It's all about the Data

According to a report by OpenAI, the cost of training a single large AI model can range from 

$3 million to $12 million. The cost of training a model on a larger dataset can be even higher, reaching up to $30 million.  With this predicted to pass $500 million by 2030. Data Preparation is a major contributor to these costs. 

Here are some Data Preparation Challenges that contribute to the training cost you will face. 

Data quality: Data quality is a significant challenge in data preparation. The quality of the data used to train machine learning models directly affects the accuracy of the model. Data quality issues include missing values, inconsistent data, and noisy data.

Data quantity: Machine learning algorithms require large amounts of data to learn effectively. However, collecting large amounts of data can be challenging, especially for niche applications.

Data labeling: Labeled data is required for supervised learning algorithms. However, labeling data can be time-consuming and expensive.

Data privacy: Data privacy is a significant concern when dealing with sensitive data such as medical records or financial information. Ensuring that data is anonymized and secure is essential.

Data bias: Data bias occurs when the training data does not accurately represent the real-world population. This can lead to biased predictions and inaccurate results.

Data integration: Data integration involves combining data from multiple sources into a single dataset. This can be challenging due to differences in data formats and structures.

Data versioning: Keeping track of different versions of datasets can be challenging, especially when multiple teams are working on the same project.