curious case of AI in the cloud

Data is not the new oil as some have claimed, and Machine Learning is not the new electricity. The shift is so gigantic that itâ??s impossible to come up with a fair analogy

We're living through the most significant technological shift in human history. Our smartphones are connected to thousands of computers and access terabytes of data, and whether we realize it or not, our lives are heavily impacted by algorithms- our news feeds, the products we buy, our food, our transportation, etc.

The shift is significant, not so much because we have access to unlimited computational power and data, but rather, because a larger number of things is now measurable.

The confluence of unlimited computational power and unlimited data, in conjunction with continuous advances in algorithms and hardware, mean that Machine Learning (ML) is the driving force of this major technological shift - and that is significant because computers will play an increasingly important role in our decision-making and ML in how the technology works.

On one hand, measuring at scale allows Machine Learning algorithms to make better predictions which help individuals and businesses make decisions. On the other hand, it feeds the improvement of algorithms that perform tasks that are better suited to computers.

Given the quickly evolving landscape, how can developers and businesses, old and new, best capitalize on this shift to have an impact and stay ahead of the game?

First, it's important to separate hype from reality. We won't have human-like robots walking our streets and performing the very hard jobs that require "soft skills" anytime soon, but AI and Machine Learning are here to stay and they are the new reality of computing.

Data is not the new oil as some have claimed, and Machine Learning is not the new electricity. The shift is so gigantic that it's impossible to come up with a fair analogy. What is clear, is that every business in the very near future will use ML and every developer will work on ML as part of a standard set of computing tools. That's a reality today for those that have embraced it.

Second, understanding the ecosystem is critical. The Cloud plays a significant role because it gives developers and businesses access and flexibility in storing as much data as needed, and in instantly scaling as needed, at low prices - size doesn't matter.

Individual developers, small teams, mid-size, and large enterprises can all leverage the Cloud and AI. And it also means that consumers, in many verticals, will continue to expect competitive performance and free or near free functionalities. So, we have the technology (cloud, data), businesses (every business becomes a tech business), and consumers (the "end point" for collection of data and impact).

Third, identifying internal opportunities and modifying processes is pivotal in driving that shift. For developers, it means getting up to speed on ML and understanding the subtleties of what different algorithms offer. For medium-sized companies, that's having a strategy to collect the right data, and execute on it, step by step. For large companies, it's ensuring that data pipelines and processes align with experimentation and leverage AI. In all cases, it's having clarity on what needs to be measured so that the right metrics are in place.

It's also important to avoid pitfalls, and there are many. On one extreme sit the skeptics that think AI does not apply to them because they're very far from being able to apply it. This is usually a misguided notion, as the Cloud and many open source tools allow fairly quick starts. Additionally, in most cases, data is already in existence in one form or another. On the other extreme sit the dreamers who believe that AI and the Cloud will magically solve everything.

In practice, the biggest challenge most companies face is not having the right expertise. For developers, learning ML has become increasingly accessible, but a common issue is a disconnect between those with the technical skills and those with the business experience. The reality is that no matter what stage you are in - as a developer, or as a company - the time to embrace AI is now.

AI can be applied anywhere where you can collect data, measure, and make predictions. Identify those opportunities and tie them to a specific customer or business needs and start ensuring the quality of the data is good and apply basic methods to start with as a proof of concept. This includes asking the right questions. Then, look to the Cloud because it gives you the flexibility to easily, quickly, and cheaply try things out, and identify the right open source and learning resources available.

In this process, keep in mind what three factors above: separate hype from reality and set reasonable expectations; have a clear understanding of the technology (cloud, data), the business, and the "customers" for the task, and have clarity on what is to be measured and what the goals are.

If you manage to put these things together and get started, even with a small project, you'll already be participating in that gigantic shift. Just be sure to have clear goals, iterate, and experiment.

Source: Economic Times