Introduction to Machine Learning in Kaggle

I’m going through the course “Intro to Machine Learning“, and I would like to keep some notes about it. My first machine learning code Model validation In almost all applications, the relevant measure of model quality is predictive accuracy. In other words, will the model’s predictions be close to what actually happens. Mean Absolute Error (MAE) … Read more

Why is important DevOps for an organization that wants to implement Machine Learning initiatives?

Machine Learning projects come with a lot of complexity in terms of organization and this type of projects are not just to be a challenge for the consumption of infrastructure services, but it’s going to be a challenge to the ability of the organization to deliver in a reasonable speed. Let’s start with some basis … Read more

What is Machine Learning Operations

If you are reading this and you are not me, you should navigate to this link: There are so many basis and nice charts that explain What is Machine Learning Operations (or ml-ops). I’m doing a summary for me 🙂 Definition of MLOps The term Machine Learning Operations is defined as “the extension of the … Read more

Year 4, Q2 Machine Learning on kaggle

During Q1 one of the topics have been to learn about Artificial Intelligence, and after reading and listen several podcasts, I have decided that I would like to learn about Machine learning using Kaggle environment: python, jupyter notebooks, examples, a large community, competitions…. I have already completed the python course to refresh my knowledge and … Read more

Machine Learning: map and players

This post is a mix review of Machine Learning type of solutions the market offers, and a quick review of some players I have in my mind. Machine Learning Wardley Map Components: Machine Learning can be used by companies and individuals. The B2B and B2C is important when you look at the perspective that many … Read more

Machine Learning Planning and architectures

There are multiple types of projects on machine learning, so the phases and steps are different. I will try to reduce to some basic type of projects. Basic project plans (main phases) Machine learning solution based on a Product Technology assessment = 2 – 3 days. Production trial = 8 – 12 days. Application deployment … Read more

Tuning a Machine Learning Model

I continue taking some basic notes of the book “Enterprise Artificial Intelligence and Machine Learning for Managers“. Tuning a machine learning model is an iterative process. Data scientists typically run numerous experiments to train and evaluate models, trying out different features, different loss functions, different AI/ML models, and adjusting model parameters and hyper-parameters. Feature engineering … Read more