ai-factory

Project Neo

AI/ML/LLM Software Engineering

This portfolio demonstrates end-to-end expertise in Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLMs) through production-ready data pipelines, algorithms, evaluation functions, and scalable infrastructure. It is written in Python and designed to highlight practical engineering skills for enterprise clients seeking advanced, reproducible solutions to drive outcomes and revenue.

Image courtesy of Codecademy

Value Proposition🪙

• Quality. Ensures model accuracy, robustness, and reliability.
• Variety. A vast library of algorithms supports rigorous mathematical and scientific analysis, adaptable to nearly every field of study.
• Reduced Costs. Efficient model design, optimized infrastructure, and smart data usage lowers analysis, file storage, and maintenance expenses, making AI solutions more scalable and sustainable.
• Saved Time. Automate repetitive/required tasks. Streamline product research & development.
• Simplicity. Plain language, clear systems architecture with modular design, and intuitive user interfaces reduce complexity.
• Information. Data driven insights from models enable better decision-making, pattern recognition, utilization, and creation, and a differentiated competitive advantage.

Table of Contents📖

• Value Proposition
• Features
• Tech Stack
• Prerequisites
• Enterprise Use Cases
• License
• Contact
• Acknowledgements

Features✨

• Data Pipelines:
  • Ingestion from API, JSON, and CSV endpoints
  • Data cleaning, transformation, and feature engineering
  • Scalable workflows for big datasets (e.g., filter, map, reduce, search, and sort functions)
• Algorithms:
  • Graphs (Graphical Models, Bayesian Networks, Markov Chain Logic Networks), posterior probabilities, probabalistic inference
  • Evolutionary algorithms, fitness functions, genetic search
  • Logic, accuracy, deduction
  • Neural networks, squared error, gradient descent (backpropogation)
  • Support vector machines, margin, constrained optimization
  • Bayes’ theorem
  • Decision Trees
  • K-Means Clustering
  • K-Nearest Neighbor
  • Linear Regression
  • Logistic Regression
  • Markov Chains
  • Markov Chain Monte Carlo
  • Monte Carlo Simulations
  • Multiple Linear Regression
  • Naive Bayes Classifier
  • Random Forests
  • Dimensionality Reduction (PCA)
  • Custom AI/ML/LLM model implementations tailored to enterprise use cases
  • Deep learning (Natural Language Processing (NLP), computer vision, time-series models)
• Evaluation Functions:
  • Accuracy, precision, recall, F1
  • Business-aligned OKRs/KPIs
  • Fitness functions
  • Margin
  • MSE, squared error, R^2
  • Posterior probabilities
• Infrastructure:
  • Modular architecture with reusable components
  • CI/CD readiness (GitHub)
  • Cloud-agnostic design (Azure, AWS, GC)

Tech Stack🛠

• Languages: Python, Bash
• Libraries: NumPy, Pandas, PyTorch, Scikit-learn, TensorFlow, XGBoost
• DevOps: GitHub
• UX/UI Visualization: CSS3, HTML5, Kotlin, Matplotlib, Plotly, Seaborn, Swift

Prerequisites

Python 3.10+

Enterprise Use Cases📈

• Asset allocation: Predict the optimal weights of a diversified investment portfolio of New York Stock Exchange, Nasdaq, and S&P 500 equities with the highest Sharpe ratios.
• Earnings Per Share (EPS). Neural network tensor analysis of 10K financial statements, annual reports, return on equity (ROE), and return on invested capital (ROIC) to predict EPS.
• Financial Wellness Scorecard: Comprehensive personal financial planning record for risk-management, short-term planning, and long-term planning that predicts retirement readiness while providing insights, guidance, and recommendations.
• Honey Production: Accurate statistics in service of U.S. agriculture production useful for policymakers, farmers, distributors, restaurants, and researchers.
• Real Estate Prices: Regression model to forecast fair market prices and rents.
• Large Language Models: Seamlessly integrate and fine tune AI language models like OpenAI’s ChatGPT, Google Gemini, or Anthropic’s Claude Opus into your enterprise websites and mobile apps to reduce customer service costs.
• Customer Churn Prediction, Social Media Customer Acquisition Costs, and more!

License⚖️

This project is licensed under a dual license. You are free to use, modify, and distribute this software with attribution for personal or research use, but must open source modifications to the community. Integration into closed source, commercial proprietary systems requires a low cost annual subscription and license fees. See license.txt for details.

Contact📬

• Are you ready for production-ready pipelines and infrastructure to evolve your business?
• Looking for customizable modules that can be adapted to specific business problems?
• Do you need proven scalability and revenue impact, all aligned with enterprise-grade standards, KPIs/OKRs, and real-world use cases?
• For enterprise consulting, partnerships, or custom AI/ML software engineering solutions contact:
• Name: Abraham Doe
• Email: abrahamdoe@gmail.com
• LinkedIn: Profile
• GitHub: Portfolio

Acknowledgements🙏

• Dr. Pedro Domingos, Dr. Ryan Ahmed, Dr. Christian Mayer, Dr. Ebony Thomas, Dr. Gary Calore, and Asabeneh Yetaveh
• Scikit-learn for robust AI/ML utilities
• UC Irvine, Kaggle, and other datasets for training data
• Open-source contributors and the SWE research community
• Inspiration from industry leaders in AI/ML models
• I am grateful. Thank you!

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