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Data Training Strategies for AI Models: Real vs. Synthetic Data, and Repositories

Data is the Michael’s Secret Stuff of artificial intelligence. For any AI project, training and fine-tuning your large language model (LLM) requires essential data to slam-dunk its tasks.

Of course, not all data is the same. Different AI training datasets have trade-offs between their cost, accuracy, and topic-specificity. Deciding which types of training data depends on budget, timeframe, and objectives.

In this blog, we'll discuss the pros and cons of machine learning datasets that you're considering: real data, synthetic data, and repository datasets.
Hetal Bhatt
Reading time: 10 min

In this blog post

Real Data: The Gold Standard of Training Data for AI

Real data is actual human ml training datasets that can be found in various online sources such as online comments, social media posts, chat messages, and other forms of unstructured text data (For this article, we’re talking about unstructured text data, not image or tabular data). It's considered the best quality of data sets for AI because it's taken from real-world human interactions, providing the most accurate representation of human behavior. Real data is like gold for generating human-like results for LLM chatbots and classifiers because the LLM is trained exclusively on content produced by actual people.

So, that’s it – real training data for AI is the best; case closed, right?
Well, it does come with some drawbacks.

For starters, the availability of real machine learning training data is rapidly decreasing.
"Researchers from AI forecasting firm Epoch AI estimate that AI companies could run out of high-quality training data as soon as 2026, while lower-quality data could be tapped out sometime between 2030 and 2060. 😆
AI models relying solely on real data sets for AI may face pitfalls in the future.
Collecting and preparing real data can also be expensive and time-consuming.

The data preparation process includes:
Thoroughly cleaning ml training data sets of personally identifiable information (PII) to comply with privacy regulations like HIPPA and General Data Protection Regulation (GDPR) or risk massive fines!
Cleaning ml data sets to remove inaccurate, duplicate, and distorted data to ensure the utmost quality.
This also means painstakingly removing stray HTML, CSS, and JSON tags embedded in datasets.
Standardizing ml training data from various sources with consistent data labels and categorization to work with an LLM properly.
If you need labeled ml training data sets, trained human annotators using detailed definitions of each label need to label each row of text.
If that sounds like a lot of work, it’s because it is! Data scientists say they spend around 80% of their time on data prep and management – and 76% say it’s the worst part of their job.
Having enough time and money to invest in high-quality AI training data is ideal. However, its feasibility as a long-term scalable solution is uncertain, so you should have a Plan B in mind.

Synthetic Data for Machine Learning: Artificially Generated AI Training Dataset

Synthetic data for machine learning refers to LLM training data that are artificially generated instead of being collected from real-world sources. These AI training datasets are created with specific parameters to best fit the use cases of an AI model.

Artificial intelligence (AI) heavily relies on synthetic training data, especially those generated by general-knowledge LLMs like ChatGPT or Bard. However, it's important to note that since AI generative models create synthetic datasets, they can be susceptible to inaccuracies that may be embedded in their training data. This may lead to a poorly trained LLM. It's worth mentioning that these synthetic datasets may not be able to capture all the complexities, nuances, and edge cases present in real data. Consequently, AI models based solely on synthetic data may not perform optimally in real-world scenarios.

The time you save by generating synthetic data may be a wash if you need to meticulously check it for accuracy. Or you might find your dataset helps train your model for the most common queries but cannot understand industry-specific, product-specific, or use-case-specific questions that don’t fit the mold of a “normal question.”
Of course, there are ways to minimize these downsides. Synthetic data providers have varying methods of producing usable ml datasets for your LLMs – but not all data is created equal!

How to Choose the Right Synthetic Data Providers

Choosing the right synthetic data provider is crucial for businesses in data-driven landscapes. Synthetic data providers Gretel, Mostly AI, and Nurdle AI offer various approaches. Below, we compare these synthetic data providers and focus on their accuracy in synthetic data generation of unstructured text.
Comparing synthetic data companies Gretel, Mostly AI, and Nurdle AI
Gretel adopted a unique approach to generating synthetic data. Unlike other methods reliant on real data, Gretel specializes in generating purely synthetic datasets. In contrast, Mostly AI takes a different route in creating synthetic data. It harnesses the power of generating synthetic data from real data. However, these products are considered “first generation synthetic data”; they were designed to produce tabular data, commonly used in ML predictive analytics rather than generative AI or text classifiers. This means they were built to scramble or obscure private information like social security numbers, health diagnoses, addresses, etc., in spreadsheets of data – but were not built to produce coherent unstructured text like you’d find in a chat, social media post, call transcript, or email. In contrast, Nurdle AI is the first “second-generation synthetic data” provider; an LLM built from the world’s largest private data vault of human-to-human social media, gaming, dating, messaging, and chat content spanning 40 languages and designed to produce realistic, human-like unstructured communications datasets - prelabeled. In other words, it’s built to train generative AI and detection classifiers.
Nurdle AI | Generating Synthetic Data from Real Data
At Nurdle, we utilize a kernel of real-world data to generate “lookalike” synthetic datasets for AI. Nurdles synthetic datasets perform at 92% accuracy of real human data at just 5% of the cost. You get highly relevant training data for your AI projects without incurring high costs.

Nurdle is designed to replace the use of actual human-generated unstructured text data. The cost of labeling 100,000 rows of real data from one of the largest data labeling providers is about $10,000 (and you have to deal with all of the privacy and regulatory risks of storing, transferring, and using real data). Nurdle produces synthetic datasets for AI that are nearly identical, pre-labeled, and with no regulatory or privacy vulnerabilities for about $500.

To demonstrate this, Nurdle conducted a double-blind case study to compare our synthetic training data against our competitors, Gretel and Mostly AI - as well as human-generated and human-labeled (using Scale) data. We were looking to see which provider produced the most accurate synthetic dataset - and how much it costs to produce usable synthetic training data sets from each synthetic data provider.

We began by analyzing a sample dataset comprising 7,500 rows of genuine data, which included 5,500 rows labeled for insult. Each provider generated the same quantity of synthetic data, and we tested each dataset to evaluate its accuracy. The results were clear: Nurdle was the top performer, producing highly accurate synthetic data at a fraction of the cost of human-labeled data.
If synthetic data is generated with a reliable foundation of real data, it can work great in training your LLM and filling in gaps not covered by your existing data. If you aren’t seeding your synthetic data in real data, you might end up with egg on your face after an embarrassing mistake by your AI.

Repository Dataset: Ready-Made Machine Learning Datasets For Many Use Cases

Chances are, the data you’re looking for already exists - or at least a small set you can use to start your project. Specifically, repository sites like AWS, Google, Hugging Face, Kaggle, and UCI Repository Dataset, are a goldmine for data and models you can repurpose into your projects. Hugging Face, in particular, has 82,000+ datasets and 100,000s of pre-trained models that are ripe for picking. On the other hand, Kaggle, the data science competition site, is where teams compete to improve models.Instead of starting from scratch, you can source a few models from Hugging Face and test them out for your use cases. Once you’ve landed on one that works well (or even just okay), you can fine-tune it using datasets from the Hugging Face vault. Hugging Face itself has an ‘AutoTrain’ feature that is great at simplifying the process of training and deploying AI models. It’s a great solution for intermediate developers who want to launch their projects quickly and efficiently.

However, repository data has its limits. Datasets from online repositories generally are made for testing rather than widespread commercial use. Chances are, they won’t include the specific use cases, industry knowledge, or edge cases needed for your specific AI project to be accurate enough to release into production. So, you’ll still need to find datasets to fine-tune your model from elsewhere once you’ve built the bones with Hugging Face assets.

Nurdle Data: The Best of All Worlds

How can you use Nurdle data to train and fine-tune your LLM?

Nurdle data combines real data's reliability with synthetic data's cost efficiency, speed, and low-hassle. More specifically, Nurdle uses a sample of real data (We can take some of yours or tap into our own real-world data vault.) to create human-like datasets tailored for your specific use cases. The data performs about 92% of real data but costs 20x less than human-prepared data.

Along with saving money, Nurdle data also saves you time!
Instead of waiting weeks or months to finish your data preparation, Nurdle data is ready for you in days or even hours. And since it’s already been vetted, cleaned, and labeled, Nurdle data can immediately be put to work for your LLM.
F1 Score
Human Data
92% Accuracy of Human Data - At 5% of the Cost
Nurdle Data
Mostly AI
Compared to Human
Data Scientist Prep Time
Cost / 100k Rows
160 hr
40 hr
80 hr
50% (random noise)
80 hr
Nurdle’s data services don’t stop there. You can also enlist Nurdle to analyze and fine-tune your LLM to ensure you’re running in tip-top shape – seriously, we got range!
Data Gap Analysis Report

This is how we identify what data your model is missing and what data we need to provide for you to hit your performance goals.
“Lookalike” Synthetic Datasets

Exactly what we described above – we’ll provide you with high-quality data to train your LLM for any use case.
Data Preparation

All the meticulous, boring stuff that your data scientists hate doing. This includes data sourcing, cleaning, and labeling.
Data Analytics + Benchmarks

We’ll measure ROI on the Nurdle datasets we provide for you and compare them with your own or even another vendor’s data.
The only downside to Nurdle is that you have to talk to an actual human being for us to understand better your goals and what data you need for your AI project to thrive.

If that sounds acceptable, get in touch with us, and we’ll be happy to talk shop!
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