Oh, I think very little (<1%) was focused on RDBMS. Most was focused on what the document model enables. Personally, I think there are great reasons to use RDBMS at times, and great reasons not to. It's this idea that RDBMS is always the right answer I find silly.
COMPUTING
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Dr. Evelyn Boyd Granville’s 99th Birthday and Historic Math Achievement
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Happy 99th birthday to Dr. Evelyn Boyd Granville. Born #OTD in 1924, she became the second Black American woman to earn a doctorate in math when she graduated from #Yale in 1949. While at #IBM, she created computer software used in the Apollo space. #stem #nasa #women @nasa #iot
→ View original post on X — @2morrowknight, 2023-05-01 16:29 UTC
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Database Types and Their Varying Data Handling Capabilities
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I was just looking at old decks and Jared's use of that image was all over them. It *is* true, if perhaps overstated. I think we can agree that different types of databases handle different kinds of data better (or worse).
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Autoscaling compute clusters for parallel job execution
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5) Enabling autoscaling of the compute cluster to execute more jobs in parallel during periods of high activity and save cost during periods of low activity.
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Containerization and Kubernetes scaling for ML workloads
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1) Containerization provides consistent, reproducible behavior required for ML workloads.
2) Scaling gets first-class support in Kubernetes, unlocking scaling for training and experimentation. -
Ray Parallelization and Job Checkpointing for Distributed AI
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3) Leveraging Ray to parallelize work at large scale across multiple worker pods in the cluster to achieve performance benchmarks
4) Implementing job checkpointing ensures that jobs always run to completion and users see minimal interruption. -
Kubernetes Lessons Learned Running ML at Snorkel
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Snorkel tackles heavy ML workloads for some of the world's largest organizations. We have built our infrastructure on Kubernetes—even though it wasn't designed for ML! Here are a few lessons learned from @wheeliamhuang
, Tech Lead Manager at Snorkel https://
snorkel.ai/kubernetes-les
sons-learned-at-snorkel-ai/
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MetaSpore: Algorithmic Application Framework for Big Data
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MetaSpore: Algorith. App Framework! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/MetaSpore -
Azure SQL Database Administration and Cloud Computing Technologies
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#AzureSQLDatabase Administration! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/AzureSQLDB -
Essential Programming Books for Data Science and Cloud Computing
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#Programming #Books You Should Read. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Programming-Bo
oks-Read
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