Louis M

Louis is a seasoned technology executive with over 20+ years of experience in leading and managing teams, building products, and implementing open-source technologies. He is certified in building Crypto currencies applications and has a proven track record of effectively leading teams of over 100+ individuals, including experience leading through a management layer (manager of managers). Louis possesses vital emotional intelligence, allowing him to lead a diverse group of engineers and managers at varying stages of their careers. He has experience leading teams through complex compliance certifications, such as FedRAMP, PCI, SOC2, ISMAP & HIPAA. He has 10+ years of security experience dealing with web systems, Cloud, SaaS, compliance, corp IT, infrastructure, and embedded devices. Louis has a solid understanding of cross-functional engineering organizations and significant experience building diverse and inclusive teams. He has superb communication skills and can articulate a compelling security vision. Most recently, Louis served as the CTO of Metabolicliving, where he led the engineering team to become a high-performing, on-time delivery cross-functional team and collaborated with Executive Leadership to develop & manage product strategy, roadmaps, and technical execution plans. Before that, he served as the CTO of Burnalong, a health and fitness tech startup. He single-handedly built the AWS infrastructure, on-site cross-functional engineering team, remote developers, and QA. He also created an internship program to cultivate new talent and grow the technical organization.

Dealing with Inaccurate Predictions in Machine Learning: A Practical Guide

Dealing with Inaccurate Predictions in Machine Learning: A Practical Guide

Discover strategies to improve and fine-tune machine learning models, addressing the challenges of inaccurate predictions in this practical guide. Unveiling the

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Machine Learning: Handling Data Leakage and Other Issues

Machine Learning: Handling Data Leakage and Other Issues

Discover the challenges of machine learning, including data leakage and overfitting. Learn strategies to handle these issues and create reliable models.

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Machine Learning Model Debugging: Techniques and Tips

Machine Learning Model Debugging: Techniques and Tips

Learn effective techniques and tips for debugging machine learning models, from understanding their importance to leveraging visualization tools. By Louis M.

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Why Machine Learning Models Fail: A Deep Dive into Model Assumptions

Why Machine Learning Models Fail: A Deep Dive into Model Assumptions

Discover the Common Pitfalls of Machine Learning Models and Learn How to Overcome Them. Improve Accuracy and Fairness for Successful Models. Read Now!

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