#DataScience #MachineLearning #LinearRegression #R2 #PValue #StatisticalAnalysis #ModelEvaluation #AIInsights #PredictiveAnalytics #DataAnalytics #AI #DeepLearning #DataDriven #DataVisualization #BigData #MLModel #Statistics #AIAdvancements

🚨 New app drop!
MagicaLCore for iPad lets you create image classifiers using your own dataset — no coding required.
• Build & test CoreML models on-device
• Export .mlmodel in seconds
• Free to try, with 3-day trial

🔗 apps.apple.com/app/id646644...

#indieDev #CoreML #AI #NoCode

Since 2023, 464 iOS apps shipped with .mlmodel files (of the ones we tracked)

⚖️ Average model size: 20 MB
💥 Largest size: 524 MB
#️⃣ Most apps had 1 .mlmodel per app, but some had more (up to 51!)

We fetch the bundled model and load it as a MLModel. Then, to enable it to work with the vision framework, we load this as a VNCoreMLModel.
This is important because of the model inputs I mentioned before. The CLIP model only accepts inputs of a specific image resolution and colour space.

Strategic pruning and quantization shrink a 7‑billion‑parameter LLAVA model to run within 4 GB VRAM, cutting memory use by ~70% and delivering a 4% performance gain. Read more: https://getnews.me/efficient-compression-techniques-boost-medical-multimodal-llms/ #mlmodel #compression

The most common ML models used were:

- FindFour.mlmodel (used in 49 apps)
- FourRecognize.mlmodel (49)
- SSDOcr.mlmodel (41)
- DeepLabV3.mlmodel (15)

These are typically used for things like recognizing patterns, scanning documents, and object removal or background editing 🧠

HiPhO, a new benchmark uses 13 physics Olympiad exams from 2024‑2025 to compare 30 multimodal LLMs with human contestants; closed‑source reasoning models earned 6‑12 gold medals. Read more: https://getnews.me/hipho-benchmark-shows-m-llms-vs-human-physics-winners/ #hipho #physicsolympiad #mlmodel

For the past few years, Emerge has tracked thousands of mobile apps to understand their size changes.

We were curious about trends in .mlmodel files, so we pulled some data and this is what we found 🧵

My new course is coming along nicely!

Python + scikit-learn + pandas to train the model. Convert to .mlmodel using coremltools, host it on Vapor and then interact with the model through JSON API through a SwiftUI app.

😎

Hoping to launch next week.

#iosdev #swiftui #MachineLearning

🐍Python libraries that implement agnostic global explainability methods 👇 

#python #machinelearning #MLModel #datascience #dataengineering

📊🤖 Finished building my first linear regression model to predict NY Taxi ride durations using the January-February 2023 dataset. Next step: tuning the model and deploying with Docker. Let's do this! #MLModel #DataScience #AI #ZoomCamp #MLOps #DataTalksClub

The app with the largest amount of .mlmodel files is the Filmmaker Pro app with 524 MB of ml model files 🫨

The entire app itself is 1 GB so we'll have to save that breakdown for a thread of its own 🫣

AndroidにMLModelのBindingの設定とかあるのか
```
buildFeatures {
mlModelBinding = true
}
```

🚗 My new ML course is coming together! You'll learn to train a car price prediction model with scikit-learn, convert it to .mlmodel using coremltools, host it on a Vapor server, and connect it to an iOS app via a JSON API.