RT @Krishnasagrawal: 20 most common SQL Interview Questions. #sql

RT @Hesamation: 10 GitHub repos to sleep with as an ai engineer covering ML systems, Agents, RAG, MLOps:. 1. Machine Learning for Beginners….

RT @Mbadiwejesse: SQL

RT @Eyowhite3: SQL to Python.

RT @Hesamation: A 680-page interactive book on computer science algorithms. This might be the sickest book i’ve seen in a long time!. 22 ch….

RT @Krishnasagrawal: SQL Joins explained very well. #sql

RT @sevalla_hosting: Git basics

RT @sysxplore: 4 main types of databases

RT @sysxplore: 12 Git commands you must know

RT @Eyowhite3: 4 types of SQL Joins fully explained in colours.

RT @DataTalksClub: Struggling to scale semantic search from a toy dataset to billions of items?. 2-step workflow that keeps latency low and….

RT @tegmark: AI can't reason and this seminal paper proves it:.

Just completed Module 3 of the #MLOpsZoomcamp by.@DataTalksClub. I used @mage_ai to streamline the deployment and management of machine learning pipelines. #MLOps #MLflow #Deployment #Orchestration #Automation

With @mage_ai, you can handle the entire machine learning lifecycle including data preparation, training and deployment, versioning, CI/CD and model maintenance. In Module 3 of the #MLOpsZoomcamp by.@DataTalksClub, I will be applying Mage AI for orchestration #MachineLearning

RT @Python_Dv: SQL Joins Cheat Sheet!. #sql #database

In Module 3, I learnt about creating training pipeline by operationalizing machine learning models. This involves moving them from development to production to drive business value. Join #MLOpsZoomcamp and to learn more about model orchestration with @mage_ai

RT @omarsar0: The Illusion of Thinking in LLMs. Apple researchers discuss the strengths and limitations of reasoning models. Apparently, r….

RT @GaryMarcus: A computer scientist’s perspective on vibe coding:

The Machine Learning lifecycle refers to the multiple steps that are needed to build and maintain a machine learning model. In week 2 of.@DataTalksClub.MLOps Zoomcamp, I am learning about model management with offline and online experiment tracking.

MLflow is open-source platform that manages the end-to-end machine learning lifecycle. It provides tool to streamline & automate various stages of the ML process, from experimentation to deployment. Experiment tracking is important for Reproducibility, Organization & Optimization.