Highview
About me: I'm an Electronics Engineer with a fascinating career arc—from 12+ years in power semiconductor reliability at International Rectifier/Infineon to supporting the FAA's National Airspace System at Leidos, and now actively transitioning into data engineering and ML operations. My technical foundation in MOSFETs, IGBTs, and GaN technologies for automotive and aerospace applications has given me a deep appreciation for system reliability, rigorous testing methodologies, and safety-critical design—principles I now apply to building robust data pipelines and production ML systems. Currently: 📡 Working on system integration packages for NEXCOM Radios and Air-to-Ground Protocol Converters at Leidos 🎓 Completing Data Engineering Zoomcamp 2026 (Docker, SQL, Terraform) 👥 Serving as peer reviewer for ML Zoomcamp 2025 and AI Dev Tools Zoomcamp 2025 capstone projects 🔧 Recently completed ML Zoomcamp 2025 with hands-on projects in computer vision, deployment, and orchestration Recent Builds: ML Model Registry & Deployment Dashboard (FastAPI, React, TypeScript, Railway) End-to-end ML pipelines with Docker/Kubernetes orchestration Transfer learning systems for image classification and human activity recognition Real-time collaborative coding platforms with WebSocket communication I'm a strong believer in learning in public—you'll find me regularly sharing my bootcamp progress, technical challenges, and solutions on LinkedIn and Bluesky. My GitHub repositories document my hands-on journey through modern data and ML engineering practices. What drives me: The intersection of reliability engineering and data systems. Whether it's ensuring power semiconductors survive extreme conditions or building ML pipelines that perform consistently in production, I'm passionate about systems that work when they need to.
Total Score: 152
Homework submissions
Homework 1: Introduction to Machine Learning
Score: 8 = 7 (questions) + 0 (FAQ) + 1 (learning in public)
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Homework 2: Machine Learning for Regression
Score: 6 = 5 (questions) + 0 (FAQ) + 1 (learning in public)
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Homework 3: Machine Learning for Classification
Score: 5 = 4 (questions) + 0 (FAQ) + 1 (learning in public)
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Homework 4: Evaluation Metrics for Classification
Score: 6 = 5 (questions) + 0 (FAQ) + 1 (learning in public)
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Homework 5: Deploying Machine Learning Models
Score: 4 = 3 (questions) + 0 (FAQ) + 1 (learning in public)
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Homework 6: Decision Trees and Ensemble Learning
Score: 6 = 5 (questions) + 0 (FAQ) + 1 (learning in public)
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Homework 8: Neural Networks and Deep Learning
Score: 8 = 6 (questions) + 0 (FAQ) + 2 (learning in public)
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Homework 9: Serverless Deep Learning
Score: 7 = 5 (questions) + 0 (FAQ) + 2 (learning in public)
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Homework 10: Kubernetes
Score: 9 = 6 (questions) + 1 (FAQ) + 2 (learning in public)
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Project submissions
Midterm Project
Project score: 14 Passed
Score: 27 = 14 (project) + 9 (peer review) + 4 (learning in public / project) + 0 (learning in public / peer review) + 0 (FAQ)
Project URL: View project
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- https://www.linkedin.com/posts/michaelaltamirano_github-highviewonemlzoomcampproject1-activity-7394595283353059328-3UmU?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAC1lLkBoRY9mHBr0ITSpcquTWA1jHSaql8
- https://bsky.app/profile/highview.bsky.social/post/3m5ig5voemc25
- https://www.threads.com/@highviewme/post/DQ_Bytfjcf65cu79JypV2yY7bMnp7HizYUzFBs0?xmt=AQF0bvmGx4yEfkPsrsd5zqZts_Ro_j4WgNWLmK2_glTQaA
- https://datatalks-club.slack.com/archives/C0288NJ5XSA/p1763013169879869
Capstone 1
Project score: 16 Passed
Score: 33 = 16 (project) + 9 (peer review) + 2 (learning in public / project) + 6 (learning in public / peer review) + 0 (FAQ)
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Capstone 2
Project score: 16 Passed
Score: 33 = 16 (project) + 9 (peer review) + 2 (learning in public / project) + 6 (learning in public / peer review) + 0 (FAQ)
Project URL: View project
Learning in public links: Show