2.5 Model Fine Tuning and Performance
Fine-Tuning for Better Performance Fine-tuning helps improve LLM performance by making them understand human-like prompts and generate natural-sounding language. However,…
2.4 Generative AI Project Lifecycle
A generative AI project typically follows these stages: Stages of the AI Cycle: AI Model Development Process Identify Use Case:…
2.3 Generative AI Architectures
Generative AI models create new, synthetic data based on existing data. The three key architectures in Generative AI are: 1.…
2.2 GEN AI and Large Language Models
Generative AI models perform better when they are larger, but training these large models is difficult and expensive. As models…
2.1 Basic Concepts of Generative AI
Generative AI is a subset of deep learning focused on generating new content such as text, images, audio, video, and…
Quiz 1
Purpose of Quiz Walkthroughs: Approach to Each Question: Question 1: Question: A company wants to analyze diverse data sources (audio,…
Model Evaluation Metrics and Business Impact
In the ML development lifecycle, evaluating model performance using various metrics is crucial. Here’s a breakdown of key metrics for…
MLOps Services and Model Evaluation Metrics
In MLOps, it’s crucial to use various services and metrics to manage models and workflows effectively. Here’s a breakdown of…
ML Lifecycle – Model Monitoring and MLOps
Model Monitoring Amazon SageMaker Model Monitor MLOps (Machine Learning Operations) Amazon SageMaker Pipelines
ML Lifecycle -Data Collection and Preparation
Below are the steps for Data Collection and Preparation in the Machine Learning Lifecycle Identify Data Requirements Data Collection Labeling…