Here is Part 10 of the Generative AI in Data Science blog series. This post goes model-specific. Now that you know how to generate, benchmark, and tune synthetic data, it’s time to align that data with the specific needs of different model architectures. Synthetic data is not one-size-fits-all. A prompt that works great for a […]
This is Part 9 of Generative AI in Data Science blog series – the post that turns feedback-driven synthetic data generation into a measurable, optimizable pipeline. Now that you have got adaptive data loops in place (Part 8), this is where we define how to evaluate their impact rigorously. By now, you have built
Part 9: Measuring What Matters -Evaluating Feedback-Tuned Synthetic Data Read More »
Here is the Part 8 of Generative AI in Data Science blog series. This one explores how to close the loop between model performance and synthetic data generation – turning your synthetic data pipeline into an adaptive, self-improving system. At this point in the series, we have covered the full synthetic data lifecycle: Building
Part 8: Closing the Loop – Using Feedback to Improve Synthetic Data Generation Read More »
This is Part 7 of Generative AI in Data Science blog series. This one goes straight into the real-world tension between synthetic and real data: when to combine them, how much to trust each, and what it actually does to your model’s performance. In the last six parts of this series, we showed how
Part 7: Combining Real + Synthetic Data -What Works, What Breaks, and Why Read More »
This is Part 6 of the Generative AI in Data Science series picking up where Part 5 (multi-modal synthetic data generation) left off. This post is about the real challenge that comes once the generative magic is working: productionizing synthetic data pipelines in a way that is reliable, reproducible, and MLOps-compatible. Part 6: Productionizing Synthetic
Part 6: Productionizing Synthetic Data Pipelines with MLOps Best Practices Read More »
This blog post is Part 5 of Generative AI in Data Science series. This one tackles multi-modal synthetic data generation combining text, images, and structured data using GPT-4, DALL·E, and prompt chaining. It is built for your audience of data science professionals who care about building real-world, reproducible pipelines. Part 5: Multi-Modal Synthetic Data
Part 5: Multi-Modal Synthetic Data Generation with GPT-4, DALL E & Prompt Chaining Read More »
This is Part 4 of Generative AI in Data Science. Do read previous parts to get a full understanding. In this blog post we will learn about benchmarking synthetic data. This post gives you a practical framework for quantifying the quality and validating the effectiveness of synthetic data for real-world model training. Part 4:
Part 4: Benchmarking Synthetic Data – How Close Is “Close Enough”? Read More »
This is Part 3 of the Generative AI in Data Science blog series. This post takes things up a next level automating synthetic data generation across domains, injecting real-world messiness, and generating labeled text data for NLP tasks. Part 3: Automating GPT-Based Synthetic Data Generation for Real-World Modeling In Part 1and Part 2, we
Part 3: Automating GPT-Based Synthetic Data Generation for Real World Modeling Read More »
Part 2 of blog series is a deep, code-heavy guide that walks you readers through building and validating a synthetic data generator using GPT-4 + Python. in Part 1 we have already learned the importance of Synthetic data and why it is needed. If you have not read Part 1 yet, Click Here Goal:
Part-2 Building a Synthetic Tabular Data Generator With GPT-4 and Python Read More »
Generative AI in Data Science. This Blogpost is an easy guide with python code examples. In this section, you will learn how to create synthetic data by just typing a prompt, and Generative AI will do the rest of the task for you. But let me start with “What is prompt?” prompt is an string
Generative AI in Data Science: Easy Guide With Python Examples Read More »
