Who it’s for
Engineers with the listed foundations who want to deepen this skill or apply it in a different stack.
Starting point: IntermediateData engineering & AI · Corporate training
Sample curriculum
Python with AI capabilities
Connect Python applications with AI capabilities through practical integration, evaluation, and data handling.
- Intermediate
- Live online and in person
At a glance
Bring these foundations
- Python functions, virtual environments, JSON, and API requests.
Technologies & tools
- Python
- AI model APIs
- Jupyter
- Environment variables
A practical destination
Skills to practise.
The sample path works towards these capabilities. Final goals reflect your team’s starting point and agreed scope.
- 01
Identify where a model call helps a Python application.
- 02
Structure requests, responses and failure handling around an API.
- 03
Evaluate generated output and protect sensitive data.
Sample curriculum
Follow the learning route.
Each module connects the concepts to practical work. Open a module to explore both.
01Module 1AI application foundations
Concepts to understand
- Defining a useful task and success criteria
- Model inputs, outputs, and API boundaries
- Environment setup and credentials
Practical work
Choose a bounded AI-assisted task and describe its inputs, expected result and failure risks. Establish a non-AI baseline for comparison.
Show the result. Explain one decision. Identify what you would improve.
02Module 2Python integration
Concepts to understand
- Preparing input data and calling model APIs
- Prompts and structured responses
- Timeouts, retries, and error handling
Practical work
Connect a Python application to a model API with controlled prompts and structured output. Handle timeouts, invalid responses and secrets.
Show the result. Explain one decision. Identify what you would improve.
03Module 3Quality & responsible data handling
Concepts to understand
- Evaluation with representative examples
- Checking outputs and handling uncertainty
- Sensitive data, logging, and usage controls
Practical work
Create representative evaluation cases. Compare generated answers with evidence and identify information that should not be sent to the model.
Show the result. Explain one decision. Identify what you would improve.
04Module 4Practical application
Concepts to understand
- Connecting AI output to an application workflow
- Human review and fallback behavior
- Testing, documentation, and demonstration
Practical work
Build an assisted feature with a human-review step and useful failure behaviour. Explain where the output can be trusted and where it needs checking.
Show the result. Explain one decision. Identify what you would improve.
A sample project
Bring the skills together.
Build an AI-enabled Python utility, evaluate it against sample tasks, and document its failure cases.
Shorter engagements can focus on selected exercises. A larger, learner-owned capstone can be scoped for a longer programme.
Review the work & reasoning
Four useful questions.
- 01
Does the work meet the agreed requirements?
- 02
Can the learner explain the key decisions and trade-offs?
- 03
What happens with invalid input, failure or an unexpected condition?
- 04
What would they change or explore next?
Designed & delivered by Ragav Kumar V.
Training since 2016 · 100+ batches · 3,000+ professionals trained
Live demonstrations, guided exercises, pair work and project reviews connect the concepts to implementation. We agree the goals, starting knowledge, cohort size, delivery and schedule before shaping the final curriculum.
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