How Synthetic Data Transforms AI Training

One of the hardest parts of fine-tuning models? Getting high-quality data without breaching compliance. This Synthetic Data Generator Pipeline ia built to solve exactly that, and it is open-sources for you to use! You can now generate task-specific, high-quality synthetic datasets without using a single piece of real data, and still fine-tune performant models. Here’s what makes it different: → LLM-driven config generation Start with a simple prompt describing your task. The pipeline auto-generates YAMLs with structured I/O schemas, filters for diversity, and LLM-based evaluation criteria. → Streaming synthetic data generation The system emits JSON-formatted examples, prompt, response, metadata at scale. Each example includes row-level quality scores. You get transparency at both data and job level. → SFT + RFT with evaluator feedback We use models like DeepSeek R1 as judges. Low-quality clusters are automatically identified and regenerated. Each iteration teaches the model what “good” looks like. → Closed-loop optimization The pipeline fine-tunes itself, adjusting decoding params, enriching prompt structures, or expanding label schemas based on what’s missing. → Zero reliance on sensitive data No PII. No customer data. This is purpose-built for enterprise, healthcare, finance, and anyone who’s building responsibly. And it works: 📊 On an internal benchmark: - SFT with real, curated data: 79% accuracy - RFT with synthetic-only data: 73% accuracy That’s huge, especially when your hands are tied on data access. If you’re building copilots, vertical agents, or domain-specific models and want to skip the data wrangling phase, this is for you. Built by Fireworks AI 🔗 Try it out: https://lnkd.in/dXXDdyuM

When I have a conversation about AI with a layperson, reactions range from apocalyptic fears to unrestrained enthusiasm. Similarly, with the topic of whether to use synthetic data in corporate settings, perspectives among leaders vary widely. We're all cognizant that AI systems rely fundamentally on data. While most organizations possess vast data repositories, the challenge often lies in the quality rather than the quantity. A foundational data estate is a 21st century competitive advantage, and synthetic data has emerged as an increasingly compelling solution to address data quality in that estate. However, it raises another question. Can I trust synthetic more or less than experiential data? Inconveniently, it depends on context. High-quality data is accurate, complete, and relevant to the purpose for which its being used. Synthetic data can be generated to meet these criteria, but it must be done carefully to avoid introducing biases or inaccuracies, both of which are likely to occur to some measure in experiential data. Bottom line, there is no inherent hierarchical advantage between experiential data (what we might call natural data) and synthetic data—there are simply different characteristics and applications. What proves most trustworthy depends entirely on the specific context and intended purpose. I believe both forms of data deliver optimal value when employed with clarity about desired outcomes. Models trained on high-quality data deliver more reliable judgments on high impact topics like credit worthiness, healthcare treatments, and employment opportunities, thereby strengthening an organization's regulatory, reputational, and financial standing. For instance, in a recent visit a customer was grappling with a relatively modest dataset. They wanted to discern meaningful patterns within their limited data, concerned that an underrepresented data attribute or pattern might be critical to their analysis. A reasonable way of revealing potential patterns is to augment their dataset synthetically. The data set would maintain statistical integrity (the synthetic mimics the statistical properties and relationships of the original data) allowing any obscure patterns to emerge with clarity. We’re finding this method particularly useful for preserving privacy, identifying rare diseases or detecting sophisticated fraud. As we continue to proliferate AI across sectors, senior leaders must know it's not all "upside." Proper oversight mechanisms to verify that synthetic data accurately represents real-world conditions without introducing new distortions is a must. However, when approached with "responsible innovation" in mind, synthetic data offers a powerful tool for enabling organizations to augment limited datasets, test for bias, and enhance privacy protections, making synthetic data a competitive differentiator. #TrustworthyAI #ResponsibleInnovation #SyntheticData

I've been reading the commentary swirling around Ilya Sutskever's recent #NeurIPS 2024 talk, and I've noticed some mischaracterizations of his key points. While some VCs are quick to post reactions suggesting that "data doesn't scale" or that synthetic data is ineffective, these takes miss the substance of the presentation, and frankly shows lack of familiarity with how these techniques actually work. If you watch Ilya's complete talk, you'll find that he actually positions synthetic data as one of the next major frontiers in AI advancement. His presentation outlines three key areas for future development: (1) Agents, (2) Synthetic Data, and (3) Inference-Time Compute. Importantly, the slides following these points—which many quick takes have overlooked—specifically highlight synthetic data as a crucial path forward. This distinction matters significantly. While the internet's natural data has limitations, next-generation AI systems will require more sophisticated and varied training signals. Rather than being a stopgap measure, synthetic data represents a strategic pathway toward developing safer, more diverse, and specialized AI models. It's becoming increasingly clear that relying solely on existing internet data won't be sufficient for future advancement. This view is gaining traction among industry leaders, with figures like Andrej Karpathy and teams at Microsoft Research (including the Phi-4 team) recognizing synthetic data's essential role in advancing beyond current capabilities. At Gretel, we're working to transform this potential into reality. Our focus lies at the convergence of advanced agents, synthetic data generation, and efficient inference—areas that align directly with the future direction Sutskever outlined. The future of AI will be shaped by the innovators who can push beyond the data we have and create the data we need. Synthetic data is that future. And we’re working hard to build it.

In the realm of building machine learning models, there are typically two primary data sources: organic data, stemming directly from customer activities, and synthetic data, generated artificially through a deliberate process. Each holds its unique value and serves a distinct purpose. This blog post, written by the Data Scientists at Expedia Group, shares how their team leveraged synthetic search data to enable flight price forecasting.   -- [Business need] The primary objective is to develop a price forecasting model that can offer future flight pricing predictions to customers. For instance, it aims to inform customers whether flight prices are likely to rise or fall in the next 7 days, aiding them in making informed purchasing decisions.    -- [Challenges] However, organic customer search data falls short due to its sparsity, even for the most popular routes. For instance, it's rare to see daily searches for two-way flights from SFO to LAX for every conceivable combination of departure and arrival dates in the upcoming three months. The limitations of this organic data are evident, making it challenging to construct a robust forecasting model.   -- [Solution] This is where synthetic search data comes into play. By systematically simulating search activities on the same route and under identical configurations, such as travel dates, on a regular basis, it provides a more comprehensive and reliable source of information. Leveraging synthetic data is a potent tool for systematic exploration, but it requires a well-balanced approach to ensure that the benefits outweigh the associated costs. Striking this balance is essential for unlocking the full potential of synthetic data in data science models. – – –  To better illustrate concepts in this and future tech blogs, I created one podcast "Snacks Weekly on Data Science" (https://lnkd.in/gKgaMvbh) to make them more accessible. It's now available on Spotify and Apple podcasts. Please check it out, and I appreciate your support! #machinelearning #datascience #search #synthetic #data #forecasting https://lnkd.in/gRjR5tTQ

Synthetic Data for LLM applications Synthetic healthcare datasets have been around for some time. Whenever we discuss synthetic data, we often find ourselves trapped in one question: How realistic is the synthetic data? We create synthetic data because we cannot share the original data for various obvious reasons, e.g., privacy, security, etc. The challenging part is that we want the synthetic data to have all the nice characteristics of the original data. With all the challenges aside, many researchers adopted and tried to prove the value of synthetic data. This morning, I came across this well-summarized survey article, "Synthetic data in healthcare: A narrative review," by Aldren Gonzales, Guruprabha Guruswamy, and Scott Smith. According to the paper, people have used synthetic data for seven categories: 1. Simulation and Prediction Research 2. Hypothesis, Methods, and Algorithm Testing 3. Epidemiological Study/Public Health Research 4. Health IT Development and Testing 5. Education and Training 6. Public Release of Datasets 7. Linking data I have been thinking about synthetic data quite a bit these days. It's because of the Large Language Models (LLMs) like ChatGPT by OpenAI and Claude by Anthropic. I have been playing around with those LLMs. I realize these days that, even if you are an expert, it's almost impossible to predict the outputs. As we cannot logically bind the output behaviors, testing an LLM model with all possible(or available) cases is the only way. Here, I think, is the new application of synthetic data: LLM application testing. If we were to build many LLM applications, we would need a lot of synthetic healthcare data. In this case, the synthetic data do not need to be hyper-realistic. They just need to represent the "quirkiness" of the real-life use cases, so weird synthetic data should be fine. Healthcare-focused LLM applications would need to test with all sorts of available synthetic data to see if such applications produce weird outputs. We may need a mechanism to do so. I think this new use case of synthetic data will be critical in healthcare. Let's see. [1] https://lnkd.in/e8DiEH9j