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Quantum Computing, Edge Analytics, and Meta-Learning: Key Trends in Data Science and Big Data

(This is a guest post from Eric Hendrickson.)

2018 marked the beginning of the rise of big data analytics and data science. It was the year when enterprises and organizations across all sectors started realizing its benefits and potential.

This has carried over in 2019, with a slight twist: it’s now shifting to become more consumer-centric rather than business-focused.

One reason for that is, big data analytics and data science have paved the way for the development of tools and platforms powered by machine learning and NLP like chatbots and automation tools.

The advent of these tools allows businesses and organizations to increase the level of personalized service they provide to their customers. And the customers love it!

As a result, businesses continue to look for ways and means to streamline and automate their business processes. That way, they can accomplish more while requiring fewer resources.

Three specific branches in data science are expected to play significant roles in 2020 and beyond: quantum computing, edge analytics, and meta-learning.

Let’s take a closer look at each one and see what role they’ll play in the coming decade.

Key big data science trend #1: Quantum computing

Experts predict that the coming decade will signal the birth of the next generation of supercomputers they call quantum computers.

Companies like IBM, Google, and a few startups have already begun working on these supercomputers, each hoping to be the first to launch these next-gen computers.

The most significant difference between these computers and the ones we currently use today lies in the way how they store and process information.

The computers that you’re using stores information using the binary system (either a 1 or 0). 

Quantum computers, on the other hand, take this to the next level by using quantum bits (also called ‘qubits). 

Qubits are essentially points within each of the two states used in the binary system. These allow more information to be stored within the two binary states. 

The idea here is that since qubits expand the capacity of the binary system currently being used by computers, a quantum computer can run complex computations more quickly and easily.

More importantly, quantum computing is anticipated to break through the barriers programmers currently face when it comes to developing deep learning systems and frameworks.

Right now, quantum computing is still a concept. But already many academic institutions are incorporating these into their artificial intelligence courses.

That way, developers and programmers that complete these course will have an edge the moment this concept becomes a reality.

Key big data science trend #2: Edge Analytics

Big data is increasingly becoming a vital tool for decision-makers to come up with initiatives and solutions for their businesses to scale and remain profitable. 

It’s also what makes IoT devices as well as apps and platforms using AI and machine learning technology to work.

And this is why edge analytics is proving to be indispensable more than ever because it provides data required in real-time.

The massive amount of data now available, combined with workforce trends like telecommuting, have caused businesses to migrate their data storage to the cloud. 

This is not only a more cost-effective option but also a convenient one because data stored in the cloud is easily accessible anywhere at any time.

The drawback here is that data retrieval is entirely dependent on the speed of your internet connection and the reliability of your cloud storage provider. 

When your cloud storage provider experiences downtime or your internet connection slows down, it’ll significantly affect the effectiveness and efficiency of your IoT devices.

What edge analytics does is that it identifies data required to process functions that are either routine or time-sensitive and puts it in the “edge” of the cloud. 

As a result, you significantly reduce the need to access all of the data stored in the cloud, quickening the data retrieval process.

In the coming decade, edge analytics will experience an upgrade through the introduction of the “digital twins” concept.

A digital twin is a virtual re-creation of your IoT device’s system and framework. You can then utilize this to identify areas of improvement and conduct enhancement experiments.

What’s great about this concept is that any modifications that you do to the framework and system won’t affect the performance of your actual device as you make the enhancements.

Again, this trend is still in its infant stage. But the potential benefits that it promises to provide developers and programmers is enough to keep an eye out for this.

Key big data science trend #3: Meta-Learning

Meta-learning is often referred to as machine learning for machine learning tools. 

Here’s why:

Machine learning involves “teaching” a system or device to carry out tasks and processes by analyzing data analytics and patterns. 

The speed in which your program “learns” is dependent on the quality and quantity of the big data received.

But what if the quality of the data is substandard or there’s an insufficient amount of data?

This is where meta-learning comes into play.

Meta-learning is an algorithm that runs alongside machine learning. Think of it as a tutor helping machine learning to understand, predict, and perform the necessary actions based on the data provided, 

In effect, meta-learning helps speed up the learning process of your tools and platforms utilizing this technology.

Of the three different trends, this is so far the one that developers are already benefiting from.

Already, there have been several meta-learning tools that are now available for you to learn and use. 

AutoML is one example. Google developed this meta-learning tool stack for machine learning systems developed on the Google Cloud Platform.

Another is AutoKeras, which is not only more affordable than AutoML but is also compatible with any machine learning tool or platform developed using Python.

The best time to prepare for big data and data science trends is now.

The world of big data and data science is rapidly evolving. So, even though it may be years before some of the trends discussed here will come to fruition, you’ll need to start taking steps to prepare yourself.

Speed and efficiency is the name of the game when it comes to getting ahead as a programmer or developer. Those who will be ready and waiting when these trends become realities are those that’ll get ahead.

And those who will get ahead of the pack are will experience a more lucrative and successful career.

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