MLOps
MLOps, or Machine Learning Operations, refers to the set of practices, principles, and tools for deploying, managing, and monitoring machine learning models in production environments. MLOps aims to bridge the gap between the development of machine learning models and their operational use by streamlining the end-to-end ML lifecycle. It involves collaboration between data scientists, engineers, and IT operations to automate the deployment process, ensure model reliability, and facilitate continuous integration and delivery (CI/CD) of ML models.
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Efficient AI tool for embedding and retrieving diverse types of data within LLMs.
AllAPI.ai simplifies AI app development, offering a single API to integrate various AI models and plugins, aiding in faster product deployment.
AI assistant for developers to interpret engineering knowledge base and provide quick answers.
Abacus.AI is an end-to-end AI platform that enables real-time deep learning at scale. It offers AI-assisted data science and MLOps solutions for tasks like forecasting, personalization, anomaly detection, and natural language processing.