Codetown ::: a software developer's community
Time: July 11, 2012 from 6pm to 8pm
Location: International Academy of Design & Technology (IADT) - Tampa
Street: 5104 Eisenhower Blvd.
City/Town: Tampa
Website or Map: http://www.meetup.com/BarCamp…
Event Type: meetup
Organized By: Adobe
Latest Activity: Jul 11, 2012
Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.
Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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This year’s QCon London brought a wealth of talks directly or indirectly related to software architecture, ranging from the rise of AI to more established areas like anything cloud-related to the usual classics like architecture quality traits . The conference also featured many talks about sociotechnical aspects of software architecture and engineering and broadly considered sustainability.
By Rafal GancarzIn a talk at QCon London 2024 titled "Curating the Developer Experience," Andy Burgin discussed embracing Developer Experience (DevEx) as an operational philosophy at the betting company Flutter. Recognising the potential of DevEx to enhance productivity and foster collaboration and empathy between teams, Burgin explained how Flutter implemented and evolved their Developer Experience.
By Matt SaundersAccording to Camilla Montonen, the challenges of building machine learning systems are mostly creating and maintaining the model. MLOps platforms and solutions contain components needed to build machine systems. MLOps is not about the tools; it is a culture and a set of practices. Montonen suggests that we should bridge the divide between practices of data science and machine learning engineering.
By Ben LindersThis insightful InfoQ article dispels the common myths surrounding Lambda Cold Starts, a widely discussed topic in the serverless computing community. As serverless architectures continue to gain popularity, misconceptions about Lambda Cold Starts have proliferated, often leading to confusion and misguided optimization strategies.
By Mohit PalriwalJules Damji discusses which infrastructure should be used for distributed fine-tuning and training, how to scale ML workloads, how to accommodate large models, and how can CPUs and GPUs be utilized?
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