MLflow - Part 1 - Fundamentals Introduction MLfLow is an open-source platform for managing workflows and artifacts in the entire machine learning lifecycle. Table of content Example Installation Example In this example a ML project is going to be created using the benefits of MLflow. Installation of MLflow Create conda environment conda create --name mlflow_fundamentals Actvate environment conda activate mlflow_fundamentals Install Mlflow conda install -c conda-forge mlflow Example import mlflow from sklearn.

Understanding Softmax Activation Function: A Key Element in Machine Learning The softmax activation function is a crucial element in the field of machine learning, particularly in the realm of multi-class classification problems. It plays a vital role in converting raw scores or logits into probabilities, enabling us to make informed decisions and predictions. In this article, we will dive deep into the concept of softmax, its mathematical representation, and how to implement it using Python.

jupyter nbconvert --to markdown notebook.ipynb

Command to convert to markdown and post.

Build a golang project using AWS CodeBuild DevOps has been useful almost required to speed up all stages in life cycle software. Following our path in AWS in this post we are going to review AWS CodeBuild. AWS CodeBuild is a managed service to comp ile, test and package software. Benefits of AWS CodeBuild is that as is managed service it scales well and process multiple builds concurrently. This post we are going to use it to create a pipeline with a golang project.

API Lifecycle Management is the process that begins with strategy design and ends when the API is retired. The API lifecycle must to be thinking to attend quickly the market changes, business requirements, take care of different stages and stakeholders who are involved in this process.

The goal in this post is to compare the stages managed by different API Managers (vendors)