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. Generative AI systems are capable of generating new data based on the patterns and structures learned from training data."

Artificial intelligence encompasses systems that perform perception, reasoning, learning, problem-solving, and decision-making. Machine learning uses data-driven methods to enable machines to learn and improve performance on tasks. Deep learning employs layered neuron-like structures and synapse-like connections to learn complex patterns and scale to large image and video workloads. Generative AI adapts deep learning models to produce new data from learned patterns without retraining or fine-tuning. The machine learning process begins with collecting and processing training data, and model quality depends heavily on data quality. Training datasets include labeled and unlabeled examples, with labeled data paired to target outputs.

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