How much training data is required for machine learning in 2025?

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AbeHolt

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What scale of training data and preprocessing steps are recommended in 2025 for building a custom AI model on an Nvidia GPU, considering the potential of the upcoming 5000-series? Could this include image-based training?
 
A bit of a boring answer, but... the amount of training data required for machine learning varies significantly depending on several factors, including the complexity of the problem, the model, and the specific task.

So if you are going to use an NVIDIA GeForce RTX 5070, for example, you will need to concentrate on a specific task, a specific set of documents or images, in short.

By 2025, large-scale models (e.g., foundation models) often rely on billions of examples for pretraining, while smaller or domain-specific models may do well with thousands—or even hundreds—of carefully curated samples. The trend is to combine large, generalized models with task-specific fine-tuning data so that developers spend less time collecting massive datasets. Ultimately, data quality and relevance remain more critical than sheer volume.

I'll let AI summarize what more I know and found:
  • Problem Complexity: For simpler problems, like basic regression or classification tasks, the consensus seems to be that you might need at least 10 times the number of samples as the number of features in your dataset. However, this is a rule of thumb and can vary. For more complex tasks, such as those involving deep learning, the data requirements can scale dramatically, often into the millions of examples. Another example, for image classification tasks using deep learning, it's commonly suggested that each class needs at least 1000 images.
  • Model Type: Deep learning models, particularly those for tasks like image recognition or natural language processing, require vast amounts of data to achieve high performance. Discussions highlight that for image classification, datasets might need to have thousands to tens of thousands of images per class to train robust models. Conversely, simpler models like linear or logistic regression might perform well with much less data.
  • Data Quality and Diversity: Another point emphasized is the quality over quantity debate. Even with large datasets, if the data doesn't represent the diversity of scenarios the model might encounter, the model's performance can be poor. Ensuring your data is diverse and of high quality can sometimes reduce the sheer volume of data needed.
  • Synthetic Data: Some mention the use of synthetic data generation to supplement real-world data, which can be particularly useful when real data is scarce or when trying to achieve data diversity. This approach can help in reducing the actual amount of real data needed for training by augmenting datasets.
  • Pre-trained Models: Utilizing pre-trained models is another strategy discussed, where you might need less data to fine-tune a model for a specific task, as these models have already learned from large, general datasets. This approach can significantly cut down training data requirements.
 
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Considering the Nvidia 5000-series GPUs, one innovative approach could be leveraging federated learning for image-based training. This method allows you to train models across multiple decentralized devices, which can significantly enhance data diversity and volume without the need for a centralized dataset. By 2025, federated learning could be more accessible due to improved hardware capabilities, making it a practical choice for custom AI models. Exploring transfer learning with these GPUs could be beneficial.

You could start with a model pre-trained on a large dataset and then fine-tune it with a smaller, task-specific dataset, reducing the overall data requirement while maintaining high performance. Have you considered these strategies for your project?
 
One aspect that hasn't been fully explored yet is the role of active learning in reducing training data needs by 2025. Active learning involves selecting the most informative samples for training, which can significantly decrease the amount of data required. With the Nvidia 5000-series GPUs, you could implement active learning strategies more efficiently due to their enhanced processing power. This approach could be particularly useful for image-based training, where you might start with a smaller dataset and iteratively add the most beneficial images for your model's learning process.

Additionally, consider the potential of quantum machine learning as it evolves. While still in early stages, by 2025, quantum algorithms might offer new ways to process and analyze data more efficiently, possibly requiring less traditional training data. These methods could complement federated and transfer learning, providing a multifaceted approach to building your custom AI model.

Have you thought about integrating these advanced techniques into your project? It's exciting to see how these technologies could shape the future of AI development!
 
Ant said:
One aspect that hasn't been fully explored yet is the role of active learning in reducing training data needs by 2025. Active learning involves selecting the most informative samples for training, which can significantly decrease the amount of data required. With the Nvidia 5000-series GPUs, you could implement active learning strategies more efficiently due to their enhanced processing power. This approach could be particularly useful for image-based training, where you might start with a smaller dataset and iteratively add the most beneficial images for your model's learning process.

Additionally, consider the potential of quantum machine learning as it evolves. While still in early stages, by 2025, quantum algorithms might offer new ways to process and analyze data more efficiently, possibly requiring less traditional training data. These methods could complement federated and transfer learning, providing a multifaceted approach to building your custom AI model.

Have you thought about integrating these advanced techniques into your project? It's exciting to see how these technologies could shape the future of AI development!
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Cool. Tell me more about active learning and reducing training data. What about text, how many samples do you need?
 
Active learning is a smart way to cut down on the amount of training data you need. By 2025, with the power of Nvidia's 5000-series GPUs, you can implement this technique more effectively, especially for image-based training. It's about picking the most useful samples to train your model, which means you can start with a smaller dataset and grow it strategically.

For text-based tasks, the number of samples you'll need can vary, but here's a general guideline: For simpler tasks like sentiment analysis, you might get by with a few thousand samples. More complex tasks like machine translation could require tens of thousands or even more, depending on the languages and the model's complexity. Remember, it's not just about the number but the quality and diversity of your data.

Have you considered how active learning could streamline your project? It's an exciting approach that could really change the game in terms of efficiency and resource use!
 
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