Sciblog - A blog designed like a scientific paper


Many people trying to switch their careers to Data Science can't land a job even if they spend tons of time and money on AI training. Why? Because most of the training teaches the AI used 20 years ago instead of the AI that companies use today. Data Science teams want candidates who can contribute to their business from day one. To successfully transition to a Data Science career you need to prioritize the AI that is useful for companies. That's the basis of Reverse Learning.


Is it possible to switch your career from Cloud Solution Architect to Data Scientist? Luis Valencia was able to secure an AI job in just 4 months following my mentoring strategy: Reverse Learning. He is currently the Lead Data Scientist in his company working on large language models. This is his story.


As a mentor of Nikolaos, my primary goal was to guide him toward securing a job in the field of data science. We developed a plan of action that involved improving his resume, LinkedIn, and GitHub profiles, theoretical and practical knowledge, and training on how to approach an interview. Nikolaos was able to get a Data Science position in less than 3 months. In this post, I share his story.


Machine Learning Mastery for Career Growth at Top-Tech Corporations

Feb. 14, 2023

Aishwarya Naresh Reganti and Miguel González-Fierro


In this interview hosted by The LevelUp Org I provide my perspective on having an exciting career if you are interested in machine learning. Many of the ideas come from what I have observed in academia, the startup world, and big tech. I hope this is useful to people starting their careers and looking for direction.


Start with Transformers

Feb. 2, 2023

Neil Leiser and Miguel González-Fierro


In this conversation with Neil Leiser, we first dig into my beginning in robotics and the connection between robotics and AI. Then I share a few stories on how to learn AI faster, how to switch fields, taking initiatives, the importance of diversity, and the power of practical knowledge. We finally talk about Data Science at Microsoft where we talk about my career path and the learnings I got from some of the best in the field.


My Most Popular LinkedIn Posts Of 2022

Jan. 2, 2023

Miguel González-Fierro


In 2022 around 11 million people saw my posts on LinkedIn. I've been posting to help people understand and apply AI. In particular, I focused on how people can switch their careers to AI because that's the journey I followed. Here are some of the most popular posts.


Building Recommender Systems

Nov. 26, 2022

Seth Juarez and Miguel González-Fierro


Recommendation systems are one of the most exciting AI solutions available today. They are information filters that learn users' behavior based on their historical interactions with items, and then predict their preferences for a given item. In this post, we make an overview of Recommenders, the open-source repository that helps Data Scientists build recommendation systems.


Whether you are interested in changing your career to an AI position or you want to improve your skills as a Data Scientist, it is key to know how to effectively create machine learning projects. In this post, I show how to start a machine learning project, by combining two of the most useful tools we have at our disposal, Jupyter notebooks and Python libraries. See the full video to discover how I create projects in my job at Microsoft.


D4 Data Podcast with Miguel Fierro - Recommendation Systems

Oct. 1, 2022

Deepak JR and Miguel González-Fierro


Deepak JR interviewed me for his podcast D4 Data Podcast where we discussed everything about recommendation systems. Recommendation systems are one of the most impactful machine learning systems in production in the world. It is not a surprise that they are one of the most complex and demanding solutions, with multiple moving parts that need to be tuned in the right way to improve the user experience and increase revenue.


An Analysis of the Adoption of Top Deep Learning Frameworks

July 27, 2022

Miguel González-Fierro


There are a lot of people comparing deep learning frameworks in terms of speed or performance, but I haven't seen anyone studying their adoption over time and analyzing the underlying reasons why some attracted more users than others. In this post, I share what I have observed since 2015, and based on historical data, I provide guidance on one of the top questions I get: what deep learning framework should I use?