How machine learning helps companies increase scalability and improve operations

Recent research reports that more than 70% of respondents stated that machine learning was important or critical to their business operations. This indicates that your competition is probably already implementing machine learning and you will probably need to do so too.

But let's enjoy the breaks for a moment. Before rushing into implementing new technologies, it is prudent to first know what machine learning is and how it helps a company achieve its goals. Let's answer these questions. First, let's define exactly what machine learning is and how it can be used to increase scalability and improve operations.

What exactly is machine learning?

The ability of a machine to make decisions and/or perform some task(s) based on learning or acquired knowledge without any appreciable intervention from a human being .

Okay, so what does this mean in practice? To answer this question, we need to define some other terms in relation to machine learning:

• Data mining. The process of analyzing large data sets to identify underlying patterns and/or mathematical relationships, which typically requires substantial computational power.
• Neural networks. Mathematical models based on the topology of the human brain (especially neurons, synapses and their connectivity), often used for processing and/or analyzing data in order to discover or form classifications.
• Deep learning. A subset or type of machine learning in which neural networks are used to perform data mining on large sets of unstructured data or data of unknown structure. In many cases for learning and/or classification.
• Unstructured data. Unstructured data is information that does not have a pre-defined data model. Or this data is not organized in a predefined way. It may have an internal structure, but it is not structured by predefined models.
• AI. The theoretical or actual ability of a computer (or machine) to display, display, or perform activities that are normally performed by or require human intelligence.
• Autonomous. Capable of performing a function or action without human control. There are several levels of autonomy; as semi-autonomous (partially controlled by a human) and fully autonomous (no direct human control), under which most machine learning falls.

Now let's get back to the question of the practicality of machine learning for business. Based on the above definitions, we can define machine learning for business as:

The use of an AI technique ; such as neural networks to apply deep learning to a business activity or problem with the aim of discovering ways to improve or improve operations .

Let's see how this can be done.

How Machine Learning Helps Businesses Grow and Prosper

Machine learning can be used to help in two areas that require the most attention: growth and operations. Just as implementing a new strategy or process requires that your software development be agile , so does the use of machine learning.

Machine Learning to Grow Your Business

Customer acquisition

Machine learning can be used to analyze industry data and trends, identify and then target potential customers with focused and relevant marketing strategies.

Customer loyalty

By analyzing customers' habits and preferences, techniques and interactions can be designed and adapted to accentuate why they return and address why they leave.

Improving operations with machine learning

Internal data processing

By monitoring and evaluating internal processes, operations and networks, focus areas of efficiency improvement can be identified and then methods for doing so implemented.

Usability and access

Machine learning can be used to deploy interaction tools such as chatbots to improve customer interface and interactions, and to improve data security and establish and maintain customer trust.

The list above is just a partial list of how you can leverage machine learning technology to improve your company's operations and trajectory. Because every business is unique, the best use of machine learning is to develop custom software that is focused, targeted, and uniquely meets your needs.

Now we know the benefits of machine learning. The next step is implementation. If you are thinking about applying machine learning or want a better ROI on your current deployment, then you need the right strategy, tools, knowledge, and expertise. agility in the software development process to optimize its use.