In order for an AI model to properly perform its function, it needs to be trained with data. Different needs and expectations determine the time required to train the model. In this article, we will discuss how long the AI model training process can take and what factors influence its duration.
What is an AI model?
AI models are essentially programs that are trained on data sets and are capable of making decisions independently, without human intervention. Some of the first attempts at AI were observed in the early 1950s when programs for playing checkers or chess could make moves in response to an opponent’s decisions.
In the AI model training process, we will frequently encounter the concepts of machine learning and deep learning. Machine learning models use learning algorithms that analyze past data to make predictions. On the other hand, deep learning models use deep neural networks, which allow learning from large amounts of data. The more data available, the more accurate the results.
Generally, the decision-making process is based on three stages, which we will discuss in more detail:
- Modeling. In this phase, an AI model is created that includes complex algorithms or layers of algorithms to analyze data, which is the basis for decision-making.
- AI Model Creation. During this stage, the created model is trained. Typically, large amounts of data are tested through the AI model, and the results are checked to see if AI model training process results meet expectations.
- Supervised and Unsupervised Learning. In this stage, we distinguish between supervised and unsupervised learning. In supervised learning, the data is labeled, meaning the correct outputs are provided in advance, and the data has pre-existing relationships between input and output. In unsupervised learning, the model is not given labeled data, so it must independently detect relationships and patterns within the data.
- Application. In this phase, the AI model is enabled to operate in a real-world environment.
Understanding AI Model Training
The AI model training process can be broken down into six steps:
- Data Preparation. The higher the quality of the data, the better the model will perform. Data can be collected through various methods: web scraping, crowdsourcing, open-source data collection, internal data collection, synthetic data generation, or sensor data collection. The method chosen depends on the domain and the intended use of the model.
- Choosing the AI Model. The selection of the model depends on the resources available, the size of the data set, the complexity of the problem, etc. For example, logistic regression may be used in medical diagnostics, while decision trees can be used for customer segmentation.
- Choosing the Learning Technique. In this stage, we decide between supervised or unsupervised learning, or a hybrid approach that uses both.
- AI Model Training.
- AI Model Adjustment and Evaluation.
- AI Model Testing. At this stage, it is useful to test the created model on an independent data set to ensure that it can be used effectively in real-world applications.
The time required to create an AI model essentially depends on its complexity. This includes the purpose for which it is created, the size and complexity of the data set (language diversity, sentence structure), and the hardware (processor power) used in the training process.
For example, creating models with small data sets using decision tree methods can take a short time. However, training models like GPT, which rely on large data sets, requires more time accordingly.
Sentisight offers an alternative to AI model training by providing pre-trained models, which can significantly simplify the model training process.
Available models include:
- General classification
- Places classification
- NSFW classification
- Object detection
- Instance segmentation
- Text recognition
- Pose estimation
- Background removal
Final Word
To sum up, the time it takes for AI model training varies significantly based on the model’s complexity. With tools like Sentisight offering pre-trained models, organizations can simplify this journey and focus on applying AI solutions effectively.
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Sources: eWeek, HPE, IBM, Quora, Sentisight
