AI Infrastructure - Software Components
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- Overview
Software components for AI infrastructure include data processing frameworks, machine learning operations (MLOps) platforms, and storage systems. These components work together to create a scalable and efficient AI infrastructure.
Software components for AI infrastructure include:
- Machine learning frameworks: Tools that provide libraries and functions for creating and training AI models. Examples include TensorFlow, PyTorch, and Keras.
- Data processing libraries: Tools for handling and processing large datasets. Examples include Pandas, NumPy, and SciPy.
- Scalable storage solutions: Technologies for storing and retrieving large volumes of data. Examples include cloud storage, data lakes, and distributed file systems.
- Programming languages: Languages used to develop AI models. Examples include Python and Java.
- Distributed computing platforms: Platforms that allow for distributed computing. Examples include Apache Spark and Hadoop.
- Data preparation and cleaning tools: Tools for preparing datasets for training purposes.
- Monitoring and management tools: Tools for monitoring and managing AI workloads.
- Cluster management software: Software that allocates GPUs to jobs, distributes batch jobs, and manages queues and priorities. Examples include Kubernetes and Slurm.
- Provisioning tools: Tools that provide containers for applications or jobs to run on the cluster. Examples include Docker and Singularity.
- Monitoring software: Software that tracks metrics and data specific to AI operations. Examples include Prometheus, Grafana, and Elastic Stack.
[More to come ...]
