I develop artificial intelligence solutions utilizing machine learning, deep learning, and large language models in Python, ensuring they include well-structured code, precise metrics, and a final product that a team can easily comprehend, execute, and expand.

Services offered include:

• Forecasting and Time Series Analysis: covering demand prediction, sales forecasting, churn analysis, and identifying anomalies.
• Natural Language Processing and Large Language Models: encompassing text categorization, sentiment analysis, topic identification, Retrieval-Augmented Generation pipelines, and chatbot development.
• ️ Computer Vision and Vision-Language Models: involving image categorization, object detection, quality assurance, and combining multiple data types.
• Traditional Machine Learning: focusing on categorization, prediction, grouping, and recommendation systems.
• Automation and Application Programming Interfaces: setting up batch processing workflows, RESTful endpoints, and interactive dashboards.

Deliverables include:

• Code that is clean and reproducible, along with necessary environment specifications.
• Optimized models and performance indicators (such as AUC, F1 score, MAPE, Confusion matrix, or specific key performance indicators).
• A performance summary written in accessible language.
• Optional deployment solutions (like an API or a simple application).
• Support for questions and a brief instructional session (if requested).

The streamlined process involves:

1. Initial Data Assessment (complimentary): sharing a data sample and project objective to confirm feasibility.
2. Developing a Baseline Model: to establish and agree upon the expected performance level.
3. Final Handover: providing clean code, documentation, and deployment choices if required.

Technologies utilized:

Python, pandas, scikit-learn, various ML Models, PyTorch, TensorFlow, HuggingFace, OpenAI.