s probabilistic spell-checker, which uses the trie data structure. 

Include spell-check in text projects using Natural

Tf-idf, or term frequency-inverse document frequency, is a statistic that indicates how important a word is to the entire document. This lesson will explain term frequency and inverse document frequency, and show how we can use tf-idf to identify the most relevant words in a body of text. 

Identify the most important words in a document using tf-idf in Natural

AngularJS is an open-source JavaScript framework, maintained by Google, that assists with running single-page applications. Its goal is to augment web-based applications with model–view–controller (MVC) capability, in an effort to make both development and testing easier.

angularjs

Rails makes an excellent choice for delivering data to AngularJS via REST apis. In this first lesson of a two part series, you'll see how to create a simple API for CRUD operations on TODOs using Rails. This isn't an introduction to Rails, and assumes you know the basics. 

Rails Todo API Part 1

In this lesson, we’ll take a look at Natural’s phonetics feature. We’ll learn how to check whether two words sound alike, looking at both the SoundEx and Metaphone algorithms.

Check if words sound alike using Natural

This lesson introduces WordNet, which is an important resource in natural language processing. With WordNet, we can look up a word’s definition, or find its synonyms.

Find a word’s definition using WordNet in Natural

An important component of many natural language processing projects is being able to identify the grammar of a piece of text. We’ll learn how to do that with Natural’s parts of speech (POS) tagger.  

There are many tags, and it's worth looking them up online (search "POS tag symbols") to become familiar with them all. 

The setup of the tagger may seem a little strange, but it allows you to replace the lexicon or the rules with a different lexicon or rule set of your choice. 

Tag parts of speech using Natural

By this point we've seen that classification can take a long time, and with more data, it would take even longer. Luckily, Natural provides support to save your classifiers. In this lesson, we will learn how to save a classifier and load it into a new project in order to classify new data.

Using machine learning classifiers in a new project

A part of Natural Language Processing (NLP) is processing text by “tokenizing” language strings. This means we can break up a string of text into parts by word, sentence, etc. In this lesson, we will use the `natural` library to tokenize a string. First, we will break the string into words using `WordTokenizer`, `WordPunctTokenizer`, and `TreebankWordTokenizer`. Then we will break the string into sentences using `RegexpTokenizer`.

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