2022 was to be the year we leave COVID-19 behind; instead, the year began with a surge of the Omicron variant. It also started with the realization that this virus is here to stay with us in one form or another. This means organizations cannot put their plans on hold. And in many cases, they need to be smart about how their dollars are spent attracting business.  

Rather than an abstract discussion on what is new and hot in the field of machine learning, this year we decided to talk about the defining trend in applied machine learning and computer vision – democratization!

Democratization of Applied Machine Learning  

There are several evolving trends in the industry around how machine learning (ML) is used and who leverages it.

Shift in focus from platforms to solutions

As ML sees a renaissance with more and more companies wanting to leverage the technology in their businesses and operations, there is a stark realization. The market is replete with ML platforms – very capable software – that puts the onus of creating the ML solutions and models on the users. But platforms are hard to use and require a level of sophistication many companies don’t yet have. It’s akin to driving a Formula 1 race car. You better know what you’re doing, or you could be in for a world of hurt and land in the ditch.

Further, platforms are expensive, have long solution cycles, and are fraught with risk. However, there is a trend emerging in enterprises, ML platform vendors, data warehouse vendors and system integrators; they are all seeking proven pre-built ML models that solve specific use cases in the many domains, rather than a promise of “it can be done.” It’s all about speed and time to value. Why build it if you can buy it?

Adoption of ML solutions across the broader industry

ML has typically been the domain of well-funded, Tier 1 players in most industries. This makes sense as the Tier 1 players have the resources to acquire and retain talent as well as invest in platforms and related tools. However, Tier 2 companies across many verticals are also aggressively looking for ways to turn their data easily and quickly into a strategic weapon to drive revenue while reducing costs and risk.  

Moreover, industries that previously have been reticent to adopt new technologies are beginning to embrace artificial intelligence (Ai), ML and computer vision in the ongoing hunt to create a sustainable competitive advantage.  

Here are some examples from the broad swath of companies leveraging ML:

• Farms are beginning to see how tens of thousands of cattle heads can be monitored for well-being, aggressive behavior, or animal cruelty – all using the power of computer vision and ML at the edge.
• Not for Profit organizations are beginning to employ ML to identify gift giving patterns, who to target as a reliable donor, and what channels to use for the most effective donation appeals.
• Construction and inspection industries are eyeing significant cost reduction in building and bridge inspections. With drone videos and still pictures fed into a ML algorithm, inspectors can see the areas of concern and where they need to spend their time. Compare this to the laborious talk of manually inspecting reams of pictures and videos for signs of corrosion, cracks, loose masonry, and a host of other defects.

Dealing with bias in Ai and ML systems

The broader adoption of ML cuts both ways. Many more benefit from what ML has to offer. Unfortunately, many are unfairly impacted because of the bias baked into the data from which the machine learns. The problem is exacerbated by the fact that the bias can be perpetrated with no malintent; “it was not me, the computer said so.”  

Minority groups being turned down for mortgages or business loans are examples of how bias plays an implicit role based on years of loan data when these groups could not borrow due to active bias. It is tricky to challenge the determination made by a machine given the refrain that humans have biases, but machines don’t.  

There are several types of biases that are frequently encountered. The roots of these biases lie in both data and the operators. This includes data such as prejudiced data, inclusion of such data into model training, or exclusion of data that would have removed bias. Observer’s bias and that in measurements are frequent culprits too.  

This year is expected to be a turning point in addressing biases using a plethora of tools, techniques and processes that are becoming available.

How ElectrifAi Can Help

ElectrifAi has been at the forefront of the ML democratization movement well before the industry started to edge in that direction. As more and more platforms became available in the market, the need for solutions became a clear priority.

Most companies do not have the wherewithal to license the platforms, build data science teams, procure subject matter experts, and build the solutions. It takes too long, and success is far from assured.  

ElectrifAi focuses on time to value for customers and lowers both costs and risks. Access to the benefits of ML becomes available in 1 to 2 months at a small fraction of the cost.  

With a vast library of pre-built ML models, ElectrifAi covers a wide set of domains and ever-growing number of use cases. In the company’s mission to make the benefits of ML available to all in an affordable manner, it opens doors for many companies that did not know where and how to start with ML.  

ElectrifAi further lowers the barrier to entry by offering Machine Learning as a Service (MLaaS). The company takes care of the infrastructure, the models, everything needed and exposes an endpoint to route the data through its service. Access to ML has never been easier!

The company’s enviable team of data scientists are also looking at ways to take the bias out of the solutions.  

2022 is going to be a defining year when ML becomes available to all, and ElectrifAi will continue to make this a reality.

If you would like to discover how ML can benefit your company, reach out to us today for a custom demo!