From DevOps to MLOPs: What I Learned Today-01

"AI is a broad field that encompasses the development of intelligent systems capable of performing tasks that typically require human intelligence, such as perception, reasoning, learning, problem-solving, and decision-making. AI serves as an umbrella term for various techniques and approaches, including machine learning, deep learning, and generative AI, among others. Machine Learning(ML) ML is a type of AI for understanding and building methods that make it possible for machines to learn. These methods use data to improve computer performance on a set of tasks."

"Deep Learning(DL) Deep learning uses the concept of neurons and synapses similar to how our brain is wired. An example of a deep learning application is Amazon Rekognition, which can analyze millions of images and streaming and stored videos within seconds. Generative AI Generative AI is a subset of deep learning because it can adapt models built using deep learning, but without retraining or fine tuning."

Artificial intelligence encompasses systems that perform perception, reasoning, learning, problem-solving, and decision-making. Machine learning is a data-driven approach that teaches computers to improve performance on defined tasks using examples. Deep learning implements layered neuron-like architectures to model complex patterns and supports large-scale image and video analysis. Generative AI produces new data by sampling and recombining patterns learned from training data, sometimes without retraining. The machine learning lifecycle begins with collecting and processing training data because model performance depends on data quality. Training datasets can be labeled, providing targets for supervised learning, or unlabeled for other methods.

Read at Medium