AI Software Development

Our Data and Big Data engineers will analyze the impact of Artificial Intelligence and Machine Learning on software development; to achieve this we have established the following procedure.

01. Definition of the problem


We analyze your software requirement in order to identify what you want AI to solve. We identify the problem and define the solution; as well, we identify use cases that allow us to build what your company needs in order to achieve the objectives.

02. Definition of casuistry


The first and most important rule of AI software development is to spend some time defining the precise problems or challenges you want AI to solve. The more specific you are about this, the better your chances of success in AI software development. The use case for AI software development is best built around achievable goals that have a positive impact on the business and end users of the software being developed.

03. Data availability


Once you have identified the pain points you want AI to solve, we ensure that systems and processes are in place to capture and monitor the data necessary to perform the required analysis that must ensure the availability of the right data with the right characteristics or variables. This will allow us to quickly advance the AI software development process.

04. Preliminary basic data exploration


We perform a basic data exploration that allows us to verify the AI’s understanding of the data, very important because it helps us determine if the data correctly represents the events that occurred. This is based on the business acumen and experience of the company. This step is important because it can be key information for creating AI or ML models. Defining a method to create and validate an AI or ML model The process to define a methodology to validate the AI or ML model involves splitting the data into sets of two. One is the training set while the other is a test set. The training set is used to train the algorithm while the test set is used for evaluation purposes. The sampling processes will depend on the complexity of the algorithm. It is important to validate the findings to ensure that everything is going in the right direction

05. Data-driven AI and model-driven AI


Choosing data in AI software development is choosing between data-driven AI and model-driven AI. The goal of data-driven AI allows us to develop a system that can identify the correct answer based on a large number of question/answer pairs it has seen and the training it has received. The purpose of model-based AI is to capture knowledge and enable decision-making through clear rules and representations

06. Key points in the implementation of AI software


Creation and development of a strategic AI method, this method clearly defines what you want to achieve with AI. The strategy should identify the end users of the AI software. Determine and ensure your AI readiness, review your AI readiness plan, and develop a plan to implement AI. Regularly, this includes reviewing areas such as data collection and storage, data quality, data security, and current business processes that will be affected by AI use cases. POC (proof of concept) this step tests are carried out with the identified use cases, in order to see how the AI software will work in real-world conditions. This is a test to find any shortcomings in accuracy, data access, and user experience (UX).

07. Deploying AI software in the cloud


These services are provided by on-demand cloud computing platforms, such as Microsoft Azure, IBM Cloud, Haweii, and AWS.

08. Testing AI software


AI-powered software testing involves developing test cases, creating test scripts, and maintaining the test case and script. AI-based software testing improves the accuracy of software testing. Additionally, it goes far beyond the limitations of manual testing, increasing overall test coverage and ensuring faster time to market for the software under test.

09. Implementation of AI software


Tested, validated and approved, the AI or ML model for software development can be implemented in production. Limited deployment for the first three months is important to fix any bugs that may have been passed during QA testing. Then the software can be deployed to all areas of the company so that the implementation becomes generalized.

10. Ongoing AI software training


You cannot create an AI software solution and then leave it with no results. AI cannot learn two different things simultaneously. If the AI is trained to do a job, then it will perform that job only. If you want your AI solution to perform another task, then it must be trained for that particular task. Therefore, frequently retraining AI software is a critical part of the AI software development process.

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