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What tech stack is used for AI?

The AI tech stack includes programming languages, machine learning frameworks, data storage solutions, and cloud services that help developers build, train, and deploy AI models.

The most widely used programming language for AI is Python, thanks to its rich ecosystem of libraries like TensorFlow, PyTorch, Scikit-learn, and Keras. Some AI startups also use Julia for high-performance numerical computing or R for statistical modeling.

For data processing, AI applications rely on SQL (PostgreSQL), NoSQL (MongoDB), and big data tools like Apache Spark. Cloud providers like AWS, Google Cloud, and Microsoft Azure offer AI-powered services, enabling scalable AI model training and deployment.

Key Takeaways:

Programming Languages → Python, Julia, R for AI model development.
AI Frameworks → TensorFlow, PyTorch, Scikit-learn, Keras.
Databases → PostgreSQL, MongoDB, Apache Spark for big data.
Cloud AI Services → AWS SageMaker, Google AI Platform, Azure AI.
Compute Power → GPUs, TPUs, and Kubernetes for distributed AI training.

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