MLOps
MLOps (Machine Learning Operations) is the practice of applying DevOps principles to the machine learning lifecycle, including development, deployment, monitoring, and maintenance. MLOps ensures reliable, reproducible, and scalable ML systems.
Understanding MLOps
MLOps (Machine Learning Operations) is the set of practices, tools, and cultural norms that streamline the deployment, monitoring, and maintenance of machine learning models in production environments. Drawing from DevOps principles, MLOps addresses challenges unique to ML systems, including data versioning, experiment tracking, model registry management, automated retraining pipelines, and performance monitoring for model drift. Without MLOps, many organizations struggle to move models beyond research prototypes into reliable production services. Key tools in the ecosystem include MLflow for experiment tracking, Kubeflow for orchestration, and platforms like Weights & Biases and Trigger.dev for workflow automation. MLOps practices ensure reproducibility, scalability, and governance across the machine learning lifecycle. As organizations deploy more AI models, including large language models and multimodal AI systems, robust MLOps pipelines become essential for managing complexity, controlling costs, and ensuring model quality over time.
Category
AI Infrastructure
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AI Chip
An AI chip is a specialized processor designed specifically for artificial intelligence workloads like neural network training and inference. Examples include NVIDIA's GPUs, Google's TPUs, and custom ASICs.
API
An API (Application Programming Interface) is a set of protocols and tools that allows different software systems to communicate. AI APIs enable developers to integrate machine learning capabilities like text generation, image recognition, and speech processing into applications.
CUDA
CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing platform that allows developers to use GPUs for general-purpose processing. CUDA is the foundation of GPU-accelerated deep learning training.
Data Lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format. AI systems often draw training data from data lakes that store structured, semi-structured, and unstructured information.
Data Pipeline
A data pipeline is an automated series of data processing steps that moves and transforms data from source systems to a destination. ML data pipelines handle ingestion, cleaning, feature engineering, and model training workflows.
Data Warehouse
A data warehouse is a centralized repository for structured, processed data optimized for analysis and reporting. AI and ML systems often source their training data from enterprise data warehouses.
Distributed Training
Distributed training is the practice of splitting model training across multiple GPUs or machines to handle large models and datasets. It uses data parallelism or model parallelism to accelerate training.
Edge AI
Edge AI refers to running artificial intelligence algorithms locally on hardware devices rather than in the cloud. Edge AI enables real-time inference with lower latency, better privacy, and reduced bandwidth requirements.