Synthetic Data Generation using LLM: Crash Course for Beginners

Check out this new video on "Synthetic Data Generation using LLM: Crash Course for Beginners." In this video, I cover the basics of synthetic data generation, explore different types, and introduce the tools and libraries you can use. I break down complex concepts into easy-to-understand segments, making it perfect for beginners.

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[akj]

Wow I needed this. I swear I will pay for this once I get a job.

[vivanshreyas]

Thank you soo much for making such an in depth video on this bro!!!

[CryptoMaN_Rahul]

I wanted to generate synthetic data of Ecommerce product size charts

[wseqwen]

🎯 Key points for quick navigation:

00:00:03 *📊 Introduction to Synthetic Data Generation*
- Exploration of synthetic data generation as a trending topic,
- Potential applications in solving complex problems in industries like climate change and healthcare,
- Mention of LLMs (Large Language Models) like Microsoft's model families trained using synthetic data.
00:01:13 *🛠️ Tools and Frameworks*
- Overview of tools for synthetic data generation, including open source and closed source frameworks,
- Mention of specific tools like distri label, Prometheus, and grittle,
- Discussion on using LLMs for standalone data creation through advanced prompt engineering.
00:02:23 *🔧 Practical Demonstration*
- Demonstration using OpenAI's GPT-3.5 turbo for generating synthetic reviews,
- Explanation of business logics and thresholds for generating quality synthetic data,
- Use case for generating product reviews and other domain-specific data.
00:04:25 *📂 Synthetic Data Process*
- Overview of the synthetic data generation process including seed data input and the role of LLMs,
- Importance of pre-processing and post-processing for enhancing data quality,
- Description of the validation and testing phase using LLMs.
00:06:01 *🔍 Explanation of PII Handling*
- Explanation of handling personally identifiable information (PII) using synthetic data,
- Example of using synthetic data to maintain confidentiality while enabling data processing,
- Introduction to Faker, a Python library for generating synthetic data patterns.
00:08:19 *💡 Synthetic Data Types: Distillation and Self-Improvement*
- Introduction to synthetic data types in the context of LLMs: distillation and self-improvement,
- Benefits and characteristics of each type,
- Explanation of distillation as teaching one model to create new data.
00:10:53 *📚 Techniques in Distillation*
- Overview of different distillation techniques like self-instruct and evolve-instruct,
- Detailed explanation of self-instruct, evolve instruct, and their processes,
- Insight into creating diverse and task-specific datasets for improved model training.
00:14:34 *🧩 Advanced Techniques: Evolve-Instruct and Lab*
- Explanation of evolve-instruct for creating complex prompts and improving LLM capabilities,
- Importance of creating challenging tasks for model advancement,
- Introduction to Lab, a method for generating diverse data sets for large scale alignment of chatbots.
00:23:19 *🤖 Hierarchical Classification in AI*
- Discusses hierarchical classifications for chatbots,
- Importance of task diversity to reduce bias,
- Combines hierarchical tasking with self-instruct for high-quality datasets.
00:26:11 *🧑‍🎓 Domain-Specific QA System*
- Methods for generating high-quality domain-specific question-answer data,
- Importance of benchmarks and student feedback in generating solutions,
- Potential of AI feedback in reinforcement learning with LLMs.
00:29:38 *🛠️ Synthetic Data Tools and Libraries*
- Introduction to Distil Library for synthetic data creation and evaluation,
- Explanation of using Griddle and other frameworks for data generation,
- Overview of pipeline setup, evaluation models, and dataset management in Argilla.
00:36:11 *🔗 Tools for PII Redaction and Tabular Data*
- Options for PII redaction and the use of tabular data synthesis,
- Reference to tools like Griddle, Faker, and integration with OpenAI,
- Encouragement to explore tools' documentation and existing notebooks for practical application.

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[shahnaz]

Hi I'm a fresher
I got selected in an mnc where I have 2 options that i can choose devops engineer role or ai/genai engineer to start my career
So could you please help me to choose which one has a better future..