Master Generative AI in 2024: Fine-tuning, Building, and Deploying LLM & Diffusion Models
Table of Contents
- Introduction
- Becoming a Developer in Generative AI
- Level-1 Developer
- Prompt Engineering
- Understanding Generative Model APIs and Frameworks
- Building Your Own GenAI Tools
- Level-2 Developer
- Strengthen the Basics
- Deep Dive into Large Language Models
- Hands-on Fine-Tuning of Foundation Models
- Develop Custom GenAI Tools
- Advancing to the Researcher Level
- Researcher in NLP
- Researcher in Computer Vision
- Conclusion
Becoming a Developer in Generative AI
🔮 Level-1 Developer
As a level-1 Developer in Generative AI, you will have a deep understanding of Prompt Engineering and hands-on experience with major Prompt Engineering techniques. This level focuses on enabling you to write effective prompts to Elicit desired responses. You will also gain a sound understanding of Generative Model APIs and their practical use. Knowledge of a programming language, preferably Python, is crucial for consuming APIs. Once you have a strong foundation in Prompt Engineering and Model APIs, you will be all set to develop your own GenAI Tools.
📚 Prompt Engineering
Prompt Engineering goes beyond considering a prompt as a simple question or command. It involves understanding that a prompt is a way to guide the AI model's response and can include context, tone, format, and other instructions. Mastering Prompt Engineering requires learning specific techniques and continuously practicing them. By analyzing the results and refining prompts based on insights, you can elicit specific information from Large Language Models effectively. Joining online communities and forums dedicated to prompt engineering can also help you learn from others' experiences and best practices.
🔌 Understanding Generative Model APIs and Frameworks
To become a Developer in Generative AI, you need to familiarize yourself with Generative Model APIs and Frameworks. Think of APIs as intermediaries between users and Large Language Models. They take prompts as input and generate responses based on the model's capabilities. Study the various API parameters, such as temperature setting, max tokens, and roles like system, user, and assistant. These parameters allow you to control the behavior and length of the model's responses. Explore popular generative models' APIs, such as ChatGPT, Google's Palm, or DALL-E, and open-source models like Falcon and Meta's Llama. Hands-on experience in making requests to these APIs and understanding their responses will enhance your skills in interacting with Generative Model APIs.
🛠️ Building Your Own GenAI Tools
Once you have mastered Prompt Engineering and gained a deep understanding of Generative Model APIs, you are ready to build your own GenAI Tools. Start by identifying a problem that can be solved using generative AI. It could range from automating content generation to creating chatbots for Customer Service. Design your solution by determining how to use the generative AI model, what inputs it requires, and how the outputs will be utilized. Leveraging your knowledge of APIs and prompt engineering, implement your solution. By the end of this phase, you will have developed a working AI Tool that solves a real-world problem, showcasing your skills as a generative AI developer.
💡 Highlights
- Master Prompt Engineering techniques to elicit desired responses.
- Understand and utilize Generative Model APIs effectively.
- Gain hands-on experience in building your own GenAI Tools.
🔮 Level-2 Developer
As a level-2 Developer in Generative AI, you will take a step further by fine-tuning foundation models for specific tasks. This level requires a deeper understanding of machine learning concepts and the mechanics of Large Language Models. You will specialize in either Generative Models for NLP or Computer Vision, depending on your interest.
📚 Strengthen the Basics
To become a level-2 Developer, you need to strengthen the basics. Deepen your understanding of programming languages, with a focus on Python as it is widely used in the field of AI. Brush up your knowledge of Probability & Statistics, which form the foundation of Machine Learning. Dive deeper into Machine Learning concepts, including Supervised, unsupervised, and reinforcement learning. Explore Deep Learning concepts, such as various architectures, frameworks, and the fine-tuning of foundation models like BERT. Additionally, understanding Attention Mechanism, Auto-encoders, and GANs is crucial.
🔍 Deep Dive into Large Language Models
The next step is to explore popular Large Language Models like GPT-3, PaLM 2, or open-source models like Llama 2. Develop a comprehensive understanding of their architecture, training process, and text generation mechanism. Participate in online discussions, forums, research Papers, and conferences to stay updated with the latest trends and research in Generative AI for NLP or Computer Vision, based on your chosen track.
🔧 Hands-on Fine-Tuning of Foundation Models
In this step, you will define a specific task or problem you want to solve and Gather a Relevant dataset for training. Choose a foundation model suitable for your task, such as GPT-3, GPT-4, or PaLM 2. If these models are not available, consider open-source alternatives like Llama 2 or Falcon. Set up the fine-tuning environment using platforms like Google Colab or Kaggle, but be aware of their usage limits. Start the fine-tuning process, monitor the training, adjust parameters, and evaluate the model's performance using appropriate metrics. Iterate and improve based on the results.
🛠️ Develop Custom GenAI Tools
Put your fine-tuned model to use by building custom AI Tools that solve real-world problems. These tools could range from medical diagnosis assistants to customer service chatbots. Thoroughly test and refine your tools based on user feedback. By the end of this roadmap, you will possess a deep understanding of Large Language Models, hands-on experience in fine-tuning them, and the ability to create custom AI tools.
💡 Highlights
- Strengthen the basics of machine learning and deep learning.
- Explore and understand popular Large Language Models.
- Fine-tune foundation models and build custom AI tools.
Advancing to the Researcher Level
🔬 Researcher in NLP
If you choose the NLP track, your journey as a researcher in Generative AI will involve delving into attention models, building GPT architectures from scratch, and gaining a deeper understanding of reinforcement learning algorithms. Stay up to date with the latest trends and research in Generative AI for NLP by actively participating in online communities, reading research papers, and attending conferences.
🖼️ Researcher in Computer Vision
For those interested in the Computer Vision track, your roadmap as a researcher will involve learning and implementing diffusion models like Stable Diffusion. These models represent the forefront of generative AI for computer vision. Building them from scratch requires a deep understanding of deep learning and computer vision. Additionally, utilize high-performance GPUs or cloud-based services like Google Cloud, AWS, or Azure for computationally intensive tasks. Stay updated with the latest research and trends in Generative AI for Computer Vision through active participation in online communities, research papers, and conferences.
Conclusion
In this roadmap, we discussed the learning journey for becoming a Developer or Researcher in Generative AI. By following the outlined steps, you will acquire the technical skills needed to excel in this field. Additionally, it is important to consider the ethical implications of AI and prioritize fairness, transparency, and user privacy in your models. Remember to nurture your Curiosity, creativity, and commitment for advancing the field. The world of generative AI offers vast opportunities, and by continuously learning and staying updated, you can contribute to its growth and innovation.
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