Machine Learning Engineer for Mentorship in Fine-Tuning and Integrating Models

Description:

I am seeking an experienced machine learning engineer to act as a mentor, helping me refine and expand my skills in fine-tuning and deploying machine learning models. The focus will be on improving GraphCodeBERT and CodeBERT for summarizing and visualizing GitHub repositories, as well as integrating these models with LangChain and OpenAI for enhanced functionality.

I have already made progress in this project and am looking for a mentor who can provide step-by-step guidance to improve my technical understanding, fine-tune models effectively, and build scalable, production-ready solutions. This is not just about completing tasks?it?s about equipping me with the skills and knowledge to confidently ship advanced machine learning models.

Scope of Work:

Advanced Fine-Tuning Techniques:

Walk me through preparing datasets for fine-tuning, including preprocessing, tokenization, and augmentation for optimal model performance.

Teach strategies for hyperparameter tuning, avoiding overfitting, and evaluating model performance.

Guide me in leveraging transfer learning to maximize efficiency with limited data.

LangChain and OpenAI Integration:

Provide best practices for integrating LangChain and OpenAI with GraphCodeBERT and CodeBERT to enhance code summarization and visualization features.

Explain how to chain different AI models together effectively to achieve robust results.

Deploying and Scaling Machine Learning Models:

Teach how to deploy the fine-tuned models in production environments using frameworks like Flask, FastAPI, or Django.

Show me how to scale the models for real-time or batch processing using tools like Docker, Kubernetes, or AWS.

Provide an overview of setting up APIs for seamless interaction with frontend applications.

Data Collection and Curation:

Teach me how to systematically collect, clean, and store large-scale GitHub datasets for ongoing model improvement.

Discuss techniques for maintaining data pipelines and ensuring high-quality inputs for training.

Experimentation and Model Monitoring:

Show me how to set up experimentation frameworks to track and compare model performance across iterations (e.g., using MLFlow or Weights & Biases).

Guide me on setting up monitoring tools to track model behavior in production, detect drift, and plan retraining cycles.

Best Practices for Machine Learning Projects:

Explain the end-to-end lifecycle of machine learning projects, including ideation, prototyping, testing, and deployment.

Share insights into version control for models and datasets, collaborative workflows, and documentation.

Hands-On Mentorship and Skill Development:

Offer in-depth, step-by-step guidance at each stage to ensure I understand both the "how" and the "why" behind every technique.

Provide tailored advice on improving my project based on best practices and real-world experience.

Deliverables:

Fine-tuned GraphCodeBERT and CodeBERT models tailored to my project requirements.

Complete documentation of each process, including fine-tuning, integration, and deployment.

Fully integrated LangChain and OpenAI components for enhanced summarization and visualization.

A deployed model accessible via APIs or other interfaces, ready for production use.

Personalized recommendations for further skill-building and learning resources.

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