Ross K.

🔍 Sara Nóbrega's new article on The Ultimate Guide to Finding Outliers in Your Time-Series Data hones in on an intriguing aspect of machine learning - outliers detection in time-series analysis. This comes across as a pivotal refinement in the field of Data Science. 🧩 Outliers, those pesky gatecrashers in our data, with the notorious potential to misguide forecasts, jumble up statistical models, and obstruct decision-making processes. When left unrecognized and unmanaged, they can present skewed viewpoints, leading to misguided decisions. 🔬 As Data Science practitioners, dealing with outliers must be deemed as not just an optional task but rather an obligatory step in data preprocessing paths. ▫️Yet, detecting outliers is often easier said than done. ▫️Effectual machine learning methods, streamlined tools, and proficient strategies are thus of the essence. 💼 Deep-diving into machine learning from a professional perspective - An erratic outlier today can be a breakthrough opportunity tomorrow. A careful, more precise analysis can weave disruptive outliers into significant patterns and trends, unveiling hidden insights waiting to be discovered. 📈 From the standpoint of an optimist - We're drawing closer to a time when outliers are no longer mere deviations, but rather outliers become crucial messengers of change and deviation. 💡My take on Sara's informative guide is twofold: 1️⃣ It's exciting to note that the boundaries of what once were seen as challenges in machine learning are now avenues for innovation and discovery. 2️⃣ It points towards the future where our focus will not simply be on attaining superior model performance but also on comprehending those unequalled elements that enkindle that performance. ☀️ Above all, let's embrace our outliers. After all, in an era of incessant transformation, they may be the sparks to ignite our pathways to advancement. #DataScience #MachineLearning #OutlierDetection #TimeSeriesAnalysis #SaraNobrega

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Looks like there's a new leader in inference speed: SambaNova. I just tried it and got over 1,000 tokens per second using Llama-3 8B. Looks faster than Groq. You can try it here: https://fast.snova.ai/. Benchmark is here: https://lnkd.in/g95Zv7-C

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I can sense the LLM-hype cycle crashing soon. Don’t forget: 1. End-user value matters. Just adding LLM doesn’t make your product better 2. LLMs can’t plan/reason; no amount of monkey patching will overcome this limitation 3. RAG isn’t always better than vanilla BM25 search processed 200 applications for the AI hackhouse. Many seem to not understand the basic limitations of LLMs everyone seems to be building similar wrappers on openAI API. I think it’s a function of perverse incentives: it is profitable to hype up your product’s capabilities Ultimately, users don’t care about AI or LLMs. What matters is if you’re solving their problems in a significantly better way v/s all other alternatives. (And remember now all other alternatives are also LLM-powered).

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DO you SPEND 20% of BUDGET ON AI ? NO?! There has been a great talk from NVIDIA saying … 📉 … that Europe is falling behind in AI innovation. One of the root causes being that not enough data/digital budget is spent on AI. They observe that about 20% of all budget spending is level to at least stay afloat. 👩‍💻 That also means that at least every 5th person in your data roles needs to be AI-skilled/producing. Take a honest look into mirror, where does your organization stand on this point? #ai #innovation #budget #spending #conference #talk #nvidia #aiaccelerationinstitute #trends

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