NeuralFlix

Performant and Cost-Effective Machine Learning At Scale

Presenter: Jay Marshall & John Furrier

Join theCUBE Host John Furrier as he discusses neural networks with Jay Marshall, VP of Business Development, Neural Magic.

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Deploy Fast and Accurate YOLOv8 Object Detection Models on CPUs You Already Have
Unlock Faster and More Efficient Language Models with SparseGPT
Pruning and Quantizing ML Models With One Shot Without Retraining
Sparse Transferring Hugging Face Models With SparseML
Apply Second-Order Pruning Algorithms for SOTA Model Compression
Performant and Cost-Effective Machine Learning At Scale
Sparse Training of Neural Networks Using AC/DC
How Well Do Sparse Models Transfer?
How to Achieve the Fastest CPU Inference Performance for Object Detection YOLO Models
Workshop: How to Optimize Deep Learning Models for Production
How to Compress Your BERT NLP Models For Very Efficient Inference
Sparsifying YOLOv5 for 10x Better Performance, 12x Smaller File Size, and Cheaper Deployment
Tissue vs. Silicon: The Future of Deep Learning Hardware
YOLOv5 on CPUs: Sparsifying to Achieve GPU-Level Performance and Tiny Footprint
YOLOv3 on the Edge: DeepSparse Engine vs. PyTorch
State-of-the-Art NLP Compression Research in Action: Understanding Crypto Sentiment
3.5x Faster NLP BERT Using a Sparsity-Aware Inference Engine on AMD Milan-X
Pruning Deep Learning Models for Success in Production
Accelerate NLP Tasks With Sparsity and the DeepSparse Runtime
Accelerate Image Classification Tasks With Sparsity and the DeepSparse Runtime
Accelerate Image Segmentation Tasks With Sparsity and the DeepSparse Runtime
Accelerate Object Detection Tasks With Sparsity and the DeepSparse Runtime
Intro to SparseZoo
Intro to SparseML
Intro to DeepSparse Runtime
Intro to Neural Magic & Software-Delivered AI
Intro to Deep Learning Model Sparsification

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Performant and Cost-Effective Machine Learning At Scale