A collection of libraries, tools, and APIs that provide the infrastructure for building, training, evaluating, and deploying machine learning and AI models. TensorFlow, PyTorch, JAX, and scikit-learn are the dominant frameworks in production AI development, and understanding which framework underlies a tool or vendor platform informs predictions about its performance characteristics, deployment constraints, and long-term support trajectory.
Also known as ML framework, deep learning framework, AI development framework
A machine learning framework provides the computational primitives that make model development feasible: automatic differentiation that computes gradients without requiring manual calculus, GPU acceleration that parallelizes matrix operations across thousands of cores, a library of pre-built layers and components that practitioners compose into model architectures, and training loops that handle the repetitive mechanics of batching, optimization, and metric logging. Without a framework, implementing a neural network from scratch would require writing thousands of lines of low-level linear algebra code for every new architecture.
PyTorch, developed by Meta, and TensorFlow, developed by Google, are the two dominant deep learning frameworks in research and production. PyTorch is the dominant choice in academic research and has gained substantial production adoption; TensorFlow is widely deployed in Google’s own systems and in production environments where its mature serving infrastructure, TensorFlow Serving and TensorFlow Lite, is valuable for deployment. JAX, also from Google, is increasingly used for research that requires high-performance numerical computing beyond standard neural network training. Scikit-learn is the standard framework for classical machine learning methods on structured data, providing a consistent API for models from logistic regression to gradient boosted ensembles.
Higher-level libraries built on top of these frameworks abstract common patterns: Hugging Face Transformers simplifies working with pre-trained language models; PyTorch Lightning standardizes training loop structure; LangChain provides components for building language model application workflows. These libraries reduce the amount of framework-level code practitioners need to write for common use cases, but they introduce their own dependency and versioning considerations. The framework stack underlying a production AI system determines its deployment requirements, hardware compatibility, and the ecosystem of tools and community support available for maintaining it.
Most agencies will not write framework-level code themselves, but the framework choices made by the AI tools and vendors they use affect deployment flexibility, integration cost, and long-term maintainability in ways that surface in procurement, project scoping, and client transitions. A working ad agency evaluating AI tools or overseeing the work of AI vendors benefits from understanding what the underlying framework means for the practical constraints of the engagement.
Framework determines deployment environment requirements. A model built in TensorFlow and served via TensorFlow Serving has different deployment requirements than one built in PyTorch. When an agency is deploying custom models to client infrastructure or working with a vendor whose tool needs to be integrated into a client’s existing stack, the framework compatibility question is a practical one that can affect whether a proposed integration is feasible at all. Understanding what to ask is more valuable than knowing how to build.
Open-source framework adoption reflects the direction of AI development. The AI research community’s near-universal shift to PyTorch means that new architectures, techniques, and pre-trained models appear first as PyTorch implementations. An agency or vendor who built on TensorFlow and has not migrated to PyTorch-compatible serving infrastructure is operating with a growing lag to the state of the art. Framework selection in 2019 is still affecting what models some organizations can and cannot deploy in 2026. Asking about framework choices in vendor evaluations is a proxy for asking about technical currency.
The Hugging Face ecosystem has become the practical distribution layer for foundation models. The majority of pre-trained foundation models used in production are distributed, accessed, and fine-tuned through the Hugging Face Hub and Transformers library. An agency or vendor that is not working within this ecosystem is either using a closed-source model accessed via API, which is its own set of constraints, or maintaining custom model management infrastructure that represents a significant additional cost and complexity. Understanding this ecosystem is a baseline for understanding how foundation models are actually deployed in practice.
An agency is scoping a custom language model deployment for a retail client who wants AI-assisted product description generation integrated into their e-commerce platform. The client’s platform engineering team uses a Kubernetes-based infrastructure and has standard deployment tooling for containerized services. An initial vendor evaluation identifies a tool that produces high-quality output but is built on a proprietary framework with a custom serving layer that requires a dedicated on-premise server with a specific hardware configuration. A second option uses a Hugging Face model served via a standard PyTorch-based inference endpoint that can be containerized and deployed on the client’s existing Kubernetes infrastructure with no new hardware. The agency recommends the second option not because the output quality is superior, the two are comparable, but because the open framework serving approach integrates into the client’s existing deployment processes, can be maintained by the client’s engineering team without vendor dependency, and can be swapped for an improved model as the Hugging Face ecosystem advances without rebuilding the serving infrastructure.
The generative AI foundations module of the workshop covers the AI development ecosystem including frameworks and model distribution infrastructure, so agencies can evaluate vendor and build options against practical deployment requirements.