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GSA Degree Show 2024 runs from 31 May to 9 June across the GSA Glasgow Campus in Garnethill. The GSA’s Master of Fine Art Degree Show opens on 30 May – 9th June at Glue Factory.<br/>The Glasgow School of Art (GSA) Degree Show 2024 opens to the public on Friday 31 May where a new generation of students will showcase their innovation, creativity and energy in what is Glasgow’s largest public exhibition of work. The physical show will run alongside an expansive digital showcase, which launches live on 30 May.<br/>Over 600 students from across the GSA’s four schools – fine art, design, architecture and innovation and technology will exhibit work that addresses the contemporary landscape they will face as they start their creative careers - from AI, web3, identity and representation, the climate emergency to health and wellbeing.<br/>The show not only celebrates the achievements of the graduates but also emphasises the importance of creativity and innovation in addressing current societal challenges. From product design engineering and interaction design to painting and printmaking, the range of<br/>projects on display highlights the students' ability to tackle complex issues and pushboundaries across disciplines.<br/>This year’s Degree Show campaign features Spider Lily, a font made by Year 4 Communication Design graduating student Nikos Ho.<br/>Nikos’ font, the ethereal and fragile Spider Lily is based on his observations of forms from the Singapore cityscape. Introduced to the island as an ornamental plant, the Spider Lily found an ideal habitat in Singapore’s tropical climate and it has embraced for its beauty in the city’s urban landscape. The naturalisation of the spider lily in Singapore mirrors the diverse migrant population that has settled on the island, integrating into its social fabric, enriching the island’s landscape and identity.<br/>Professor Penny Macbeth, Director of The Glasgow School of Art said:<br/>\

Glasgow School of Art Degree Show 2024

⏲ 5:29 👁 39.8M

Ingram Micro Cloud ANZ

Autotask PSA Integration Demo

⏲ 15 minutes 26 seconds 👁 808

Sophos MSP Global

Sophos Central - Autotask PSA Integration

⏲ 9 minutes 19 seconds 👁 484

Credit: SWNS / Simone White<br/><br/>A mum has been left with a brain injury after a suspected spiking incident - and now struggles to walk and talk.<br/><br/>Simone White, 43, was out with friends at a bottomless brunch in Bristol when she started to feel \

Mum left with brain injury after suspected SPIKING at bottomless brunch

⏲ 4:15 👁 245K

CyberQP

Datto Autotask Integration

⏲ 1 minute 6 seconds 👁 316

Capterra

Autotask PSA Review: Solid solution with lots of integrations

⏲ 3 minutes 13 seconds 👁 46

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

Datto, Inc.

Autotask PSA Demo

⏲ 52 minutes 11 seconds 👁 21.3K

Datto, Inc.

Datto RMM & Autotask PSA Overview

⏲ 10 minutes 48 seconds 👁 10.7K

The Rise Of Sustainable Brands

⏲ 29:50 👁 11.5M

Chris Timm - Sondela Consulting

How to use Teams in Autotask

⏲ 3 minutes 5 seconds 👁 3.2K

Bardeen

Automating Data Entry (Full Tutorial)

⏲ 30 minutes 57 seconds 👁 11.5K

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