pedro-a-d-s / concrete_strength_prediction

TechCon Experimentation with MLFlow and Dask

Hi, I'm Pedro! 👋

🚀 About Me

I'm a Machine Learning Engineer and data science professional passionate about leveraging data-driven insights to solve complex problems and deploy ML pipelines. With a strong background in mathematics, machine learning and MLOps, I have successfully created ML models on Itaú Unibanco and my GitHub profile.

🛠 Main Skills

I have expertise in:

• Data Science: I have expertise in analyzing large datasets, applying statistical techniques, and building predictive models to extract valuable insights and drive decision-making.
• MLOps: I am specialized in implementing MLOps practices to streamline the development, deployment, and monitoring of machine learning models.
• Machine learning: I have hands-on experience with various machine learning algorithms and frameworks, allowing me to develop robust and accurate models for diverse applications.
• SQL: I am proficient in SQL and have utilized it extensively to extract, transform, and analyze data from relational databases.

🔗 Links

• - Let's connect on LinkedIn to stay updated on my latest professional endeavors.

👯 Collaboration

I'm enthusiastic about collaborating on data science and machine learning open source projects that involve solving real-world problems. If you have any opportunities or ideas, I would love to hear from you!

💬 Ask Me About

• MLFlow to enhance model versioning, tracking, and deployment capabilities, enabling reproducible and scalable machine learning workflows.
• Python programming, including best practices, advanced concepts, and techniques for writing clean and efficient code.
• Machine learning algorithms, model evaluation techniques, and practical tips for building effective models.
• Kedro for scalable and reproducible data pipelines.
• Best practices in MLOps, including model deployment, monitoring, and continuous integration and delivery (CI/CD) pipelines.
• Testing methodologies and frameworks in Python, helping you write robust and reliable code through unit tests, integration tests, and more.
• OOP principles and techniques in Python, including class design and how to use them in the data field.
• Various libraries used in the data lifecycle, such as pandas, NumPy, scikit-learn, and matplotlib.

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