01
144 Hours of Structured Learning
Build understanding over time with guided instruction, coding exercises, review, and space to apply each new concept.
- 48 learning sessions
- 3 hours per session
- Guided practice and project work
INDIVIDUAL TRAINING AI ENGINEERING
Build Intelligent Applications. Engineer What’s Next.
Turn AI concepts into working applications through a structured learning journey. Build foundations in Python and machine learning, explore deep learning and large language models, then develop, evaluate, and deploy practical AI-powered solutions.
Practical skills. Purposeful technology. Real-world application.
THE PROGRAM
From AI Foundations to Useful, Working Applications.
Follow a progressive path from Python and data preparation to machine learning, deep learning, and generative AI. Learn to connect models and AI services to software applications, evaluate their results, and prepare solutions for responsible use.
02
A Progressive AI Engineering Journey
Move from core programming and model fundamentals to generative AI systems and application deployment.
- Python, data, and machine learning
- Deep learning and language models
- RAG, evaluation, and deployment
03
Learn by Building
Apply the concepts through small experiments, application checkpoints, and a final project that brings the engineering workflow together.
- Hands-on coding and model experiments
- Practical testing and evaluation
- A documented AI application project
LEARNING OUTCOMES
Connect AI Knowledge with Practical Engineering Work.
Develop the ability to select an appropriate approach, work with models and AI services, test system behavior, and explain the trade-offs behind a solution. The focus is not just on calling an AI API, but on building an application that can be evaluated and improved.
01
Build with Python
Write readable Python code and prepare data for AI development tasks.
- Use functions, collections, modules, and exceptions
- Work with notebooks and common data libraries
02
Train and Evaluate Models
Explore machine learning methods and measure model behavior against a clear objective.
- Build introductory regression and classification models
- Compare metrics, error patterns, and limitations
03
Understand Deep Learning
Work with neural-network concepts and learn how modern models are used for language and other data.
- Understand training, loss, and overfitting
- Explore embeddings and transformer concepts
04
Build Generative AI Features
Integrate language models into application workflows with clearer inputs, outputs, and guardrails.
- Use model APIs and structured responses
- Design retrieval-augmented generation workflows
05
Test AI System Behavior
Evaluate outputs and identify failure cases before exposing a feature to real users.
- Create test cases and evaluation datasets
- Consider hallucination, privacy, cost, and latency
06
Deliver an AI Application
Package a focused AI solution with a usable interface, documented behavior, and a deployment plan.
- Connect models through application interfaces
- Explain design choices and known limitations
PROGRAM CURRICULUM
From Python Foundations to Deployed AI Applications.
Explore the modules, learning objectives, topics, and subtopics that guide the program. Each stage builds on the previous one, with practical checkpoints before learners move to more complex model and application workflows.
Module Learning Objective
Write, run, debug, and organize Python code for AI workflows. Build the programming foundation needed to work with datasets, libraries, APIs, and model-serving components.
- Distinguish AI, machine learning, deep learning, and generative AI at a practical level
- Set up Python, virtual environments, notebooks, and an editor
- Understand packages, dependencies, and reproducible environments
- Use Git to track code, notebooks, and project notes
- Use variables, basic types, operators, conditions, and loops
- Work with lists, dictionaries, tuples, sets, and comprehensions
- Write reusable functions and understand scope
- Read tracebacks and debug common errors
- Split code into modules and imports
- Read and write CSV, JSON, and text files
- Handle exceptions and validate inputs
- Use type hints, docstrings, and clear naming where useful
- Create a small Python program to load and summarize a dataset or API response
- Validate inputs and handle a missing or malformed value
- Commit the work with a short README
- Explain the program flow and one improvement you would make
Module Learning Objective
Develop practical intuition for the mathematical and data concepts used in machine learning, while learning to frame AI problems, prepare data, and establish a repeatable experiment workflow.
- Interpret vectors, matrices, and dimensions in examples
- Understand functions, slopes, and the intuition behind gradients
- Read basic probability and statistical summaries
- Relate numerical features to model inputs and outputs
- Inspect columns, types, missing values, and duplicate records
- Separate features from target labels in a supervised task
- Understand train, validation, and test splits
- Recognize leakage and why it can make results misleading
- Define a problem statement and a baseline approach
- Record dataset versions, assumptions, and evaluation metrics
- Use random seeds and keep experiments comparable
- Checkpoint 2 - Dataset and experiment brief
Module Learning Objective
Train and evaluate common machine learning models using Python libraries. Compare model families, detect common evaluation problems, and justify a model choice against a defined task and dataset.
- Distinguish supervised, unsupervised, and reinforcement learning
- Map prediction, classification, clustering, and anomaly detection to use cases
- Establish a simple baseline before introducing a complex model
- Understand feature engineering and the role of domain context
- Fit a simple linear regression model
- Explore logistic regression and classification outputs
- Understand decision trees, random forests, and boosted-tree concepts
- Compare model output with the baseline
- Understand loss, optimization, and the train-validation-test workflow
- Recognize overfitting, underfitting, bias, and variance
- Choose appropriate metrics such as MAE, precision, recall, and F1
- Use confusion matrices and inspect misclassified examples
- Use clustering to explore groups in a dataset
- Understand dimensionality reduction at a conceptual level
- Scale and encode features when the method requires it
- Interpret segments carefully and avoid unsupported causal claims
- Train at least two candidate models on a prepared dataset
- Compare metrics on a held-out split
- Inspect representative errors and explain trade-offs
- Checkpoint 3 - Model comparison note and reproducible notebook
Module Learning Objective
Understand the main concepts behind neural networks and implement a small training workflow. Learn how deep learning extends traditional machine learning and when the added complexity may or may not be justified.
- Understand layers, weights, biases, activations, and outputs
- Connect forward propagation with a prediction
- Interpret loss as a training objective
- Understand gradient descent and backpropagation intuitively
- Prepare input features and labels for a neural model
- Build and train a small model with a supported framework
- Track training and validation loss
- Identify common causes of unstable or poor training
- Understand the high-level purpose of convolutional neural networks
- Understand sequence and transformer-model concepts
- Compare compute, data, and interpretability trade-offs
- Checkpoint 4 - Neural-network experiment and learning notes
Module Learning Objective
Build basic application features using a large language model, structure model inputs and outputs, and recognize practical limitations around quality, context, cost, latency, and sensitive data.
- Understand tokens, context windows, model inference, and embeddings
- Compare hosted model APIs with local or open-weight model options
- Identify where model behavior is probabilistic rather than deterministic
- Select a narrow use case with a measurable user benefit
- Specify role, task, context, constraints, and output format
- Use examples and delimiters to make instructions clearer
- Request structured output and validate it before application use
- Test ambiguous, incomplete, and adversarial inputs
- Call a model API from a Python application
- Manage secrets through environment configuration
- Handle timeouts, rate limits, retries, and provider errors
- Track token usage, latency, and approximate cost
- Identify hallucination, prompt injection, and unsafe-output risks
- Avoid sending confidential or personal information without permission
- Apply output validation, access controls, and human review where needed
- Communicate limitations and avoid overstating model reliability
- Build a focused text-processing, research, or assistance feature
- Use a defined sample set to test outputs
- Document failure cases and planned guardrails
- Demonstrate the feature and explain its operating boundaries
Module Learning Objective
Extend an LLM application with retrieval from a bounded knowledge source and introduce tool-using agent workflows. Learn to inspect retrieval quality, restrict tool access, and evaluate the complete application rather than judging answers by appearance alone.
- Understand vector embeddings and semantic similarity
- Chunk documents and retain useful metadata
- Use a vector store or vector-search library in a controlled example
- Inspect relevant and irrelevant retrieval results
- Build ingestion, chunking, embedding, retrieval, and answer steps
- Use retrieved context to ground an answer
- Return relevant source references where available
- Handle missing evidence and questions outside the knowledge base
- Create representative questions with expected evidence
- Check retrieval relevance and answer faithfulness separately
- Tune chunking, metadata, top-k, and prompt behavior
- Checkpoint 6 - RAG evaluation report
- Distinguish a fixed workflow from a model-directed agent
- Define tools with explicit inputs, outputs, and permission boundaries
- Use tool calling for a small, bounded task
- Add step limits, approval gates, and error handling
Module Learning Objective
Package AI capabilities behind a maintainable application interface, apply systematic tests, and prepare a basic deployment workflow. Consider security, reliability, observability, model changes, and cost as part of engineering rather than afterthoughts.
- Expose an AI function through a small API or application endpoint
- Validate requests and return predictable responses
- Separate application logic from model-provider code
- Keep credentials and sensitive configuration out of source control
- Build a repeatable test set for common and edge-case inputs
- Measure output quality alongside latency and cost
- Log useful diagnostics without recording sensitive content unnecessarily
- Plan for model changes, provider failures, and fallback behavior
- Package the app with environment-based configuration
- Use a documented deployment or hosting workflow appropriate to the project
- Run smoke tests and review failure scenarios
- Checkpoint 7 - Deployment readiness review
Module Learning Objective
Design, build, evaluate, and present a scoped AI-powered application that responds to a clearly defined need. Demonstrate a reproducible workflow, evidence-based evaluation, responsible handling of data, and transparent documentation of system limitations.
- Choose a practical use case and define the intended user
- Write functional requirements and acceptance criteria
- Decide whether the solution needs traditional ML, an LLM, RAG, or a simpler non-AI approach
- Define privacy, safety, cost, and quality constraints
- Prepare or select an appropriate sample dataset or knowledge source
- Map the application flow, model calls, data storage, and API boundaries
- Implement the core feature and a usable interaction path
- Track code changes and document setup instructions
- Create normal, edge-case, and failure-path tests
- Evaluate outputs against agreed criteria rather than anecdotal examples
- Improve the weakest part of the system using evidence
- Document residual issues, fallback behavior, and known limitations
- Prepare a working demonstration or controlled deployment
- Present the problem, architecture, evidence, and trade-offs
- Provide a README, test examples, and an evaluation summary
- Explain what the system can and cannot reliably do, and identify future improvements
HOW YOU'LL LEARN
Understand the Model. Engineer the Application.
Build AI engineering skills through guided explanations, coding labs, model experiments, application development, and structured project reviews. Learn to question the output, test the behavior, and improve the system based on evidence.
01
Understand
Build intuition for how data, models, prompts, retrieval, and application components work together.
02
Practice
Write code, train small models, integrate AI services, and test behavior using repeatable exercises.
03
Apply
Bring the pieces together in a scoped AI application, evaluate the results, and communicate limitations clearly.
Guided coding labs. Evidence-based evaluation. Practical AI applications.
PROGRAM INFORMATION
Build Skills. Engineer Useful AI.
Develop practical AI engineering capabilities through a structured program that connects programming, model fundamentals, generative AI, application integration, testing, and project work. The information below reflects the proposed learning design for this program.
01
Learning Commitment
A structured learning plan with dedicated time for guided sessions, hands-on exercises, and project development.
- 48 learning sessions
- 3 hours per session
- 144 proposed learning hours
- Guided practice and project work
02
Core AI Engineering Skills
Build a foundation across programming, model development, generative AI, and the engineering practices used to connect them.
- Python, data, and machine learning
- Deep learning and LLM applications
- RAG and bounded agent workflows
- Evaluation and deployment fundamentals
03
Practical Milestones
Track progress through checkpoints designed to validate foundational understanding before moving to the next stage.
- Python and data workflow exercises
- Model comparison and evaluation
- LLM and RAG prototypes
- Deployment readiness review
04
Your Next Step
Use project evidence and reflection to identify strengths and plan continued development in AI engineering.
- Review model and application behavior
- Document design choices and limitations
- Prepare a project README and demo
- Identify advanced topics to explore next
FREQUENTLY ASKED QUESTIONS
Before You Build Your First AI Application.
Learn more about the AI Engineer program structure, learning expectations, technologies, and practical work.
Still have questions?
Let's discuss your current experience, learning goals, and whether this program fits your next step.
Talk to Our TeamHow long is the program?
The proposed curriculum consists of 48 sessions, with 3 hours per session, for a total of 144 learning hours.
The learning journey moves from Python and machine learning foundations through generative AI, application engineering, and a final project.
Do I need programming experience?
Some familiarity with programming concepts will help learners progress more comfortably. The curriculum starts with a structured Python ramp-up, but the program is designed to move toward technical model and application work rather than remaining a general introduction to coding.
If you are new to programming, talk to our team so we can help you assess readiness and the preparation that may be useful.
What technologies and concepts will I learn?
The curriculum covers Python, data preparation, machine learning, neural-network fundamentals, model evaluation, LLM APIs, embeddings, retrieval-augmented generation (RAG), and basic AI application deployment.
Libraries and services may vary by exercise or project. The emphasis is on understanding the workflow and engineering choices rather than mastering every framework in the market.
Will I train a large language model from scratch?
The program focuses on applying and engineering AI capabilities, including training small learning models, integrating existing models, building RAG workflows, and evaluating application behavior.
Training a production-scale foundation model from scratch is outside the proposed scope because it requires significantly different data, infrastructure, compute, and research resources.
What will I build during the program?
Learners complete small Python and model exercises, an LLM feature prototype, a RAG evaluation checkpoint, and a final AI-powered application based on a defined problem.
The final project should include a clear use case, documented implementation, example tests or evaluation evidence, and an explanation of known limitations.
How is progress evaluated?
Progress is assessed through coding exercises, repeatable model experiments, checkpoints, application tests, and a final project presentation.
Learners are encouraged to explain why an approach was selected, what the results show, and which limitations or risks remain.
YOUR NEXT STEP STARTS HERE
Ready to Engineer Your Next AI Application?
Develop practical skills across Python, machine learning, generative AI, and application engineering. Talk to our team to discuss your goals and whether this learning path is the right fit.