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Balitanghali is the daily noontime newscast of GTV anchored by Raffy Tima and Connie Sison. It airs Mondays to Fridays at 10:00 AM (PHL Time). For more videos from Balitanghali, visit http://www.gmanews.tv/balitanghali.<br/><br/>#GMAIntegratedNews #KapusoStream<br/><br/>Breaking news and stories from the Philippines and abroad:<br/>GMA Integrated News Portal: http://www.gmanews.tv<br/>Facebook: http://www.facebook.com/gmanews<br/>TikTok: https://www.tiktok.com/@gmanews<br/>Twitter: http://www.twitter.com/gmanews<br/>Instagram: http://www.instagram.com/gmanews<br/><br/>GMA Network Kapuso programs on GMA Pinoy TV: https://gmapinoytv.com/subscribe
⏲ 1:34 👁 275K
Geo News
⏲ 37 minutes 9 seconds 👁 16.9K
Sangeeta
⏲ 4 minutes 40 seconds 👁 530.7K
In a stunning turn of events, the election result trends in West Bengal send shockwaves through the BJP camp, as the anticipated \
⏲ 3:43 👁 100K
KL BRO Biju Rithvik
⏲ 1 minute 👁 23.1M
Buzz of Kolkata
⏲ 11 seconds 👁 159
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 \
⏲ 4:9 👁 75K
Eagle Movies
⏲ 4 minutes 16 seconds 👁 9M
Star India 1
⏲ 28 seconds 👁 27.8M
Edin Terzic shared his admiration for Real Madrid boss Carlo Ancelotti ahead of the Champions League final.
⏲ 0:33 👁 65K
Sangeeta Music
⏲ 5 minutes 41 seconds 👁 10.3M
Divya Lifestyle
⏲ 3 minutes 17 seconds 👁 47.6K
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