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3 followers · 15 following

Achievements

Achievement: Pair ExtraordinaireAchievement: Pull SharkAchievement: YOLOAchievement: Quickdraw

Achievements

Achievement: Pair ExtraordinaireAchievement: Pull SharkAchievement: YOLOAchievement: Quickdraw

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Abeshith/README.md

πŸ‘¨β€πŸ’» About Me

I am a Machine Learning Engineer πŸ€– passionate about building AI-driven solutions and enhancing my expertise in Data Science πŸ“Š, Machine Learning 🧠, and Deep Learning 🌟. I specialize in ML algorithms πŸ“‰, Deep Learning models 🧬, and Natural Language Processing (NLP) ✍️, with a growing focus on Large Language Models (LLM) πŸ’¬. My goal is to design, implement, and optimize AI systems that drive meaningful insights.

I have hands-on experience with AWS services ☁️ such as EC2, Lambda, EKS, ECR, S3, CloudWatch, SageMaker, and CloudFormation (CFT), implementing cost optimization πŸ’° strategies, resource monitoring using lambda and cloudwatch πŸ“ˆ, and deploying Docker containers using ECS 🚒.

I actively participate in Kaggle πŸ† and have earned multiple votes for my solutions in the Playground Series. I’ve solved over 400+ problems on LeetCode πŸ’»& 550+ on GFG (focusing on Data Structures) and earned 5-star ratings 🌟 on HackerRank in both Java and Python.

Additionally, I have been exploring Docker 🐳 for containerization and Kubernetes πŸ› οΈ for orchestration, along with CI/CD practices using GitHub Actions βš™οΈ and Jenkins. I also write Terraform scripts πŸ“ to manage infrastructure

Find Me Around the Web 🌍:

  • πŸ“§ Email: [email protected]
    Reach out to me via email for collaborations, inquiries, or just to say hello! βœ‰οΈ

  • πŸ’Ό LinkedIn: @abeshithkumaravel
    Connect with me on LinkedIn for professional updates and networking. 🀝

  • 🌐 Portfolio: abeshith-portfolio.netlify.app
    Check out my personal portfolio for a look at my projects, skills, and achievements. πŸ’»

  • πŸ† Kaggle: @abeshith
    Explore my Kaggle profile to see my data science and machine learning projects, and solutions to challenges. πŸ“Š

  • πŸ’» LeetCode: @Abeshith
    Follow my LeetCode journey where I tackle data structures, algorithms, and more! (Solved: LeetCode Badge) πŸ…

  • πŸ“š GeeksforGeeks: @abheshith_24
    Browse my GeeksforGeeks profile to see my problem-solving skills in data structures, algorithms, and more. πŸŽ“

Technologies I Work With πŸ’»:

LANGUAGES



FRAMEWORKS & LIBRARIES


MLOPS

MLOps Apache Airflow MLflow DVC Astronomer ZenML Weights & Biases

DEPLOYMENT

Docker Kubernetes Jenkins GitHub Actions GitHub Terraform

CLOUD COMPUTING

AWS

MONITORING TOOLS

Prometheus Grafana

Pinned Loading

  1. RAG-FundaMentals RAG-FundaMentals Public

    πŸ”° A Comprehensive RAG repository covering basic vanilla RAG techniques, advanced retrieval methods, hybrid search fusion approaches, hands-on reranking techniques with code + explanation πŸ“šβœ¨

    Jupyter Notebook

  2. MachineLearning-PipeLine MachineLearning-PipeLine Public

    Full MLOps pipeline with DVC for data/model versioning, MLflow for experiment tracking, & Airflow DAGs orchestrating data ingestion, validation, feature engineering, training & evaluation. Integrat…

    Python

  3. FineTuning_LanguageModels FineTuning_LanguageModels Public

    🎯 Fine-tune large language models and use them for text-related tasks. This repository provides a straightforward approach to fine-tuning models like Gemma, Llama πŸ¦™, and Mistral πŸŒͺ️ for various NLP …

    Jupyter Notebook

  4. Enhanced-Image-Search Enhanced-Image-Search Public

    πŸ–ΌοΈ An web application that allows users to upload images, extract embedded text using OCR, and store metadata in a searchable database. πŸ” Users can search for specific text to retrieve relevant ima…

    HTML

  5. Java_Fundamentals Java_Fundamentals Public

    πŸš€ This Java repository covers everything from basics to advanced topics like OOP, Linked Lists πŸ”—, Sorting Algorithms πŸ“Š, Recursion πŸ”„, Backtracking πŸ”™, and BST 🌳. It’s a perfect resource to learn, pra…

    Java

  6. Kubernetes Kubernetes Public

    πŸ₯ A compact collection of Kubernetes YAMLs and best practices β€” perfect for experimenting locally πŸš€ Intended to help you learn core concepts like Deployments, Services, and ConfigMaps in just a few…

    Python