Experienced in the fields of Artificial Intelligence, Data Science, Software Engineering, and Big Data. I specialize as Machine Learning Engineer and provide consulting services for AI & Data related projects.

Highly motivated with a passion for exploring the intersections of Mathematics, Data, and Computer Science, and how they can be used to solve real-world problems.

The repositories you'll see here are mostly for my learning journey towards growing in my career in data science and machine learning engineering or simply fulfilling my personal quest for knowledge.

• ml-regression-project-california-housing: showcase my ability to produce a successful and resilient Machine Learning system from the EDA to the engineering phase. It also shows my consultant skills in all the cycles of a project of this kind.
• scratch-nn: python library for building simple Deep Neural Networks from scratch, using only vectorized operations with NumPy to develop further understanding in the base concepts of neural networks and provide a foundation for building a Production-ready Python library.
• [ml-classification-titanic]: show my skills as Data Scientist and ML Engineer. (still to be released, working on it currently)
• statistical-rethinking: repository to practice statistics with Python libraries learned in the book of the same name. (Abandoned atm, I feel my statistical skills are proficient for Data Science and ML, but maybe one day I will come to the basics again to learn a statistical framework)
• block-chain-java: simulate the functionality of blockchain technology used by crypto-currencies.

Capgemini Engineering: Machine Learning Engineer, AI & Data Consultant

Worked on projects related to:

• Exploratory Data Analysis, Data Mining, and design and built predictive services for different sectors like Marketing, Life Sciences, Manufacturing, Finances ...
• Developed Machine Learning Operations (MLOps) and pipelines, based on Python, and AWS Sagemaker, with data versioning using Feature Stores, models versioning in Model Registries, and history tracking of experiments and deployments.
• Mix Marketing Modeling with Bayesian MCMC using PyMC for a pharmaceutical company.
• Analyzed data from large-scale databases and developed Machine Learning models in Python with Scikit-Learn and TensorFlow and resilient predictive services to serve them.
• Facilitated the serving and CI/CD workflows for an operations optimization engine based in Pyomo and built a microservice with Docker to deploy it in Kubernetes.
• Increase team productivity and code quality by creating a guide consolidating good development, documenting, and testing practices, applying DevOps principles, and developing a Python library to abstract Machine Learning workflows.
• Designed the architecture and operations of microservices for a Federated Machine Learning platform with flwr, Tensorflow Federated, Django, Mlflow, and Apache Airflow.
• Contributed to AWS SageMaker SDK to facilitate the use of Feature Stores.
• Conducted a talk about MLOps with AWS at a conference

I am a master's student in the Applied Artificial Intelligence online program at the Luleå University of Technology As this takes most of my time, in addition to working the learning paths below got stopped.

• Mix marketing modeling (learning currently). Work related, so the project cannot be shown.

• Bayesian Statistics (stopped), primarily through the book Statistical Rethinking, and some articles that you can find in towards datascience. At the moment this is my progress

• Time Serie Analysis (finished), I am mainly using any source that I find interesting from this repository which contains a lot of references to learn. Unluckily, any project related to TSA cannot be shown as there are used in my job :(.

• Neural Networks and Deep Learning (finished), by the Deep Learning Specialization of Coursera, and I have been enjoying being exposed to Linear Algebra and Deep Learning. My progress (First course, finished)

• Data Engineering with Apache Spark and Delta Lakes (finished). Acquiring a Data Engineer Professional Certificate from Databricks. I enjoy data modeling and creating data pipelines to support Data Science projects.

• MLOps, DevOps, and AWS SageMaker (finished). Learned and applied in all of the projects at work.
• Cloud (Azure, AWS) and CI/CD tools (Azure DevOps, GitHub Actions, AWS CodeBuild, CodeDeploy, and CodePipeline) (finished). Learned and applied in all of the projects at work.

Open source projects related to Environmental Intelligence to help to preserve nature or evaluate the state of it.

• Supervised Machine Learning: RegressionSupervised Machine Learning: Regression
• Exploratory Data Analysis for Machine Learning
• Neural Networks & Deep Learning, DeepLearning.AI
• Data Processing with Azure
• Data Engineering, Databricks
• Apache Spark Associate Developer, Databricks
• Delta Lake Accreditation, Databricks
• Fundamentals of Unified Data Analytics, Databricks
• Architecting and SysOps in AWS · pdf format ...

See more at my Linkedin profile.