Banias Baabe’s Post

In the coming days, I will be uploading a presentation (and, of course, the code) on how to efficiently design machine learning models, considering their resource consumption requirements in order to create an efficient system using W&B (experiment tracking) and MLflow (from the perspective of Pipelines). Stay tuned for more updates on my GitHub.

Scikit-learn has a set_output API where we can get outputs as a Polars DataFrame instead of a Numpy array In this example we change the output from the StandardScaler to Polars. We can also do this for ColumnTransformers and whole Pipelines (as we'll soon see!)

Get ready to level up your MLOps skills! In this live workshop session, we'll dive into crafting modular code for your machine learning projects. Learn how to build reusable components, streamline your development process, and make your ML systems more maintainable and scalable. Session Recording and Source code available at: https://lnkd.in/gtHcseix What You'll Do: Master the principles of modular code design for ML projects. Build and integrate modular components into a real-world ML pipeline. Get expert tips on effective code organization and refactoring.

I just published Scikit-learn Tutorial: Exploring Popular Datasets

Dive into the performance benefits of NumPy with Intel’s own Bob Chesebrough. You can speed up Pandas natively by leveraging NumPy powered by oneAPI, to solve Pandas apply performance issues. Find out how right here. https://intel.ly/3I7rU8X #Developer #oneAPI #Training

Of course you can integrate outlier detection into your scikit-learn classification pipeline! Just pip install scikit-fallback==0.1.0rc0 🧐 ✍️ Overview: https://lnkd.in/dVDHCrvD 👩💻 Code: https://lnkd.in/dN4baKh9

YOLO inference with Docker via API

Hey Connections! 👋 🔔 Day 13 of my Data Structures & Algorithms (DSA) in C journey, and today, we’re diving into something fundamental but incredibly powerful: Stacks! 🚀 Imagine a structure where the last thing you put in is the first thing you take out — that’s a Stack for you! 🌟 Whether you're building algorithms, parsing expressions, or managing function calls, the stack is your go-to tool! Think of it as a plate of dishes 🥞 — you can only take from the top, and you can only add on top! What makes stacks so special? 👉 LIFO (Last In, First Out) — Simple but mighty! 👉 Fast operations with only two main actions: Push and Pop! 👉 Essential for handling recursion, backtracking, undo mechanisms, and so much more! 💡 🔧 Today’s hands-on practice was all about mastering how to: 1 . Push elements to the stack 🖇️ 2 . Pop elements off the stack 🛠️ 3 . Peek to see what’s on top without removing it 👀 And yes, making sure we don’t overflow! 💥 The best part? This simple data structure is the foundation of many complex algorithms. Stay tuned for more insightful learnings on this DSA journey! 🚀 #DataStructures #Algorithms #CProgramming #Stack #CodingJourney #TechLearning #LinkedInLearning #ProgrammingCommunity #CodeNewbie #TechWithPurpose #DSAChallenge #DailyCoding #StackOverflow #ProblemSolving

DAG integrity and coding conventions are critical. Join Austin Bennett from ChartBoost as he shares lessons on testing/verifying DAGs in GitHub workflows, unlocking efficiencies, and catching errors pre-deployment. Learn how Airflow & CI checks improve processes. #ApacheAirflow https://airflowsummit.org/