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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?

In gradient descent, each parameter update is based solely on the current gradient, which can lead to undesirable oscillations during the optimization process. Similar to control tasks, where a filter is often required, a momentum mechanism is introduced. Momentum acts … Continue reading →

Gradient flow refers to the movement of gradients during the training of a machine learning model. A gradient is a vector or scalar that represents the direction and magnitude of the steepest increase of a function. Mathematically, it quantifies the … Continue reading →