A typical cloud-based data science workflow involves several integrated stages: training, inference, and experimentation. During the training phase, you upload datasets to cloud storage, provision compute resources such as virtual machines or serverless runtimes, and run your code to build and tune models. Once a model is trained, it is deployed to serve predictions — this is the inference phase, where the model responds to new data in real time or batch mode, often using managed endpoints or serverless functions. Experimentation is ongoing throughout: you iterate quickly by leveraging versioned datasets, automated pipelines, and scalable infrastructure, allowing you to test new hypotheses, architectures, and parameters efficiently. Each stage is tightly connected through cloud-native services, enabling rapid collaboration, reproducibility, and the ability to scale up or down as needed.

Architectural intuition is essential when designing cloud data science systems. Cost control is a primary concern: cloud services are metered, so you must balance performance with budget by selecting appropriate compute types, scheduling workloads to avoid idle resources, and using autoscaling features. Reproducibility is another critical factor — by using infrastructure-as-code, containerization, and versioned storage, you ensure that results can be reliably repeated and audited. The interplay between compute, storage, and networking defines your pipeline's efficiency:

• Compute resources process data;
• Storage holds datasets and models;
• Networking moves data between services.

Optimizing data locality and minimizing unnecessary data transfers can significantly reduce both cost and latency, while careful permission management keeps sensitive data secure.

However, cloud architecture for data science comes with trade-offs and limitations. A common mistake is overprovisioning resources, which leads to runaway costs without proportional gains in speed or scalability. Underestimating the complexity of data movement can cause bottlenecks if large datasets are transferred inefficiently between regions or services. Failing to implement robust monitoring and logging can make debugging and optimization difficult, especially in distributed or serverless environments.

Best practices include:

• Designing stateless, modular components;
• Automating deployment and scaling;
• Always considering the principle of least privilege for security.

By applying these principles, you can build data science systems that are robust, scalable, and maintainable in the cloud, while avoiding pitfalls that can undermine reliability or inflate costs.