Let's consider the main stages of the task:
Setting the goal. First, you need to understand what tasks the neural network will solve. For example, classifying pictures, determining tonality and processing text, and so on.
Forming a data set. At this stage, you need to prepare the information that will be used by the neural network for training. The more data is used, the more perfect the final system will be.
Writing code. This will require choosing a programming language.
Stages of creating your own neural network for business
Training. Logical chains loan data package need to be formed. To increase the speed of training, you can run the neural network on a dedicated server.
Analysis of results. It is necessary to evaluate the efficiency of the neural network by various parameters. For example, to determine the accuracy of its predictions, the speed of learning and deviation.
Open source. This is the final stage, where you need to double-check the neural network and prepare it before starting work.
Options for achieving neural network scalability
This is one of their biggest challenges. AI must be built in such a way that it can handle ever-increasing amounts of data. The more users a system has, the more urgent the need to scale.
There are several ways to solve this problem:
Use of cloud computing resources. For example, Amazon Web Services, Microsoft Azure or Google Cloud Platform. The services provide large computing capacities for data processing and interaction with multiple users. Thanks to this, the neural network can easily scale.
Using distributed computing systems. For example, Apache Hadoop or Apache Spark. Such platforms make it possible to distribute computing tasks among a number of nodes. This reduces the load on computing resources and helps to process big data.
Download a useful document on the topic:
Checklist: How to Achieve Your Goals in Negotiations with Clients
Situations when a neural network is useless for business
Let's consider circumstances in which they will not help.
The person or company has no idea of the desired outcome
Regardless of the complexity of AI, it will not be able to solve a problem if the user has no idea about the result that needs to be obtained. For example, no neural network will be able to create a logo for a business if the company employee does not know what the brand philosophy is.
AI solves clearly defined problems. It is not a magic tool that can read people's thoughts. In order for the neural network to respond to the necessary requests, they must be composed correctly. Before this, it is important to talk to the brand manager and marketer to get information about the desired result.
It is necessary to obtain a non-standard solution
Neural networks are not capable of inventing something fundamentally new. Any AI forms template options based on existing materials. In addition, simple neural networks have no idea about the interests of clients from certain areas.
Business has problems
As already noted, the neural network is a useful tool, but it cannot solve all the problems for the company itself. For example, AI is unlikely to create a unique selling proposition that will immediately increase the company's revenue and bring it out of crisis.