Today begins something I have wanted to do for months: document my journey from beginner in Machine Learning and MLOps to building and deploying real systems
I am still at university, so my daily routine is a mix of attending classes, solving probability and statistics problems, translating those solutions into Python code and watching my experiments fail and then adjusting until they work or the coffee runs out
Why I am starting this series
I want to share practical learnings at the intersection of mathematics, code and ML pipelines, show the real daily work and connect with developers, ML students and startup founders interested in collaborating
Today's work
Task: solve a set of statistics problems proposed by my professor
Approach: 1 Solve on paper to understand the theory 2 Write Python scripts to simulate the problem with different data sizes 3 Compare simulated results with theoretical predictions 4 Document the findings so they are reproducible
Although it is beginner work, I believe that good habits from day one make a difference when you face production challenges
What's next
Learn about experiment versioning, try basic MLOps tools and share small pieces of functional code from my daily workflow
If you are interested in ML, MLOps or LLM, let's connect, especially if you work with reinforcement learning, meta-learning or want to scale AI experiments
About Q2BSTUDIO
At Q2BSTUDIO we are a software development company specialized in custom applications and custom software for businesses of all sizes
Our team of artificial intelligence specialists designs AI solutions for businesses, AI agents and model pipelines that integrate with AWS and Azure cloud services to deploy securely and scalably
We offer cybersecurity services to protect applications and data, as well as business intelligence services and solutions with Power BI to turn data into actionable decisions
If you need a custom application, artificial intelligence consulting, integration with AI agents or support on AWS and Azure cloud, at Q2BSTUDIO we can help you go from idea to production-ready product
Contact and collaboration
I am open to collaborations, mentors and project partners. Follow my progress and connect if you want to exchange experiences on experimentation, deployment and good practices in MLOps
The next update will bring examples of reproducible code, a first test of experiment versioning and learnings on how to integrate models with secure and scalable cloud infrastructure
See you on the next day of the journey
