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🤖 *Beep Beep - confident at █████▒ 99.9% certainty*🤖
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🎓 Computer Scientist and Engineer in Artificial Intelligence, with experience leading data analysis, predictive modeling projects and cloud aplications in AWS and Azure. 🔧 Proficient in utilizing various tools/API´s to drive successful AI initiatives such as vectorial databases, monitoring or model context protocol tools. 🗣️ Great B2 level of English, enabling expanded job opportunities and effective communication. 🏕️ Dedicated member of the Scout Association of Colombia for over nine years, recognized for leading and participating in diverse initiatives and projects nationwide. 🧠 Gained invaluable experience in problem-solving, public speaking, assertive presentation of ideas, and comprehensive soft skills development. 🎯 Amuse in explore solutions involving data and cloud technology. ❤️ Passionate IA enthusiast, constantly seeking to transform code into innovative, significant and fun solutions. |
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A collection of some of my favorite, interesting and practical repositories. Click to discover everything while exploring!
🗣 A convolutional autoencoder that gets latent representations and evaluates them.
👩💻 Use a temporal wrapper, featuring Gru-bi RRN, 3D convolutions, and sequence preprocessing.
📐 Includes L2 and cosine distance to effectively evaluate each language trained.
🐳 Dockerized distributed cloud architecture.
🎭 LangChain and Langraph to organize different agents communication.
🧠 Uses RAG strategy with ChromaDB ingested with 2020 Tweets.
💎 Use an Gemini API to analice information.
🐘 Include Maestro SDK for its deployment.
💡 Has a nice web interface to showcase the app.
☁ Design in AWS using EC2 instances with custom RNN to generate a new scientific name.
🦙 Ollama integration to generate the physical description and Dall-e to visualize it, both via Ngrok.
🐱🐉 Immersive web museum to chat with any dinosaur created using the Gemini API.
📶 Orchestatres serverless ETL pipeline using AWS Lambda to automate scraping and processing of news webs.
📲 Integrates AWS Glue and Athena to catalog, transform, and store structured data Amazon S3.
📂 Generates machine-learning-ready datasets in CSV format for predictive analysis and modeling.
☁ Integrates simple cases of AI in Azure virtual machines.
✨ Have different experiments, from object detection in images to extracting text from images.
✅ This repository is a copy of a set of official tutorials from Microsoft as a tutorial for the use of AI in cloud.
🌐 Ingest data from Sakila in a AWS RDS utilizing ETL process as a introduction to Big Data.
📎 Uses AWS Glue Workflows to orchestrate the process executing jobs, then crawlers.
⌛ Includes CI/CD workflows to accelerate deployment and ensure the robustness of the web application.
🚂 Implements a RAG search over embeddings of text and images to recognize train cars.
✍ Evaluates the search with RAGAS, Faithfulness, Answer Similarity, and Context Precision.
💽 Uses a Gemini API as a multimodal tool to generate the final response with the results.
🔝 Starts with basic concepts to advanced time series, data cleaning, interpretation, and predictive models.
📅 A collection of different notebooks that document years of learning Data Analisis and ML.
🏁 Integrates various final projects with deep analysis of data in practical cases.
🥇 The collection goes from basic prediction models to deep data evaluation and complex clustering tasks.
⭕ The final projects include experiments with supervised and unsupervised learning.
📈 Has methods of feature engineering, optimization, and model evaluation with tools such as the ROC curve.