Describe the workflow using AI tools from task receipt to deployment.

The workflow with AI tools typically includes the following stages:

1. Task acquisition — analyzing requirements, setting goals, determining which parts can be automated or improved with AI.

2. Data research and preparation — collecting, cleaning, and preparing data for training or using AI.

3. Selection and configuration of AI tools — choosing models, libraries, or services (e.g., OpenAI API, TensorFlow, Hugging Face).

4. Development and integration — writing code that uses AI tools to solve the task, integrating with existing applications.

5. Testing — verifying the correctness of AI operation, evaluating the quality of results, adjusting parameters.

6. Optimization — improving performance, reducing costs, adapting to load.

7. Deployment — deploying the application with AI components into production, setting up monitoring and logging.

8. Support and iterations — collecting feedback, updating models, fixing errors.

Example: For an automatic text classification task, a developer receives a set of documents, trains a model on labeled data, integrates the model call into the backend, tests on new data, then deploys the service in a container with quality monitoring.