Explore the key approaches to create an AI model to improve business competence.  

Artificial Intelligence (AI) and Machine Learning (ML) have already demonstrated extensive possibilities for organizations to improve operations and maximize their revenues. While businesses these days face enormous problems, there are a variety of machine learning models used to solve these problems. Some algorithms are better at addressing certain sorts of problems than the others. In this way, there is a need of a clear understanding of what every type of ML model is best for.

In a 2019 McKinsey survey, most executives adopted AI solutions reported that they gained an uptick in revenue, while 44 percent said that AI has reduced costs. However, building an AI model is often expensive and poses a risk where the model will fail to have a meaningful impact on an organization as well as fail in pilot purgatory after completion. So, how can a company create AI models to maximize the probability of success of their business?

Getting Started with Selection Process

As there are a multiple number of algorithms out there, choosing the right one can be very complex. In this context, organizations need to have a deep understanding of their business objectives. Deep diving in the research and explore and find out what is possible for them can be an effective approach to an AI model development. It is also significant to consider some factors including model performance, accuracy, interpretability and compute power, among others when selecting the right model.

Having the right kind of data is also vital for certain models. This is vital because inadequate data quality can create challenges to deploying AI models. Thus, leveraging the right data set can prove to be a critical factor in creating an AI model.

Training

According to a Forbes article, once businesses will have an algorithm, or a set of them, they must perform tests against the dataset. To do so, the best practice is to bifurcate the dataset into at least two parts. Nearly 70 percent to 80 percent is for testing and tuning of the model, while the remaining will be used for validation. Afterward, there is a need to look at the accuracy rates. There are numerous AI platforms that can help in streamlining the process, also open source offerings, including TensorFlow, KNIME, PyTorch, Anaconda and Keras, in addition to proprietary applications, such as Alteryx, Databricks, DataRobot, MathWorks and SAS.

Optimization

In order to make the model better, handling and preparing datasets for model training, tuning parameters, or any number of other approaches and techniques can help optimize artificial intelligence models. For ML model optimization across the lifecycle, data handling, architecture selection, model debugging, ML model visualization, ML model evaluation and selection, hyperparameter tuning, algorithms optimization, and more are essential factors.

Feature Engineering

In this process, businesses need to find the variables that are the best predictors for a model. For this, the expertise of a data scientist becomes essential. But there is also often a need to have domain experts to help out in this process. However, finding the right features can be almost impossible, and this could be the case with computer vision, such as when used with autonomous vehicles. Yet using sophisticated deep learning can be a solution.

In the last, businesses must continue evaluating their models based on metrics such as accuracy, precision, and F1 score. This provides them information on how well their models are performing, and enables them to find where they will need to make improvements.