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_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 \")
_alfez-khaishagi-is-jenna-now-splitsvilla-12-contestant-alfez-khaishagi-shocking-transformation.webp "Reality Shows Updates")
⏲ 2 minutes 13 seconds 👁 323.4K
_happy-holi-tappu-amp-nandini124-love-status-video-shorts-124-tappuparmar-status-love-shorts-short.webp "Tappu Honey Parmar")
⏲ 16 seconds 👁 2M
_fashion-show.webp "This weekend its time to catch up on the latest fashion trends. You've been putting it off all week but finally its the weekend and you have the time to watch these Lingerie Fashion Shows. Here's a shuffle play of the most exciting and interesting lingerie fashion shows on this list. Enjoy!<br/><br/>Do you like music?<br/> SHIFT RADIO... TURN IT ON!<br/>https://shiftswim.com/radio<br/><br/>Tell us who your favorite model is in the comments!<br/><br/>We really want to hear from you.<br/>Love,<br/>David and Yulia<br/><br/>Don't forget to LIKEand SUBSCRIBE !<br/><br/>#shift #bikini #watchparty #swimweek #miamiswimweek #nyswimweek")
_rajnandini-metal-share-bonus-news-rajnandini-metal-share-analysis-wire-ampcable-sector-pennystocks.webp "Online BSE")
⏲ 3 minutes 45 seconds 👁 3.3K
_wh3r3-are-they-now-after-4-years-rajnandiniarchi-kavya-isha-amp-martina.webp "The News Bee")
⏲ 5 minutes 18 seconds 👁 60K
_ancelotti-39a-role-model-for-every-young-coach39-terzic.webp "Edin Terzic shared his admiration for Real Madrid boss Carlo Ancelotti ahead of the Champions League final.")
_bjp-election.webp "Mera Safar")
⏲ 9 minutes 22 seconds 👁 127.9K
_nije-sve-u-lovi-124-not-all-about-the-money.webp "Nije sve u lovi | Not All About the Money from rajnaodni model")
_review-on-the-macbook-air-m3-models.webp "Tom's Guide reviews the 13- and 15-inch MacBook Air laptops with the latest Apple M3 processor.")
_hitep7-spoiled-by-my-disabled-ceo-drama-miniseries-minidrama-revenge-minidrama-relationship.webp "HITEP7 Spoiled By My Disabled CEO drama miniseries minidrama revenge minidrama relationship")
⏲ 0:59 👁 50K
_seymour-duncan-78-model-humbucker-review.webp "As Guitar World Tech Editor Paul Riario explains, “In the late ‘70s, Seymour Duncan – the man – was well-known as the guy to custom-wind pickups for some noteworthy guitarists who were looking to venture beyond what was available and create their own identifiable tone and sound.\")
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