I'm a Advanced ML Engineer with a passion for building robust and scalable applications. Some facts about me:
- 🔭 Student pursuing a Master’s Degree in Electrical Engineering and Information Technologies, specializing in Dedicated Computer Systems
- 👨💻 Advanced ML Engineer working on MLOps and GenAI solutions.
- 🥅 2024 Goals: To expend my knowledge in Machine Learning, Deep Learning, Federated Learning, Data & MLOps and Cloud Computing.
- ⚡ Fun fact: I love to experiment and make different kinds of coffee and tea !
go_to_languages:
- Python
- Javascript
- C
cloud_services:
description: Hands-on experience with various AWS services
services:
- AWS CloudFormation: Managing and provisioning your AWS infrastructure as code.
- AWS IAM: Managing access and permissions for your AWS resources.
- AWS Cognito: Adding authentication and authorization to your applications.
- AWS S3: Scalable object storage for your applications.
- AWS ECR: Managed container image registry.
- AWS DynamoDB: NoSQL database for high-performance applications.
- AWS RDS: Managed relational database service for MySQL, PostgreSQL, and other databases.
- AWS Lambda: Running serverless functions in the cloud.
- AWS EC2: Scalable virtual servers in the cloud.
- AWS ECS: Orchestrating and managing containerized applications.
- AWS API Gateway: Building and managing APIs for your applications.
- AWS SageMaker: Building, training, orchestrating and deploying machine learning models and pipelines.
- AWS Bedrock: Fully managed service that offers a choice of high-performing foundation LLMs.
devops_and_fullstack:
description: Libraries, Tools, Frameworks leveraged for DevOps and FullStack
libraries_tools_frameworks:
- Docker: Building and deploying applications in containers for easy scalability and portability.
- CDK & Pulumi: Infrastructure as Code (IaC) tools for defining, deploying, and managing cloud infrastructure.
- Serverless Framework: Simplifying the deployment and management of serverless applications.
- GitHub Actions: Automating CI/CD workflows.
- Flask & FastAPI: My go-to Python frameworks for building robust and efficient backends.
- jQuery or React: Crafting interactive and responsive frontend experiences.
- Streamlit: Transforming Python code into interactive web apps.
machine_learning:
description: Libraries, Tools, Frameworks leveraged for Machine Learning, Deep Learning, Federated Learning, Data Science, and Data & MLOps.
libraries_tools_frameworks:
- Numpy, Scipy, Pandas and PySpark: Fundamental libraries for numerical computing and data manipulation.
- Matplotlib, Plotly, and Seaborn: Creating stunning visualizations to gain insights from data.
- DVC: Open-source, platform-agnostic library for version control of data.
- Scikit-learn, XGBoost, and LightGBM: Powerful Machine Learning libraries for classification, regression, and more.
- Keras, TensorFlow and PyTorch: Building, developing and training Deep Learning models.
- Flower: Exploring Federated Learning for Distributed Machine Learning.
- MLflow: Open-source platform for managing and tracking machine learning experiments.
- LangFuse: Open-source platform for LLM monitoring, observability & tracing.
- Hugging Face: Platform for building, training, and deploying any kind of Machine Learning models.
- Unsloth: Optimization of the resources needed for training and finetuning of LLMs.
- Langchain & Llamaindex: Orchestration frameworks for LLM based applications
- Ragas: Framework that evaluates Retrieval Augmented Generation (RAG) systems.
I am always eager to expand my knowledge and collaborate on challenging projects. Feel free to reach out to me if you're interested in potential collaborations on the following platforms:
- LinkedIn: Bojan Jakimovski
- Hugging Face: Bojan Jakimovski
- Research Gate: Bojan Jakimovski
- Email: jakimovski_bojan@outlook.com
- Personal blog: My Personal Blog
Looking forward to connecting with you and exploring exciting opportunities together! 🚀