Big Data Opportunity that Only a Handful Took in 2024

The demand for Big Data Machine Learning technologies has tilted the current startup industry in its favor where $500 billion (US) could be invested in the next 4 years. While the dollars keep pouring, what we lack here is a seriously good talent that can lead and manage teams of Data Engineers, Analysts, Technical Writers, Researchers, and Product Developers.

According to a recent report on Big Data Machine Learning startup industry, the spending in developing and promoting the AI-based technology is 300% higher compared to what goes into hiring and recruiting. Clearly, there is a major dearth in how people are handled and hired for the most prolific job in the history of modern century. A big proportion of Hiring budget still goes into developing the top line of AI leaders and Scientists who are entrusted with building a roadmap for Data Science, AI and Ml projects.

We keep looking for determined AI ML startups that are clear in their mind and above the frivolous and contagious practice of ‘rinse and repeat’ regime in the industry.

Here are our top picks in the startup ecosystem who are working with Computer Vision, 3D Image Processing, Robotics, AI-based Data Analytics and so on.

Computer Vision

What is Computer Vision?

Computer vision is an extended specialization within Computer Science that enables a computer to simulate human’s ability to see, process and handle visual objects, and provide a logical output. Using Computer Vision, the computer not only sees the object in front but also analyzes the object to process data and deliver next steps on what to do with it.

Applications of CV are finding their way into radiology, surgery, mining, metallurgy, aero space and aeronautics. Currently there are close to 900+ companies in this field with a collective evaluation of 10 billion USD as of Jan 2019.

Some of the leading startups in CV are –

• Sense Time
• Intello Labs
• Veo Robotics
• Occipital
• KeyMe
• Xmov
• Contilio
• Sensifai
• Matterport
• Zyper
• Hive

Who Do What?

At Hive, the AI Computer Vision team helps their customers to solve complex business problems. Their AI as a Service is one of the first full-stack AIOps and Innovation platform built on the pioneering concept of Machine Learning Data Science for Big Data applied to the ‘www’.

The Company has a clear line of distinction for Data Sourcing, Data Labeling, AI Model Training and Model Deployment. These are built on their proprietary Load Balancing Engine that is capable of processing billions of API inquiries per month across Computer Vision GPU clusters.

In a recent interview, CEO of Hive, Kevin Guo had explained how AI can be implemented in various Marketing and Sales operations, and how you can fully regulate AI models to predict future outcomes for your organization. It’s more like crystal gazing into your company’s future on a big cine screen using AI.

Another interesting company playing in this field is, Argo.ai. We all hear the clutter of human-driven cars and trucks are going to be steamed off by driverless vehicles. That’s the  future—robots or voice-controlled systems chauffeuring humans and transporting cargoes.

Argo.ai is a key Machine Learning Data Science performer in making the roads safer and driver’s smarter. Barely 4 years old, Argo.ai brings in 100 years of app development and computer vision expertise for safety engineering and automation in cars.

 Where are We, and Where do We go from here?

AI, Machine Learning, Data Science Analytics, AR VR, and 3D Processing are all taking the world by storm. From a futuristic point of view, we may be only at the first decade of innovation but the plot ahead is a complex and twisted one that needs tons of human expertise and craftsmanship in Cloud Computing, Programming, 5G Capabilities and connectivity at a hyperscale level.

Today, we are seeing some amazing applications of AI and Machine Learning applied to healthcare, Automotive, Oil and gas, and marketing and sales.

While half of our teams juggle and huddle to solve the perennial hard data management problems, we can be optimistic about the coming of age in New SQL databases, in-memory grid, data storage and analytics taking us over the line by 2022.

A handful of brilliant minds are coming together from various academia and research institutions to revolutionize the world of science with the physical world in a more acceptable way that would benefit the human race, and other life forms in general.