Rise of the Canonical Stack in Machine Learning

Rise of the Canonical Stack in Machine Learning

Rise of the Canonical Stack in Machine Learning. How a Dominant New Software Stack Will Unlock the Next Generation of Cutting Edge AI Apps

With every generation of computing comes a dominant new software or hardware stack that sweeps away the competition and catapults a fledgling technology into the mainstream.

I call it the Canonical Stack (CS).

Think the WinTel dynasty in the 80s and 90s, with Microsoft on 95% of all PCs with “Intel inside.” Think LAMP and MEAN stack. Think Amazon’s S3 becoming a near universal API for storage. Think of Kubernetes and Docker for cloud orchestration.

The stack emerges from the noise of tens of thousands of other solutions, as organizations look to solve the same super challenging problems. In the beginning of any complex system, the problems are legion. Stalled progress on one blocked progress on dozens of others. But as people solve one problem completely, it unlocks the door to a massive number of new solutions.

In the early days of the Internet, engineers worked to solve thousands of novel problems all at the same time, with each solution building on the last. Once someone invents SSL, you can do encrypted transfers of information. Once you have the Netscape browser that can do SSL you can now start working on e-commerce. Each solution unlocks a new piece of the puzzle that lets people build more and more complex applications.

As more and more pieces of the stack come together the “network effect” kicks in. Each node that comes online makes the network more and more valuable. Suddenly, when you’ve added enough people you hit a “tipping point” and adoption accelerates rapidly up an exponential S curve. Once it accelerates fast enough you hit critical mass and adoption becomes unstoppable.

When a CS forms it lets developers move “up the stack” to solve more interesting problems. Over the last few decades we’ve seen traditional software development reach dizzying new heights as better and better stacks emerged. It once took a small army of developers to write a database with an ugly interface that could serve a few thousand corporate users in the 1980s and 1990s.

It took only 35 engineers to reach 450 million users with WhatsApp.

That’s the network effect in full effect, where any team can leverage cutting edge IDEs, APIs, and libraries from dozens of other teams to deliver innovation at a breakneck pace.

We can track the formation of a CS with the famous Technology Adoption Curve.

Source: Wikimedia Commons

devops mlops artificial-intelligence venture-capital machine-learning

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

AI(Artificial Intelligence): The Business Benefits of Machine Learning

Enroll now at CETPA, the best Institute in India for Artificial Intelligence Online Training Course and Certification for students & working professionals & avail 50% instant discount.

Learning in Artificial Intelligence - Great Learning

What is Artificial Intelligence (AI)? AI is the ability of a machine to think like human, learn and perform tasks like a human. Know the future of AI, Examples of AI and who provides the course of Artificial Intelligence?

Artificial Intelligence, Machine Learning, Deep Learning 

Artificial Intelligence (AI) will and is currently taking over an important role in our lives — not necessarily through intelligent robots.

How To Get Started With Machine Learning With The Right Mindset

You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Artificial Intelligence vs. Machine Learning vs. Deep Learning. We are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning