Training Your Own AI Model Is Not As Hard As You (Probably) Think
TLDRThe video emphasizes that training a specialized AI model is easier and more efficient than using large, off-the-shelf models. It shares the experience of converting Figma designs into high-quality code, highlighting the benefits of smaller, specialized models over general-purpose ones like GPT-3 and 4. The process involves breaking down the problem, identifying the right model type, generating quality example data, and using tools like Google's Vertex AI for training. The result is a faster, cheaper, and more customizable solution that outperforms general models.
Takeaways
- 🚀 Training your own AI model can be easier than expected with basic development skills.
- 💡 Using an off-the-shelf large model like GPT-3 or GPT-4 may not always yield satisfactory results for specific use cases.
- 📈 Customizing and optimizing your own model can lead to over 1,000 times faster and cheaper results compared to large models.
- 🔍 Breaking down the problem into smaller pieces is crucial for training specialized AI models.
- 🛠️ Starting with plain code for parts of the problem that can be solved without AI can significantly simplify the process.
- 🧠 Choosing the right type of model and generating lots of example data are key to training a successful AI model.
- 🔎 Quality of the model is highly dependent on the quality of the data used for training.
- 🌐 Utilizing public and free data sources, like web crawlers, can help generate necessary training data.
- 💻 Tools like Google's Vertex AI can streamline the model training process without requiring extensive coding.
- 📊 Testing an LLM (Large Language Model) is recommended for exploratory purposes, but relying on plain code and specialized models can lead to better results.
- 🎨 For the final step of customization, using an LLM can be effective in adjusting and enhancing the baseline code.
Q & A
Why might using an off-the-shelf large model like those provided by OpenAI not always be the best solution?
-An off-the-shelf large model might not be the best solution because it can be slow, expensive, unpredictable, and difficult to customize. It may also not work well for specific use cases, as it may not meet the unique requirements or provide the desired level of performance.
What were the main drawbacks experienced when trying to use OpenAI's GPT-3 and GPT-4 for the given problem?
-The main drawbacks included the models being incredibly slow, insanely expensive, highly unpredictable, and very difficult to customize. These factors made them unsuitable for the specific use case described in the script.
What was the main advantage of training a specialized AI model over using a large, general-purpose model?
-The main advantage was that the specialized AI models were smaller, faster, and cheaper. They were also more predictable, reliable, and highly customizable, leading to better results tailored to the specific use case.
How did the process of breaking down the problem help in developing the AI model?
-Breaking down the problem into smaller pieces allowed the identification of specific challenges that needed to be addressed. This approach facilitated the development of specialized models for each part of the problem, leading to a more efficient and effective overall solution.
What type of model was used for the image identification task in the script's example?
-An object detection model was used for the image identification task. This model could take an image and return bounding boxes for specific types of objects, which was adapted for the novel use case of processing Figma designs.
How was the training data for the object detection model generated?
-The training data was generated using a symbol crawler with a headless browser to identify images and their bounding boxes on web pages. The data was then manually verified and corrected to ensure the highest quality for the model.
What platform was used to train the object detection model without requiring coding?
-Google's Vertex AI platform was used to train the model. It provided built-in tools for uploading data and training the model without the need for coding, making the process more accessible.
What was the minimum cost for training the object detection model on Google Cloud?
-The minimum cost for training the object detection model on Google Cloud was about $60, which is significantly cheaper than using one's own GPU to run the training for hours or days.
How did the script's example utilize an LLM for the final step of the process?
-An LLM was used to refine the baseline code generated from the design. The LLM could make adjustments to the code, providing new code with small changes, which was the best solution for the final step of customizing the code according to user preferences.
What was the final outcome of combining specialized models and plain code in the script's example?
-The final outcome was a tool chain that could rapidly generate high-quality, responsive, pixel-perfect code from Figma designs. This included the ability to output code in various formats and styles, enhancing the user experience and providing a robust solution.
What advice does the script offer for those considering training their own AI models?
-The script advises testing an LLM for exploratory purposes but encourages writing plain code as much as possible. Where bottlenecks occur, it suggests finding specialized models that can be trained with self-generated data using platforms like Vertex AI, to create a custom and effective tool chain.
Outlines
🤖 Training a Specialized AI Model
This paragraph discusses the process of training a specialized AI model for a specific use case, as opposed to using a large, off-the-shelf model like those from OpenAI. The author explains that while using an LLM (like GPT-3 or GPT-4) seemed like a good idea initially, it proved to be slow, expensive, and difficult to customize. The author's team decided to train their own model, which turned out to be easier than expected and yielded much faster, cheaper, and more predictable results. The key takeaway is the importance of breaking down the problem into smaller, manageable pieces and exploring the possibility of solving parts of the problem with plain code before resorting to AI.
🔍 Generating Training Data for Custom Model
The second paragraph focuses on the critical aspect of generating high-quality training data for the custom AI model. The author describes their approach to creating a symbol crawler using a headless browser to identify images and their bounding boxes on websites. The importance of verifying and correcting the data manually to ensure the highest quality is emphasized. The author's team used Google's Vertex AI for uploading data and training the model without the need for coding, which streamlined the process and kept costs relatively low. The paragraph highlights the balance between automation and manual quality assurance in training AI models.
🚀 Building a Robust Tool Chain with AI
In the final paragraph, the author talks about the culmination of their efforts in building a robust tool chain that combines specialized AI models with plain code to create a powerful solution for their specific problem. They discuss the process of using specialized models for image identification and layout hierarchy, and then leveraging an LLM for the final step of code customization. The author emphasizes the benefits of controlling the entire process, which allows for rapid iteration and customization. They invite readers to check out their blog post for a more detailed breakdown and express excitement for the potential applications of their tool chain.
Mindmap
Keywords
💡AI Model
💡Figma Design
💡Object Detection Model
💡Google's Vertex AI
💡Data Quality
💡Large Language Model (LLM)
💡Code Generation
💡Specialized AI Model
💡Customizability
💡Predictability
💡Cost-Effectiveness
Highlights
Training your own AI model is easier than you think and can yield faster, cheaper, and better results than using large off-the-shelf models.
Using an off-the-shelf model like OpenAI's GPT-3 and GPT-4 can be slow, expensive, unpredictable, and difficult to customize.
When customizing an off-the-shelf model didn't work, the team decided to train their own model, which was not as hard as anticipated.
Their custom model was over 1,000 times faster and cheaper, serving their use case better and being more predictable and reliable.
Breaking down the problem into smaller pieces is crucial for training a specialized AI model.
Pre-existing models may not always work well for specific use cases, necessitating the creation of custom models.
Large models are expensive and time-consuming to train, and generating the required data can be challenging.
Instead of a single large model, it's often better to solve as much of the problem without AI and break the problem into discrete pieces.
Identifying the right type of model and generating lots of example data are key to training your own AI model.
Object detection models can be repurposed for novel use cases, such as processing design files from Figma.
Public and free data can be derived from the web to train your models, with tools like a symbol crawler using a headless browser.
The quality of your model is entirely dependent on the quality of your data, so meticulous verification and correction are essential.
Google's Vertex AI provides built-in tools for uploading data and training models without the need for coding.
Using the default settings and minimum training hours on Vertex AI can significantly reduce costs.
After training, you can deploy your model and use an API to get bounding boxes with confidence levels.
For certain parts of the problem, like style and basic code generation, plain code is the most efficient and reliable solution.
For customization, an LLM can be used to adjust basic code, even if it's not the best solution for the entire process.
Combining specialized models with plain code creates a robust and incredible tool chain for users.