AI Engineering: Project Planning for Text Heavy Models

Contemplating building AI powered SaaS or maybe your own game-changing model? This article will get the idea juices flowing and help you formulate a definitive set of requirements for most 'text heavy' model related use cases. Add your thoughts, ideas, and suggestions in the comments below!

Project Scope:

1. Develop a web-based SaaS platform accessible through modern web browsers focused on generating high quality patent drafts.
2. Create & train a generative AI model on high quality patent samples and templates using TensorFlow, PyTorch, or another suitable framework
3. Generate high-quality patent drafts based on user input.

Model Roadmap

1. Data Preprocessing
   1. Data Cleaning
      1. Noise Removal: Eliminate irrelevant information, such as headers, footers, and extraneous characters
      2. Inconsistency Handling: Standardize formats, units, and terminology.
      3. Missing Data: Handle missing values (e.g., imputation, deletion).
   2. Text Preprocessing
      1. Tokenization: Break text into words or subwords.
      2. Stop Word Removal: Eliminate common words that add little semantic value
      3. Stemming or Lemmatization: Reduce words to their root form
      4. Lowercasing: Convert text to lowercase for consistency.
   3. Data Augmentation (Optional)
      1. Synthetic Data Generation: Create new training examples to improve model robustness.
      2. Backtranslation: Translate text to another language and back to increase diversity.
   4. Embeddings
      1. Vectorization: Convert processed text into numerical representations using embedding techniques.
2. High-Quality Patent Document Embeddings:
   1. Utilize a robust embedding model (like those from OpenAI, Hugging Face, or custom-built) to convert patent documents into dense numerical representations.
      1. Jina AIs ‘jina-embeddings-v2-base-en’ and Alibaba's ‘gte-large’ embedding models are completely open source (MIT license) and great option development of prop model powered, licenseable Saas
   2. These embeddings capture semantic and syntactic information crucial for downstream tasks.
3. Frameworks
   1. Hybrid approach utilizing PyTorch during prototyping/development stage and transitioning to TensorFlow for production to leverage the strength of each platform
   2. Pre-trained models (like BERT, RoBERTa) to serve as a starting point.
4. Model Architecture:
   1. Transformer - Why?
      1. Long-term dependencies:
         1. Patents often involve complex legal and technical language with long-range dependencies. Transformers excel at capturing these relationships due to their attention mechanism.
      2. Parallel processing:
         1. Transformers can process input data in parallel, leading to faster training and inference times.
      3. State-of-the-art performance:
         1. They have consistently outperformed other architectures on various NLP benchmarks.
5. Embedding Integration
   1. The embeddings will act as input features for the base model and enhance textual information interpretation and processing capabilities.
6. Data Splitting
   1. Train, Validation, and Test Sets: Divide data into subsets for model training, evaluation, and testing.
7. Training and Fine-Tuning:
   1. Train model on the embedded patent data.
   2. Consider incorporating additional features (e.g., publication date, inventor information) for enhanced performance.
   3. Fine-tune the model iteratively based on user feedback and performance metrics.
8. Laser Vision:
   1. Define primary product focus and stick to it!
   2. Gather user feedback and iterate rapidly to enhance the product.

When to Consider GPU Acceleration for AI Models

GPU Acceleration becomes a recommended strategy when the computational demands of models outpace the capabilities of your CPU. This typically occurs with:

1. Large Datasets:
   1. Massive amounts of data:
      1. When dealing with millions or billions of documents, processing times on CPUs can become prohibitively long.
   2. Complex preprocessing:
      1. Tasks like tokenization, stemming, and lemmatization can be accelerated significantly with GPUs.
2. Complex Models:
   1. How large and complex is the transformer model?
   2. Deep neural networks:
      1. Transformer models, especially large-scale ones, benefit greatly from GPU acceleration due to their matrix operations.
   3. Iterative training:
      1. Models that require multiple epochs of training can see substantial speedups with GPUs.
3. Real-time or Near-Real-Time Processing:
   1. Interactive patent search:
      1. If you aim to provide real-time search results based on patent queries, low latency is crucial. GPUs can handle these demands effectively
   2. Patent suggestion systems:
      1. Generating patent suggestions on the fly requires fast processing times.
4. Experimentation and Hyperparameter Tuning:
   1. Multiple experiments:
      1. GPUs can accelerate the training and evaluation of different model architectures and hyperparameters.
   2. Iterative development:
      1. Rapid prototyping and experimentation benefit from faster computation times.
5. Budget:
   1. What is your financial allocation for GPU's at each stage?
6. Cloud vs. On-Premise:
   1. Do you prefer a cloud-based or on-premise solution?
7. Additional Considerations:
   1. GPU Memory:
      1. Ensure the GPU has sufficient memory to handle your dataset and model.
   2. Programming Framework:
      1. Choose a framework with strong GPU support (e.g., PyTorch, TensorFlow).
   3. Cost-Benefit Analysis:
      1. Evaluate the cost of GPU hardware and software against the expected performance gains.

Evaluating GPU Needs for Patent Generation Transformer Models

1. Dataset Size Considerations:
   1. Small to Medium Datasets:
      1. Range:
         1. Typically under 100GB.
      2. Suitable GPUs:
         1. Consumer-grade GPUs (e.g., NVIDIA GeForce RTX series) can handle these datasets efficiently.
      3. Considerations:
         1. For smaller datasets, focus on GPU architecture (e.g., Tensor Cores) and memory bandwidth for optimal performance.
   2. Medium to Large Datasets:
      1. Range:
         1. Between 100GB and 1TB.
      2. Suitable GPUs:
         1. Data center GPUs (e.g., NVIDIA Tesla series) offer better performance and larger memory for handling these datasets.
      3. Considerations:
         1. Consider using multiple GPUs or distributed training for efficiency.
   3. Very Large Datasets:
      1. Range:
         1. Over 1TB.
      2. Suitable GPUs:
         1. High-end data center GPUs or cloud-based GPU instances are recommended.
      3. Considerations:
         1. Distributed training and data partitioning are essential for handling such large datasets.
2. Factors Affecting GPU Choice:
   1. Data Format:
      1. Compressed data can significantly reduce storage requirements.
   2. Data Preprocessing:
      1. Efficient data preprocessing can reduce the dataset's footprint.
   3. Model Complexity:
      1. Larger, more complex models require more GPU memory.
   4. Batch Size:
      1. Experiment with different batch sizes to optimize GPU utilization.
3. When to Consider Consumer-Grade GPUs
   1. Early-stage development:
      1. Testing and prototyping with smaller datasets.
   2. Budget constraints:
      1. Limited financial resources for hardware.
   3. Low to moderate computational demands:
      1. For models with relatively simple architectures.
4. When to Consider Data Center or Cloud-Based GPUs
   1. Large-scale training:
      1. Handling massive datasets and complex models.
   2. Real-time or near-real-time applications:
      1. Requiring low latency and high throughput.
   3. Distributed training:
      1. Distributing the workload across multiple GPUs
5. GPU Options - based on typical patent generation model requirements, here are some potential GPU options:
   1. Consumer-Grade GPUs
      1. Pros:
         1. Relatively affordable, suitable for smaller datasets and simpler models.
      2. Cons:
         1. Limited memory, slower performance compared to professional-grade GPUs.
      3. Examples:
         1. NVIDIA GeForce RTX series (e.g., RTX 4090, RTX 3090), AMD Radeon RX series.
   2. Data Center GPUs
      1. Pros:
         1. High performance, large memory, suitable for large-scale training and inference.
      2. Cons:
         1. Higher cost, often requires specialized hardware and software.
      3. Examples:
         1. NVIDIA Tesla series (e.g., A100, H100), AMD Instinct MI series.
   3. Cloud-Based GPUs
      1. Pros:
         1. Scalability, pay-per-use model, access to high-performance GPUs without upfront investment.
      2. Cons:
         1. Potential latency, dependency on cloud provider.
      3. Examples:
         1. NVIDIA GPUs on cloud platforms like AWS, GCP, Azure.
6. Integrating GPU Acceleration - Integration overview for patent generation model.
   1. Deep Learning Framework:
      1. Choose a framework with strong GPU support, such as PyTorch or TensorFlow.
   2. GPU Device:
      1. Identify and specify the GPU device you want to use.
   3. Data Transfer:
      1. Transfer your dataset to the GPU's memory for efficient processing.
   4. Model Placement:
      1. Move your model to the GPU for accelerated computations.
   5. Optimization:
      1. Utilize framework-specific optimizations for GPU performance (e.g., mixed precision, tensor cores).

Typical Timeline

1. Phase 1: Planning and Data Preparation (2-4 weeks)
   1. Define project scope and goals
   2. Data acquisition and cleaning
   3. Exploratory data analysis
2. Phase 2: Model Development and Training (4-8 weeks)
   1. Experiment with different model architectures
   2. Train and fine-tune models
   3. Evaluate model performance
3. Phase 3: Product Development (4-8 weeks)
   1. Design user interface and user experience
   2. Develop front-end and back-end components
   3. Integrate AI model into the application
   4. Build API for external integrations (optional)
4. Phase 4: Testing and Iteration (2-4 weeks)
   1. Conduct thorough testing
   2. Gather user feedback
   3. Iterate on product based on feedback
5. Total estimated time: 12-24 weeks (3-6 months)

Cost Breakdown for MVP Grade SaaS

1. Development: $60,000 - $150,000
2. Operational costs (1st year): $25,000 - $50,000
3. Total Cost MVP: $85,000 - $200,000 (estimated)

Cost Breakdown for Enterprise Grade SaaS

• Data Acquisition and Preparation: $25,000 - $50,000
  • Cleaning and labeling the data for training the model.
  • Licensing costs for the dataset might apply.
• Model Development and Training: $200,000 - $400,000
  • This involves designing the text embedding model architecture suitable for patent data.
  • Training the model on the prepared dataset, potentially requiring significant computational resources (GPUs/TPUs).
  • Fine-tuning the model for optimal performance and accuracy.
• SaaS Development: $100,000 - $200,000
  • Development of a user-friendly web interface for inputting claims, drawings, and descriptions.
  • Developing functionalities to integrate with the trained model and generate draft applications.
  • Implementing secure user authentication and data storage mechanisms that ensure various levels of data compliance including but not limited to Soc2, GDPR, CCPA, HIPPA, SHIELD, ISO27001
• Infrastructure Costs: $50,000 - $100,000
  • Cloud computing resources for hosting the SaaS platform and the trained model.
  • Costs might vary depending on usage and scalability needs.
• Team Salaries: $150,000 - $300,000
  • Hiring data scientists, machine learning engineers, and software engineers with expertise in NLP, AI, and SaaS development.
  • Costs depend on location, experience level, and team size.

Total Estimated Cost Enterprise Grade: $525,000 - $1,050,000

Advantages of This Approach

• Efficiency: Leveraging pre-trained embeddings and deep learning frameworks accelerates development.
• Scalability: Deep learning models can handle large datasets and complex tasks.
• Flexibility: You can easily expand the product's features by adding new models or functionalities.
• Continuous Improvement: Iterative development allows for refinement based on user feedback.

Challenges and Considerations

• Licensing: Its crucial to carefully review the terms of service and licensing agreements of any AI model or platform before building a licensed SaaS product.
• Data Quality: The quality of your patent dataset significantly impacts model performance.
• Computational Resources: Training and deploying deep learning models can be computationally intensive.
• Model Explainability: Understanding how the model arrives at its conclusions can be challenging.
• Ethical Implications: Ensure fairness and avoid biases in your model and data.

Additional Considerations:

• Model Legal Compliance: Ensure the model's outputs comply with patent application formatting and legal requirements.
• Data Security: Implement robust security measures to protect user data (patent information).
• Scalability: Design the platform to handle potential growth in user base and data volume.
• Model Explainability: Consider incorporating features to explain the model's reasoning behind generated drafts, fostering user trust.

Recommendations:

• Conduct a pilot project with a smaller dataset to validate the model's effectiveness and user interface before full-scale development.
• Consider partnering with legal professionals to ensure the model's outputs meet legal requirements.
• Explore open-source patent application datasets to potentially reduce data acquisition costs.