The recent launch of the DeepSeek-R1 model sent ripples across the global AI community. It delivered breakthroughs on par with the reasoning models from Meta and OpenAI, achieving this in a fraction of the time and at a significantly lower cost.
Beyond the headlines and online buzz, how can we assess the model’s reasoning abilities…
Deploying your Large Language Model (LLM) is not necessarily the final step in productionizing your Generative AI application. An often forgotten, yet crucial part of the MLOPs lifecycle is properly load testing your LLM and ensuring it is ready to withstand your expected production traffic. Load testing at a high level is the practice of…
Recently, Sesame AI published a demo of their latest Speech-to-Speech model. A conversational AI agent who is really good at speaking, they provide relevant answers, they speak with expressions, and honestly, they are just very fun and interactive to play with.
Note that a technical paper is not out yet, but they do have a…
For a ML model to be useful it needs to run somewhere. This somewhere is most likely not your local machine. A not-so-good model that runs in a production environment is better than a perfect model that never leaves your local machine.
However, the production machine is usually different from the one you developed the…
I’m definitely not the only person who feels that YouTube sponsor segments have become longer and more frequent recently. Sometimes, I watch videos that seem to be trying to sell me something every couple of seconds.
On one hand, it’s great that both small and medium-sized YouTubers are able to make a living from their…
In my previous article, I discussed how morphological feature extractors mimic the way biological experts visually assess images.
This time, I want to go a step further and explore a new question: Can different architectures complement each other to build an AI that “sees” like an expert?
Introduction: Rethinking Model Architecture Design
While building a…
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Least Squares is used almost everywhere when it comes to numerical optimization and regression tasks in machine learning. It aims at minimizing the Mean Squared Error (MSE) of a given model.
Both L1 (sum of absolute values) and L2 (sum of squares) norms offer an intuitive way to sum signed errors while preventing…
Which Outcome Matters? Here is a common scenario : An A/B test was conducted, where a random sample of units (e.g. customers) were selected for a campaign and they received Treatment A. Another sample was selected to receive Treatment B. “A” could be a communication or offer and “B” could be no communication or no…
In the world of machine learning, we obsess over model architectures, training pipelines, and hyper-parameter tuning, yet often overlook a fundamental aspect: how our features live and breathe throughout their lifecycle. From in-memory calculations that vanish after each prediction to the challenge of reproducing exact feature values months later, the way we handle features can…
Why distributed tracing is the key to resolving performance issues (Image by Author) - Distributed tracing — ideaMy articles are free for everyone to read! If you don’t have a Medium subscription, feel free to explore the full article directly on my blog: https://blog.bytedoodle.com/distributed-tracing-a-powerful-approach-to-debugging-complex-systems/ M odern applications are increasingly built using microservices, where hundreds of…