Machine Learning Consulting & Software Solutions

Machine learning and artificial intelligence have already established themselves as powerful tools for businesses to harness in their search for efficiency, cost-reduction, and improved operations. 

By relying on these high-tech solutions in the form of applications suitable for everything from early-stage startups to mass enterprises, machine learning is quickly becoming a vital part of the world of business. But how and why these technologies, and is it possible to use them in any industry?

Many companies are data and process-heavy but still rely on traditional methods of managing them. With artificial intelligence apps built by expert agencies housing master machine learning engineers, several industries are already being transformed, from supply chain management to healthcare, eCommerce, and education to name just a few. 

These technologies are helping real-world businesses better understand customer data, automate tedious processes, and have the added benefit of iterating as they scale. That means that if you choose artificial intelligence technology as a solution for your business, it will grow with you.

Machine learning is as the name implies ultimately a learning process. In essence, code is used to create an algorithm that can extract information from labeled or unlabelled data. What makes machine learning techniques different from other types of algorithm models is that the code is made to adapt and change as it gains more information. 

This foundation helps the application identify and analyze patterns, make behavioral predictions or take on other intended goals.

There are also different types of machine learning tools and these different categories have distinct purposes. We discuss artificial intelligence and the key differences in our FAQs below.

Machine learning as a solution is usually divided into four categories: supervised, semi-supervised, unsupervised, and reinforcement learning.

Supervised learning refers to a machine learning algorithm that is manually taught information. Much like a child learning in school, the application is given known datasets and told the desired outcome. Then it’s up to the supervised machine learning algorithm to arrive at the intended destination.

Semi-supervised machine learning solutions are rather similar to supervised learning ones, except the data provided for the algorithm is a mix of known and unknown data sets. The goal here is to teach the algorithm to understand the known data and then to use that as a basis from which to label or categorize the unknown data.

Then there is unsupervised learning. This is the wild west of machine learning, where the algorithm is left to study and interpret large data sets without any input or supervision. The idea here is that the machine learning app will find a way to categorize the data into some sort of structure.

And last but not least there is reinforcement learning. Here, the algorithm is given a set of actions, requirements, limitations, and the expected final values. The machine learning solution tries to achieve the end result through various methods in order to find the most efficient one. The app is allowed to learn through trial and error, which iteratively helps it get to the best end result.

For the difference between machine learning algorithms and machine learning models, check out this post.

Machine learning and artificial intelligence both already have existing footprints in the business world, from smaller companies to some of the largest enterprises around the globe. In fact, different types of AI and supervised learning algorithms are already used to customize experiences on smartphones, web browsers, and other online platforms.

From Tesla’s machine learning models for their autopilot modes to social media platforms adapting to show users content they might like, there is machine learning in more applications today than ever before.

Streaming giant Netflix is said to have saved as much as $1 billion due to its machine learning algorithm for content recommendations,  while Google and other search engines commonly use machine learning to improve their search results, maps, and language translation capabilities. And they aren’t the only ones - companies like Salesforce and Hubspot use it to enable user automation that improves marketing flows.

Machine learning is an incredible tool because it is both flexible and adaptable. This type of technology can be applied to any industry, and all industries can enjoy the benefits of improved efficiency, data-tracking, cost reductions, and much more. 

What makes these algorithms even more powerful is that they can go from as simple as tracking sales or social media performance to complex solutions for language or audio recognition. 

The most important thing to know about machine learning for businesses is that it is vital to work with a machine learning agency that has a proven track record in delivering machine learning and AI solutions for various industries. Choosing the wrong machine learning partner can be highly detrimental and end up costing businesses thousands of dollars in wasted time and effort. 

NineTwoThree Venture Studio is an experienced machine learning agency most recently honored for the second time by Inc. 5000 and having been ranked as the top development and mobile app development company in Boston by Clutch.co. 

Let us organize your data to make better decisions or build Machine Learning and AI software to improve your business.