Monthly Archives: December 2024

I just read the following article: https://medium.com/towards-data-science/your-company-needs-small-language-models-d0a223e0b6d9. I personally liked the following illustration: From a non technical view I was shocked by the following illustration: At least this is why SML will be the next step.

Classical sequence-to-sequence (Seq2Seq) models based on Recurrent Neural Networks (RNNs) condense the entire input sequence into a single fixed-length context vector. While effective for short sequences, they struggle with longer or more complex inputs due to their inability to capture … Continue reading →

The DORA Quick Check is a streamlined assessment tool designed to measure your team’s software delivery performance. It focuses on four key metrics: Explore more about it here: DORA Quick Check. A first try for usual automotive cases:

A common pattern for solution architecture are different layer (aka tiers) e.g, frontend, backend and database. The common approaches such as micro-service and self contained (aka CSC- self contained system) are just different kinds of commonly used layer.

Micro-service architecture is a contemporary software design approach widely adopted by companies such as Netflix and Amazon, allowing them to release features at an impressive pace. This approach decomposes a large application into a collection of small, loosely coupled services. … Continue reading →

My takeaways from the book “Software Engineering at Google: Lessons Learned from Programming Over Time” can be grouped into cultural, organizational, and technical insights. Open communication and psychological safety are emphasized as critical components of a successful engineering culture. These principles foster … Continue reading →

As a technology manager, navigating new solution spaces to improve technology adoption, mitigate risks, and discover opportunities can be a complex task. The TRIZ (Theory of Inventive Problem Solving) methodology provides a structured approach to evaluate existing technological solutions from … Continue reading →

Traditional neural network design relies heavily on gradient flow, which refers to the movement of gradients during the training process. A gradient is a value (vector or scalar) that represents the direction and magnitude of the steepest increase of a … Continue reading →

Gradient flow describes the movement of gradients during the training of a machine learning model. A gradient is a value (vector or scalar) that shows the direction and magnitude of the steepest increase of a function, indicating how a function … Continue reading →

Different approach to achieve parallelism in training deep neural networks: Any better idea?