How to build an AI

1. Collect data

By now, you're well aware that machines learn from the raw input data that we provide for them. In this stage, it's extremely important to provide reliable data so that your model can distinguish correct patterns.

The quality of the data you provide determines your model's overall accuracy. The model will produce wrong outcomes or predictions if you input incorrect or outdated data.

(Simplilearn, 2022)

2. Clean and prepare data

Once you have accumulated your chosen data, the next step is to clean and prepare it. This process is a very critical step in developing machine learning models, to the extent that people typically spend 80% of their time organising and cleaning data. Having clean and organised data significantly impacts the model's accuracy in the finished product.

Data can be prepared by:

- Cleaning Data: This involves removing unwanted data, missing values, rows, columns, duplications of content, and so on.

- Randomising accumulated data: This makes sure that all data is evenly distributed and that the ordering doesn't affect the learning process of the model

- Splitting data into sets: The 2 sets include training and testing sets. The training set is where your model learns from, while a testing set is used to check the model's accuracy after it has completed its training.

(Simplilearn, 2022)

3. Train model

Once the model is prepared with the chosen data, it's time to train the machine learning model to find patterns and make predictions. This data is then connected to an algorithm that helps the model learn and develop patterns and predictions.

Over time the model will become more proficient at predicting and delivering accurate results.

4. Test model

In this next step, it's time to test the ability of the trained model. This is where the prepared test data from the "stage clean and prepare data" comes into play. The overall performance of the model will indicate the model's accuracy and maturity levels.

Note: If the same training data is used to test the model's ability, the results will be inaccurate. The model has already used this data to identify and learn from its patterns. It will then result in disproportionately high accuracy. (Simplilearn, 2022)

Poor Results - If the results are not satisfactory, it's important to go back and revisit the previous stages and improve the data input for the model.

5. Improve

Overall, the most important thing to remember when training machine learning models is to continually improve and test the system until it performs the desired task with satisfactory accuracy. Some tips that can be done to improve the model include:

Reviewing outcomes:

It's always important to go back and review the outcomes within a team and with business stakeholders. This way, it's easy to reflect on if there are missing data elements or if additional information needs to be added to make the model more accurate.

Reconsider the algorithm choice:

Trialing different algorithms to test a model's performance is a great way to determine if more suitable options may achieve better outcomes.

Adjust small parameters:

Going back to the previous stages and making small adjustments or changes can sometimes significantly impact the overall performance of a machine learning model. (Centric, 2019)