How to Create an AI Solution? Your Guide to Building an Intelligent and Efficient AI Solution

Building an AI system is different from traditional computer programming, where software doesn’t automatically improve itself.

However, with the right team in place and following the steps outlined in this article, you can successfully implement an AI solution within your business.

Many individuals are interested in knowing “How to create an AI solution” these days. To make an AI, you need to:

• Identify the problem you’re trying to solve
• Collect the right data
• Create algorithms
• Train the AI model
• Choose the right platform
• Pick a programming language
• Deploy and monitor the operation of your AI system

What is an artificial intelligence (AI) solution?

An AI solution is a computer system that mimics human intelligence to make decisions or carry out tasks. This can be anything from providing customer support to autonomously driving a car.

To create an AI solution, businesses must first understand what they want their AI system to achieve and then design and implement it accordingly.

Some interesting facts and figures related to artificial intelligence

• According to Statista, revenue from the artificial intelligence (AI) software market worldwide is expected to reach 126 billion dollars by 2025.
• As per Gartner, 37% of organizations have implemented AI in some form. The percentage of enterprises employing AI grew 270% over the past four years.

• According to Servion Global Solutions, by 2025, 95% of customer interactions will be powered by AI.
• A 2020 report from Statista reveals that the global AI software market is expected to grow approximately 54% year-on-year and is expected to reach a forecast size of  USD 22.6 billion.

What are the benefits of artificial intelligence (AI) solutions?

There are many benefits associated with implementing an AI solution within your business.

Perhaps the most notable use is that AI solutions can automate repetitive tasks.

This can free up your employees’ time to focus on more critical tasks.

Another benefit of AI solutions is that they can help you to make better decisions.

This is because AI solutions can process large amounts of data quickly and identify patterns that humans may not be able to see.

How do you develop an AI solution?

There are many different ways to develop an AI solution. The most important thing is to clearly understand the problem you are trying to solve and the available data.

Once you have this understanding, you can start exploring different AI techniques that may be able to help solve your problem.

• One popular technique is called machine learning.
  • This involves training a computer algorithm on data to learn to recognize patterns.
  • It can be used for image recognition or predicting future events.
• Another common technique is natural language processing (NLP).
  • It involves teaching computers to understand human language so that they can perform tasks such as sentiment analysis or question answering.

Once you have decided on the AI techniques you want to use, you need to implement them in a software solution. It usually involves writing code in a programming language such as Python or Java.

Once your AI solution is up and running, you will need to monitor it and ensure it is working correctly.

Stages of developing an artificial intelligence solution

The following are the detailed stages for developing an AI solution:

1. Identifying the problem

Ask questions like

• What are the primary goals of your AI project or initiative?
• Is there an end goal?
• Is there a specific set of problems that your AI solution will solve?
• What are the resources required for developing your AI solution?
• Where will you get the data?
• How will you select and train the algorithm?
• How are you going to monitor your AI solutions’ success?

Also, remember that AI is not the remedy for all difficulties. It’s simply a tool that may be utilized to tackle them.

2. Preparing the data

One might believe that the long strings of code representing the algorithm are the foundation of any working AI system. But in reality, that’s not the case.

Data is a fundamental component of every AI toolkit, and there’s no way around it.

Before an AI model is run, the data must be verified for errors, labels must be assigned, and a chronological order should be established.

3. Deciding the algorithm

The algorithm is an essential element of putting up an AI system. The algorithm may alter the form it takes depending on the learning type. As mentioned below, there are two primary approaches to learning:

1. Supervised Learning: A computer program is provided with training data set, and its task is to learn a general rule that can be used to predict new data output.
2. Unsupervised Learning: A computer program is given a data set but not told what to do. It must find some structure or patterns within the data on its own.

4. Training the algorithm

The selected algorithm must be trained to ensure the model’s accuracy, so the next logical step in building the AI system is to teach the algorithm.

There are no clear guidelines for model accuracy. However, it is still critical to maintain a certain level of precision within the framework that has been chosen.

The key to developing a successful AI system is training and retraining. It’s only natural that the algorithm would need to be retrained if the required accuracy isn’t achieved.

5. Selecting the best language for building AI

This is one of the most important decisions you will make while building an AI system.

When building an AI system, it is essential to remember that the goal is not to create a perfect replica of human intelligence but rather to make an intelligent and efficient system.

That means trade-offs must be made between accuracy and speed, flexibility and robustness, and interoperability and understandability.

The language you select for developing an AI system must be easy to learn and use, scale well, and has good support for future AI development.

Some popular languages for AI development include Java, Lisp, C++, and more modern languages, like Python and R.

6. Platform selection

It’s crucial to pick a platform that offers you all the services you’ll need to create your AI systems instead of requiring you to purchase everything separately.

The ML platforms are designed to assist with learning and allow you to construct your models.

Popular platforms like Microsoft Azure Machine Learning, Google Cloud Prediction API, and TensorFlow all help users with:

• Data pre-processing
• AI model training
• Evaluation prediction

Application of AI in your business operations

Developing artificial intelligence (AI) systems is getting more accessible and less expensive. Collecting valuable data is at the core of building a successful AI system.

The more sophisticated your AI technology, the better it can analyze vast amounts of data to learn how to carry out a specific activity.

AI’s applications extend into many sectors of life. The following are some of the most prevalent uses of artificial intelligence (AI) in everyday life:

1. Speech recognition

Speech recognition, often known as computer speech recognition or automatic speech recognition (ASR), is a function that utilizes NLP to convert human speech into written text.

Siri, for example, uses voice searching through speech recognition.

2. Customer service

95% of all customer interactions will involve artificial intelligence by 2025. Businesses are turning to online virtual agents instead of human representatives to handle customer service.

3. Computer vision

In this example, AI technology extracts meaningful data from digital photographs, videos, and other visual stimuli. On social media, you may witness its use in photo tagging.

4. Discovery of data trends

AI algorithms may use customers’ actions to detect data patterns, allowing businesses to create cross-selling plans.

As a consequence, during the check-out process, relevant add-on suggestions can be provided by companies. That’s where predictive analytics software comes in.

5. Fraud and risk detection

Complex calculations, data storage requirements, and system integration difficulties can be daunting. However, introducing artificial intelligence (AI) into your organization’s architecture may help you overcome these barriers.

AI software is capable of making decisions about your data in real-time.

For example, the software may generate risk assessment models such as fraud and risk detection, targeted advertising, and product recommendations.

One of the primary problems that artificial intelligence tackles are payment and sensitive information fraud.

Companies utilize AI-based systems to detect and prevent this type of fraud effectively.

6. Automated stock trading

AI-based high-frequency trading platforms make thousands or, sometimes, millions of trades each day. As of 2020, half of stock market trades in America were automated.

Allied Market Research estimates that the global algorithmic market will account for $31.2 million by 2028.

Concluding thoughts: Are you ready to create an AI solution?

Developing an artificial intelligence (AI) solution for your organization will be about the code, the thinking, and the model underlying it.

AI or artificial intelligence has much potential for many coders out there. On the other hand, this technology is still in its early phases.

With that in mind, AI is progressing at a breakneck pace, and it’s only a matter of time before it achieves more sophisticated activities.

This means it’s more vital than ever to know how to create and construct an AI system.

Although we only glanced at some of the information needed to build AI programs in a general sense, this subject is broad and complex.

Therefore, if you are thinking of merging artificial intelligence (AI) into your business process, it is only appropriate to get professionals on board.

Opt for a digital consultant that could find the most efficient, resource-saving, and customer-centric way of developing an artificial intelligence solution for your business.

Now the question you might be having is related to AI project estimation and the pros and cons of outsourcing it! Well, let us give you a quick overview.

AI project estimation

The procedure for creating an AI project estimate depends on various parameters. To help you understand it, here’s a quick rundown of what it looks like while working with Rapidops Inc.

1. We do the estimation after defining the end user problem and the skillset required to resolve the problem(s).
2. Many clients show up with scattered and unclear requirements, which is why they need our expert advice not only for the project at hand but also for defining the project and building the solution architecture.
3. One of the most crucial parameters in AI project cost estimation is data engineering. This phase also takes up a great amount of time for any AI solution development team.
4. The first and foremost step is to decide from where to avail the data required for training the ML models. The quality and quantity of data are very critical in this phase.
5. Each AI project comprises exploratory data analysis or the search for cutting-edge technology in a specific domain.
6. The next phase is selecting the tools and technology to ensure the development of a quality product with regard to the project.
7. With our expertise and learnings from previous AI projects, we predict the time required by each of these components.
8. We also anticipate how many of these components will actually be required in the current project.
9. The actual implementation time will vary depending on the availability and the volume of the data.
10. We aim to complete it at the earliest and focus on developing salient machine learning (ML) algorithms during this period.
11. We also focus on maturing the model over a period of time with the help of new data sets.
12. The next phase is maintenance, which depends on multiple factors such as:
    • Response time of back-and-forth communication
    • Strategy agreed upon while making changes to the model
    • Changes in input data, availability of new data sources, etc.
13. The last stage is the integration with the existing infrastructure.
14. Our experts engage with you and describe the scope of work, which is then estimated by a specialized team of developers.
15. Finally, we will estimate the cost of development as well as infrastructure based on factors such as:
    • Cloud or on-premises program
    • Scope of scalability
    • Requirement of other computational resources
    • Backup policy

**The price of infrastructure may differ based on the client and overall project requirement.

Real-life example of AI solution cost estimation

For instance, we know that a particular client’s AI project is going to exist in AWS and will be using more than one P2/P8  instance from an existing AMI.

Now while having a conversation with the client, it is deduced that the custom Elasticsearch features will not make the cut and even though the regular search features are required, they won’t be sufficient.

So, we will build a hybrid ML model with existing document search tools..

Now, this system will need TensorFlow, Word2vec, Doc2vec, Keras, and Flask to expose the feature set as RESTful JSON service, just like Elasticsearch.

The project also requires a server for a REACTJS GUI, so we might opt for Jupyter notebook. Jupyter notebook is an excellent choice for developers who own the project as they will be able to easily run the code on servers.

Basically, the idea is to be efficient in the whole development process. And when we talk about being efficient, it covers all the resources required for successfully carrying out the client’s AI project.

So, if you are looking to turn your vision into a working AI solution, then do get in touch with our experts, and we will be happy to be your digital product partner.