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Big wreck collects big names, ending day for several drivers
⏲ 0:38 👁 130K
THE RISE AND FALL OF VISION (Week in Comic Speculation)
⏲ 15 minutes 56 seconds 👁 701
GoCollect | My First Article on Star Wars Comic Book Picks is Now Live!
⏲ 14 minutes 14 seconds 👁 170
Alida makes crispy fried pork spring rolls with shrimp - and goes to the forest to collect firewood
⏲ 8:0 👁 125K
Comic Collecting Strategies for a HOT Market (This Week in Speculation)
⏲ 16 minutes 6 seconds 👁 937
The Wasp is Heating Up?!
⏲ 17 minutes 59 seconds 👁 736
DRC activists build an island while cleaning up Lake Kivu
⏲ 4:1 👁 300K
GoCollect VS Overstreet (This Week in Comic Speculation)
⏲ 15 minutes 11 seconds 👁 906
COLLECTING VS INVESTING (This Week in Comic Speculation)
⏲ 20 minutes 43 seconds 👁 1K
Session 17: Deployment of Machine Learning Models in Open RAN Networks
⏲ 4:9 👁 75K
Weekly Speculation Recap
⏲ 28 minutes 41 seconds 👁 1.2K
Weekly Comic Speculation
⏲ 13 minutes 20 seconds 👁 716
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_big-wreck-collects-big-names-ending-day-for-several-drivers.webp "Chandler Smith spins entering Turn 3 on the race restart in the final stage, collecting and ending the day for several drivers, including Ty Gibbs.")
_the-rise-and-fall-of-vision-week-in-comic-speculation.webp "GoCollect")
_gocollect-124-my-first-article-on-star-wars-comic-book-picks-is-now-live.webp "Action Figure Grader")
_alida-makes-crispy-fried-pork-spring-rolls-with-shrimp-and-goes-to-the-forest-to-collect-firewood.webp "❤️Thank you for watching the video ❤️<br/>Please LIKE, SHARE and SUBSCRIBE <br/>Have a nice day ❤️")
_comic-collecting-strategies-for-a-hot-market-this-week-in-speculation.webp "GoCollect")
_the-wasp-is-heating-up.webp "GoCollect")
_drc-activists-build-an-island-while-cleaning-up-lake-kivu.webp "Josaphat Rubenga and his fellow \")
_gocollect-vs-overstreet-this-week-in-comic-speculation.webp "GoCollect")
_collecting-vs-investing-this-week-in-comic-speculation.webp "GoCollect")
_session-17-deployment-of-machine-learning-models-in-open-ran-networks.webp "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 \")
_weekly-speculation-recap.webp "GoCollect")
_weekly-comic-speculation.webp "GoCollect")
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