In 2020, enterprises realized the need for AI in their business. Due to COVID-19, most companies have scaled up their AI adoption and increased their AI investment. Yet, to completely harness the power of AI in your business, you need to build and deploy multiple models. This article discusses the steps in AI model development.
Step 1: Identification of the Business Problem
Define the business problem you are looking to solve.
- Ask the following questions: what results are you expecting from the process, what processes are in use to solve this problem, how do you see AI improving the current process, What are the KPIs that will help you track progress, what resources will be required, How do you break down the problem into iterative sprints?
The Next Step
The above steps gave a detailed approach to building an AI model.
- However, these steps do not factor in two crucial aspects of a business – time and people
- Hiring AI experts who have well-defined processes to build and deploy models at a pace is the solution.
Step 2: Identifying and Collecting Data
Since machine learning models are only as accurate as the data fed to them, it becomes crucial to identify the right data to ensure model accuracy and relevance.
- Ask questions like: What data is required to solve the business problem, what quantity of the data, do you have enough data to build the model, how is the data collected and where is it stored, can you use pre-trained data?
Model Deployment
Deploy the model into the intended infrastructure like the cloud, at the edge, or on-premises environment.
- Before deployment, make sure you continuously measure and monitor the model performance
- Define a baseline to measure future iterations of the model
- Keep iterating the model to improve model performance with the changing data
Step 3: Preparing the Data
Data scientists and ML engineers tend to spend around 80% of the AI model development time in this stage
- The data collected in the previous step need not be in the same form, the same quality, or the same quantity as required
- Some of the things you need to consider at this stage include: Transforming the data into the required format
- Clean the data set for erroneous and irrelevant data
- Enhance and augment the data data set if the quantity is low
A Note On Model Governance
Model governance is not a defined step in an AI model lifecycle
- It is necessary to ensure the model adapts to the changing environment without many changes in its results.
- Consider monitoring the model for the following parameters: Deviations from the pre-defined accuracy of the model, Irregular decisions or predictions, Drifts in the data affecting the model performance
Step 4: Model Building and Training
Define the features of the model
- Use the same features for training and testing
- Consider working with Subject Matter Experts
- Determine the most suitable algorithm
- Model interpretability
- Test the model with the training data
Model Testing
The main objective of this step is to minimize the change in model behavior upon its deployment in the real world.
- This stage involves carrying out multiple experiments on the model to bring out its best abilities and minimize the changes it undergoes post-deployment.
