The potential for misuse or malicious attacks grows as we increasingly rely on AI systems to make vital decisions. Therefore, a strong security framework that safeguards against such threats while enabling the beneficial use of AI is necessary.
MLOps is the set of practices and methods designed to efficiently manage the lifecycle of machine learning models in a production environment.
Data analysis is the process of analyzing, cleaning, transforming, and modeling data to uncover useful information and draw conclusions from it to support decision making.
Automated Machine Learning (AutoML) is an innovation that has reshaped the landscape of machine learning, democratizing its potential by automating the intricate, labor-intensive, and expertise-requiring processes involved.
Neural networks, referred to as artificial neural networks (ANNs), are computational models that mimic the structure and operations of the human brain.
Prompt engineering is the practice of designing and refining specific text prompts to guide transformer-based language models, such as Large Language Models (LLMs), in generating desired outputs.
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