What is CoreML?

Roboflow · Beginner ·👁️ Computer Vision ·5y ago
Skills: CV Basics80%

Key Takeaways

The video introduces CoreML, Apple's framework for on-device machine learning inference, and explains how to create and deploy CoreML models using tools like Create ML, TensorFlow, and PyTorch.

Full Transcript

[Music] hi everyone welcome back to ml in a minute i'm kaylee here at roboflow here today with joseph ceo of roboflow so today let's talk about core ml can you talk a little bit about what core ml is yeah uh so core ml is apple's framework for doing on-device inference and so when you're doing on-device inference you want to be especially considerate of creating a model that is small low latency and uses low power consumption and so core ml allows you to easily have a model file.ml model file in the apple ecosystem to deploy two iphones and apple devices that makes use of the neural engine so that the models run faster with lower power consumption you can create a core ml model using some a tool like create ml you can also import your h5 file from tensorflow or your dot pt file from pytorch so basically it's an easy way to get up and going in the apple ecosystem for on-device machine learning awesome well that's been another ml in a minute if you like this video and you want more like it please be sure to subscribe and we'll keep you in the loop see you on the next one

Original Description

CoreML is Apple's framework for doing on-device machine learning inference. Roboflow's "Machine Learning in a Minute" series gives you quick bites of machine learning information day by day. Subscribe for more! Roboflow is the easiest way to get started with Computer Vision, even if you're not a Machine Learning expert. Check out our tutorials and get started for free! https://roboflow.com
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CoreML is a framework for on-device machine learning inference that allows developers to create and deploy models on Apple devices. The video explains how to get started with CoreML and deploy models using tools like Create ML, TensorFlow, and PyTorch.

Key Takeaways
  1. Create a CoreML model using Create ML
  2. Import a model from TensorFlow or PyTorch
  3. Optimize the model for on-device inference
  4. Deploy the model on an Apple device
  5. Use the neural engine to improve performance
💡 CoreML allows developers to easily deploy machine learning models on Apple devices, taking advantage of the neural engine for improved performance and low power consumption.

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