Yun Sensei

RIP Ozzy Osbourne, the iconic Black Sabbath frontman who transformed heavy metal and inspired millions-true proof that ordinary beginnings can forge extraordinary legends. moneyweek.com/economy/people…

Palantir is soaring, but there may be bigger AI opportunities ahead! Discover why Nvidia, Alphabet, and ASML could offer more upside for smart investors. fool.com/investing/2025…

Intel posts better-than-expected revenue, but cuts jobs and scales back European projects amid AI chip industry shakeup. Rivals SK hynix and Nvidia thrive on soaring AI demand. ibtimes.com/us-chip-maker-…

Intel warns it may exit chip manufacturing if it can't secure major customers for its new 14A process, signaling a big shift in the tech industry landscape. reuters.com/world/asia-pac…

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AI-generated songs by deceased artists are popping up on Spotify, sparking outrage and ethical debates-should AI be allowed to mimic artists without permission? techradar.com/computing/arti…

To truly lead in AI, America must prioritize nationwide fiber broadband-connecting every community to unlock the full power of next-gen technology and innovation. washingtonexaminer.com/opinion/347581…

Unlock your startup's future at Tech30, India's top startup showcase at TechSparks 2025. Apps open for innovators in AI, deeptech, SaaS & more-apply by Aug 14! yourstory.com/2025/07/tech30…

@Aurimas_Gr CT streamlines operational efficiency, enhancing adaptability in deployments.

MLOps 101: 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 (𝗖𝗧) and what steps are needed to achieve it. CT is the process of automated ML Model retraining in Production Environments on a specific trigger. Let’s look into some prerequisites for this: 1) Automation of ML Pipelines. - Pipelines are orchestrated. - Each pipeline step is developed independently and is able to run on different technology stacks. - Pipelines are treated as a code artifact. ✅ You deploy Pipelines instead of Model Artifacts allowing Continuous Training In production. ✅ Reuse of components allows for rapid experimentation. 2) Introduction of strict Data and Model Validation steps in the ML Pipeline. - Data is validated before training the Model. If inconsistencies are found - Pipeline is aborted. - Model is validated after training. Only after it passes the validation is it handed over for deployment. ✅ Short circuits of the Pipeline allow for safe CT in production. 3) Introduction of ML Metadata Store. - Any Metadata related to ML artifact creation is tracked here. - We also track performance of the ML Model. ✅ Experiments become reproducible and comparable between each other. ✅ Model Registry acts as glue between training and deployment pipelines. 4) Different Pipeline triggers in production. - Ad-hoc. - Cron. - Reactive to Metrics produced in Model Monitoring System. - Arrival of New Data. ✅ This is where the Continuous Training is actually triggered. 5) Introduction of Feature Store (Optional). - Avoid work duplication when defining features. - Reduce risk of Training/Serving Skew. 𝗠𝘆 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀 𝗼𝗻 𝗖𝗧: ➡️ Introduction of CT is not straightforward and you should approach it iteratively. The following could be good Quarterly Goals to set: - Experiment Tracking is extremely important at any level of ML Maturity and the least invasive in the process of ML Model training - I would start with ML Metadata Store introduction. - Orchestration of ML Pipelines is always a good idea, there are many tools supporting this (Airflow, Kubeflow, VertexAI etc.). If you are not doing it yet - grab this next, also make the validation steps part of this goal. - The need for a Feature Store will wary on the types of Models you are deploying. I would prioritise it if you have Models that perform Online predictions as it will help with avoiding Training/Serving Skew. - Don’t rush with Automated retraining. Ad-hoc and on-schedule will bring you a long way. Let me know your thoughts! 👇 #LLM #MachineLearning #AI

Hi @lukas_trips I thought you might like this!

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The 3D surveillance software market is set to double by 2031, driven by rising demand across energy, real estate, manufacturing, and defense sectors for advanced security solutions. kalkinemedia.com/news/world-new…

Struggling with today's NYT Strands? The theme is "I fold!" and the answers include origami, crane, flower, butterfly, and heart. Check out all the hints! techradar.com/computing/webs…

The smart wearables market is booming! Driven by health and tech awareness, it's set to nearly triple by 2032, evolving beyond fitness into work and healthcare. kalkinemedia.com/news/world-new…

Sify Technologies reports strong Q1 FY26 growth in revenue and EBITDA, expanding data center capacity and driving AI/cloud adoption in India with new flexible, pay-per-use colocation solutions. fool.com/earnings/call-…

Wondering if paid antivirus is really worth it? Paid options offer advanced features and more complete protection compared to basic, free options. Your cybersecurity might thank you! techradar.com/news/is-paid-a…

Vertiv is acquiring Great Lakes Data Racks & Cabinets for $200M, boosting their AI-ready rack solutions and strengthening digital infrastructure offerings for data centers worldwide! newbusinessethiopia.com/vertiv-to-acqu…

Graphene takes center stage! Forecasts show the global market soaring from $1.4B in 2025 to $10.1B by 2034, fueled by breakthroughs in energy, electronics, and composites. kalkinemedia.com/news/world-new…

California's cities lead the nation in AI readiness, with the Bay Area named an absolute "superstar" by Brookings. The Golden State is setting the pace in AI innovation! latimes.com/business/story…

Microsoft scrapped the compact taskbar date and time in Windows 11 after tester backlash-would you have liked the option to choose? Share your thoughts! techradar.com/computing/wind…