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Stanley Cup Finals Stir Hostility Among Canadian Fans from fl library

Stanley Cup Finals Stir Hostility Among Canadian Fans

⏲ 0:55 👁 8.8M

Mazin

Library Review - Orlando Public Library

⏲ 6 minutes 2 seconds 👁 271

Curbside Circus

Curbside Circus Visits the Orange County Library System | Orlando, FL | Kids Educational Videos

⏲ 16 minutes 28 seconds 👁 86

During a civil war between humans and androids, In the year 2090, a robotics engineer tries to bring her fallen lover back to life.<br/><br/>\

Sci-Fi Short Film \

⏲ 9:51 👁 135K

Collier Television

New Self-Check System at Collier County Public Library, Naples FL

⏲ 1 minute 26 seconds 👁 198

Alex Moukala Tutorials

Why You Shouldn't Use Free Orchestral Libraries (Edirol Orchestra Etc.)

⏲ 12 minutes 55 seconds 👁 76.3K

Welcome back to our journey through the world of Open RAN and machine learning. In this session, In this session, we'll explore the deployment of machine learning models in Open RAN networks, focusing on practical examples and deployment strategies.<br/><br/>Deployment Example:<br/>Consider a scenario where an Open RAN operator wants to optimize resource allocation by predicting network congestion. They decide to deploy a machine learning model to predict congestion based on historical traffic data and network conditions.<br/><br/>Deployment Steps:<br/><br/>1. Data Collection and Preprocessing:<br/>The operator collects historical traffic data, including throughput, latency, and user traffic patterns.<br/>They preprocess the data to remove outliers and normalize features.<br/><br/>2. Model Development:<br/>Data scientists develop a machine learning model, such as a regression model, to predict congestion based on the collected data.<br/>They use a development environment with libraries like TensorFlow or scikit-learn for model development.<br/><br/>3. Offline Model Training and Validation (Loop 1):<br/>The model is trained on historical data using algorithms like linear regression or decision trees.<br/>Validation is done using a separate dataset to ensure the model's accuracy.<br/><br/>4. Online Model Deployment and Monitoring (Loop 2):<br/>Once validated, the model is deployed in the network's edge servers or cloud infrastructure.<br/>Real-time network data, such as current traffic conditions, is fed into the model for predictions.<br/>Model performance is monitored using metrics like prediction accuracy and latency.<br/><br/>5. Closed-Loop Automation (Loop 3):<br/>The model's predictions are used by the network's orchestration and automation tools to dynamically allocate resources.<br/>For example, if congestion is predicted in a certain area, the network can allocate additional resources or reroute traffic to avoid congestion.<br/><br/>Subscribe to \

Session 17: Deployment of Machine Learning Models in Open RAN Networks

⏲ 4:9 👁 75K

Collier Television

Author and Book Fest at Collier County Library, Naples FL

⏲ 1 minute 4 seconds 👁 31

CBS Sunday Morning

Beyond books: The 21st century public library

⏲ 7 minutes 4 seconds 👁 68.9K

The Many Names of Amerant Bank Arena: Six Names in 25 Years from fl library

The Many Names of Amerant Bank Arena: Six Names in 25 Years

⏲ 1:0 👁 65K

Production Den

How to Install KONTAKT LIBRARIES: Install Native and 3RD PARTY Kontakt Libraries

⏲ 5 minutes 59 seconds 👁 81.9K

Alex Moukala Tutorials

The Best FREE Orchestral VST Library Ever Made.

⏲ 4 minutes 40 seconds 👁 276.2K

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