5 AI Training Steps & Best Practices

One of the biggest challenges in developing AI systems is training the models.

To help developers improve the process of building AI, this article explores 5 steps and best practices to train your AI models effectively. You can also explore how to train large language models.

Dataset preparation

Data collection and preparation is a prerequisite for training AI and machine learning algorithms. Without quality data, machine & deep learning models cannot perform the required tasks and mimic human behavior.

Hence, this stage of the training process is of utmost importance.

1.1. Collect the right data

Custom crowdsourcing

Private collection or in-house data collection

Precleaned and prepackaged data sets

Automated data collection

1.2. Data preprocessing

Data gathered to train machine learning models can be messy and needs preprocessing and data modeling to be prepared for training.

Data processing involves enhancing and cleaning the data to improve the overall quality and relevancy of the whole dataset.

Data modeling can help prepare datasets for training machine learning models by identifying the relevant variables, relationships, and constraints that need to be represented in the data. This can help ensure that the dataset is comprehensive, accurate, and appropriate for the specific AI/ML problem being addressed.

1.3. Accurate data annotation

After the data has been gathered, the next step is to annotate it. This involves labeling the data to make it machine-readable. Ensuring the annotation quality is paramount to ensuring the overall quality of the training data.

Model selection

The complexity of the problem

The size and structure of the data

The computational resources available

The desired level of accuracy

Initial training

After data collection and annotation, the training process can start by inputting the prepared data into the model to identify any errors that might surface.

Expanding the training dataset

Leveraging data augmentation

Simplifying the model can also help avoid overfitting. Sometimes the complexity of the model makes it overfitting even when the dataset is large.

Training validation

Once the initial training phase is complete, the model can move to the next stage: validation. In the validation phase, you will corroborate your assumptions about the performance of the machine learning model with a new dataset called the validation dataset.