Inference and Quantization for AI - Session 3
Explore quantized inference, TensorRT 5, and TensorFlow integration for AI model optimization. Learn about NVIDIA's inference server and techniques to improve model efficiency and performance.
On-Device Speech Models Optimization and Deployment for Mobile Hardware
Explore on-device speech model optimization and deployment, covering streaming-aware design, quantization techniques, and benchmarks for popular speech processing model topologies on mobile platforms.
Build Your Own AI Assistant - Object Classification with Python
Learn to build an AI assistant using Python, TensorFlow, and Google Colab for object classification. Covers neural networks, image processing, and hands-on demos for practical implementation.
TinyML Vision Challenge - Summit 2021
Discover the tinyML Vision Challenge: Create innovative computer vision applications using tinyML, compete for $6k in prizes, and gain recognition from the tinyML Foundation.
Quick to Production: Integrating Spark and TensorFlow for Efficient MLOps
Learn to quickly deploy deep learning models using Spark and TensorFlow, simplifying MLOps for data scientists. Explore distributed computing, feature engineering, and leveraging TensorFlow ecosystem libraries for efficient model production.
Conversational AI with Transformer Models - Building Blocks and Optimization Techniques
Explore building conversational AI agents using transformer models, covering advantages over RNN/LSTM, knowledge distillation, and model compression techniques for efficient production deployment.
Making the Most Out of Hardware Accelerators in Kubernetes Clusters
Optimize GPU and TPU usage in Kubernetes clusters for MLOps. Learn efficient hardware accelerator management, scaling, and time-sharing techniques to enhance machine learning workloads.
The State and Future of Cloud-Native Model Serving
Explore KServe's progress, latest developments, and future roadmap for cloud-native ML model serving. Learn about Kubernetes design patterns and CNCF ecosystem integration for production model deployment.
Building AutoML Pipelines with Argo Workflows and Katib
Explore AutoML pipelines using Argo Workflows and Katib for efficient model selection and hyperparameter tuning in Kubernetes environments, enhancing MLOps processes.
End-to-End AI Developer Journey with Containerized Assets Using Intel DevCatalog and DevCloud
Explore containerized AI development using Intel DevCatalog and DevCloud. Learn to access, customize, and deploy AI assets on various Intel hardware, leveraging OpenVINO for training and inference.
Running Distributed TensorFlow on DC/OS
Learn to run distributed TensorFlow on DC/OS, addressing challenges in large-scale machine learning. Explore framework design, live demo, and practical implementation for efficient model training on your infrastructure.
Distributed Deep Learning on Apache Mesos with GPUs and Gang Scheduling
Explore distributed deep learning on Apache Mesos, leveraging GPU clusters for efficient model training in self-driving cars, trip forecasting, and fraud detection at UBER.
Quantum Machine Learning 101: An Introduction for Beginners with TensorFlow
Explore quantum machine learning basics and implement simple algorithms using TensorFlow. Gain hands-on experience in this emerging field at the intersection of quantum computing and AI.
Introduction to KubeFlow: Using and Use Cases
Explore KubeFlow for machine learning on Kubernetes, covering components, use cases, and a practical recommender system example with collaborative filtering and TensorFlow.
Minimizing GPU Cost for Deep Learning on Kubernetes
Learn to optimize GPU usage for deep learning on Kubernetes, reducing costs by up to 40%. Explore GPU sharing solutions, API design, scheduling, and isolation techniques with practical Tensorflow examples.
Large Scale Distributed Deep Learning on Kubernetes Clusters
Explore distributed deep learning deployment on Kubernetes, comparing tf-operator and mpi-operator for TensorFlow training jobs, and optimizing resource utilization for better economics in machine learning tasks.
AI Landmark Recognition With Tensorflow Lite and CameraX on Android
Integrate Tensorflow Lite model for landmark recognition in Android using CameraX library. Learn professional techniques for AI-powered camera applications.
Doubling Stable Diffusion Inference Speed with RTX Acceleration and TensorRT - A Comprehensive Guide
Comprehensive guide on boosting Stable Diffusion performance with RTX Acceleration TensorRT, covering installation, optimization, and troubleshooting for various models and extensions.
Building Your Own AI Platform and Tools Using ChatGPT
Explore creating custom AI platforms using TensorFlow, covering data collection, cleaning, training, and building a CLI-based system tailored to your organization's environment.
Object Classification Using VGG16 and XGBoost for Vehicle Classification with TensorFlow
Learn to classify vehicles using VGG16 and XGBoost in TensorFlow. Covers data preparation, feature extraction, model training, and testing. Gain practical skills in object classification and deep learning.
Creating Keras 3 - An Interview with François Chollet
Explore Keras 3 creation, AI research, and AGI with François Chollet. Gain insights on intelligence, machine learning advancements, and the future of AI in this engaging discussion.