Integrating artificial intelligence into software applications has moved from optional to expected for applications competing on productivity, personalisation, and data intelligence. The question for engineering teams is not whether to integrate AI, but which tools are the right fit for the application's requirements and the team's existing expertise.
This post covers six AI toolkits that software developers use to build machine learning capabilities into applications, what each one is designed for, and what the integration trade-offs look like in practice. For context on how AI is reshaping what software teams build, our overview of enterprise versus consumer AI software covers the broader landscape.
Choosing the Right AI Toolkit
The right toolkit depends on four factors: the type of AI capability required (deep learning, traditional ML, NLP, or cloud-hosted services), the team's existing expertise in Python and machine learning, the project's scale and deployment requirements, and whether the application needs a managed cloud service or a framework the team controls directly. Getting this choice right before committing to an integration avoids significant rework later.
1. TensorFlow
TensorFlow is Google's open-source machine learning framework, designed for building and deploying complex neural networks at scale. It supports deep learning tasks including image recognition, speech processing, and natural language understanding, with production deployment tools that move models from research to production environments.
Best suited to: applications requiring custom deep learning models with specific performance requirements; teams with ML expertise building production-grade neural network pipelines. TensorFlow's scalability makes it the standard for enterprise applications where AI model performance at scale is a requirement.
The trade-off is a steep learning curve for teams new to deep learning. It is not the right choice for standard machine learning tasks that simpler tools handle with less overhead.
2. PyTorch
PyTorch is Meta's open-source deep learning framework, known for its dynamic computation graph and developer-friendly design. Where TensorFlow emphasises production deployment, PyTorch prioritises iterative experimentation and research, making it the dominant framework in academic and research contexts.
Best suited to: teams that need to iterate rapidly on model architecture, research contexts, and applications where the model design is still evolving. PyTorch's intuitive API makes the development loop faster for teams experimenting with new model approaches rather than deploying a fixed architecture.
To get full value from PyTorch, developers need a solid understanding of ML principles. For teams building in that space, it is the fastest framework for moving from idea to working model.
3. Keras
Keras is a high-level deep learning API designed for accessibility without sacrificing capability. Now available as a multi-backend framework (Keras 3) that runs on TensorFlow, JAX, or PyTorch, it provides consistent, simplified APIs for building ML models without requiring direct interaction with lower-level framework operations.
Best suited to: teams building machine learning capabilities who are not ML specialists; applications where development speed matters more than fine-grained model control; developers upskilling into ML who need a framework that reduces cognitive overhead. Keras is specifically designed so that common tasks require minimal user actions, with error messages that are diagnostic rather than cryptic.
Where Keras may not be the right choice is in applications requiring deeply customised models. For those cases, the additional control offered by TensorFlow or PyTorch directly is worth the overhead.
4. Scikit-Learn
Scikit-Learn is the standard Python library for traditional (non-deep-learning) machine learning. It provides a clean, consistent interface for classification, regression, clustering, and dimensionality reduction, with extensive tooling for data preprocessing, model evaluation, and pipeline construction.
Best suited to: applications involving structured data analysis, predictive modelling, and data mining where deep learning is unnecessary. If the problem is classifying customers, predicting churn, or detecting anomalies in structured data, Scikit-Learn typically delivers the required results with significantly less complexity than a deep learning framework. For teams assessing the skills needed for AI integration, Scikit-Learn is often the lowest-friction entry point.
Its limitation is deep learning: for image recognition, NLP, or applications requiring neural networks, it is not the right tool.
5. Azure Machine Learning
Azure Machine Learning is Microsoft's cloud-based ML platform, providing a managed environment for the full ML lifecycle: data preparation, model training, deployment, and monitoring. It integrates with the broader Azure ecosystem and provides enterprise-grade features for governance, compliance, and access control.
Best suited to: large-scale applications already operating in the Azure ecosystem where the team wants managed infrastructure rather than self-hosted model training and deployment. Azure ML handles infrastructure complexity so engineering teams can focus on model development rather than cluster management.
Teams unfamiliar with Azure will face a meaningful onboarding curve. The platform's breadth is a strength for teams that need it but adds unnecessary complexity for smaller projects where a lighter framework would serve equally well.
6. IBM Watson and Watsonx
IBM Watson is a suite of AI services with particular strength in natural language processing, conversational AI, and enterprise AI applications. The Watsonx platform extends this with capabilities for training and deploying custom AI models across business domains, with a focus on giving organisations control over their own data and model governance.
Best suited to: enterprise applications requiring advanced NLP, document understanding, or conversational AI; organisations where data sovereignty and governance requirements preclude using third-party model APIs; large enterprises already operating in the IBM ecosystem.
Watson and Watsonx carry a steeper entry cost, both in licensing and in learning, than open-source frameworks. They are most appropriate for organisations with specific enterprise AI requirements that more accessible tools do not address.
Matching Toolkit to Requirement
The six toolkits cover different positions on the spectrum from accessible to powerful. Keras and Scikit-Learn optimise for accessibility and development speed. TensorFlow and PyTorch optimise for model flexibility and performance. Azure ML and IBM Watson optimise for managed, enterprise-scale deployment. Most production applications will use one of the first four for model development and one of the cloud platforms for deployment and monitoring.
For teams building AI-powered software and looking to understand how this connects to engineering career development, our overview of the rise of AI in software development provides useful context. To discuss AI integration in production applications, speak to Scrums.com about how our teams approach this work.
Frequently Asked Questions
What is the difference between TensorFlow and PyTorch?
Both are open-source deep learning frameworks for building and training neural networks, but they differ in design philosophy. TensorFlow was built with production deployment in mind, with strong support for serving models at scale. PyTorch was built for research and iterative development, with a dynamic computation graph that makes it easier to experiment and debug. PyTorch dominates in academic research; TensorFlow remains widely used in production deployments, particularly in organisations with existing TensorFlow infrastructure.
When should I use Scikit-Learn instead of a deep learning framework?
Scikit-Learn is the right choice for most structured data problems: classification, regression, clustering, and predictive modelling on tabular data. Deep learning frameworks are necessary when working with unstructured data like images, audio, or text where neural networks deliver results that traditional ML algorithms cannot. If you can solve the problem with a decision tree, random forest, or support vector machine, Scikit-Learn is simpler, faster to implement, and easier to interpret than a deep learning approach.
Is Keras the same as TensorFlow?
Keras started as a high-level API running on top of TensorFlow. With Keras 3, it became a standalone multi-backend framework that can run on TensorFlow, JAX, or PyTorch. The practical difference for most developers is that Keras provides a simpler, more consistent interface for building models, while TensorFlow provides lower-level control and production tooling. For teams building ML capabilities without specialised ML expertise, Keras is the more accessible starting point.
What is Watsonx and how does it differ from IBM Watson?
IBM Watson is IBM's suite of AI services, known particularly for natural language processing and conversational AI. Watsonx is IBM's next-generation AI platform, designed to give enterprises greater control over building and deploying custom AI models with a focus on data governance and model transparency. Watsonx addresses enterprise concerns around data sovereignty and AI accountability that general-purpose AI APIs do not. Both platforms target enterprise use cases where governance, compliance, and customisation requirements exceed what consumer AI services provide.
Should I use a cloud AI platform or an open-source framework?
The choice depends on your team's infrastructure capacity and application requirements. Open-source frameworks (TensorFlow, PyTorch, Scikit-Learn) give you full control over the model and require you to manage training infrastructure, deployment, and monitoring. Cloud platforms (Azure ML, IBM Watson) manage that infrastructure at the cost of vendor dependency and licensing. For most teams without dedicated ML infrastructure engineers, a cloud platform for deployment combined with an open-source framework for model development is the most practical combination.
