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AI / ML Engineer
Build systems that learn, reason, and improve from data
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Les compétences que vous en tirerez
Les emplois auxquels il mène
- ML Engineer
- Data Scientist
- AI Engineer
- Research Engineer
- MLOps Engineer
Avant de commencer
- ✓ Solid Python programming skills
- ✓ High school level statistics (mean, variance, distributions)
8 missions jusqu’à AI / ML Engineer
4 mondes, 8 missions. Touchez un cercle pour l’ouvrir ; celui qui brille est le suivant.
ML is applied mathematics. You need Python fluency and a working grasp of linear algebra, probability, and calculus.
Learn the canonical ML algorithms and the workflow of a real ML project end-to-end using Scikit-Learn.
Learn the architectures powering modern AI: CNNs for vision, and the architectures that evolved into modern LLMs.
Deploying a model is only the beginning. Complete the journey by focusing on ML deployment, monitoring, and modern GenAI architectures.
En savoir plus sur ce métier
Le métier, le salaire là où vous vivez, les questions fréquentes, et où ce métier peut vous mener ensuite.
Le métier
Ce que vous feriez
- Clean and process large sets of data using statistical software
- Build, test and validate models so they predict outcomes accurately
- Compare models using performance measures such as loss functions
- Write code in programming languages to run analyses and experiments
- Read research papers and conference work to follow new methods
- Present results and recommend data-driven solutions to stakeholders
Où vous travailleriez
- Publishing, broadcasting and content providers
- Insurance companies
- Computer systems design firms
- Company head offices
Toutes les étapes en détail
Le texte complet de chaque étape, à lire ou à imprimer. Avancez étape par étape dans le parcours ci-dessus.
Python, Math & Statistics 2 étapes
Python for High-Performance Math
Unlike standard software engineering, ML heavily relies on mathematical operations on large multi-dimensional arrays. Start by mastering Python and its foundational math libraries.
Tâche concrète: Given a large CSV of real estate data, write a pure NumPy and Pandas script (NO for-loops) that cleans missing values using column medians, normalizes all numerical columns via Min-Max scaling, and computes the covariance matrix of all numerical features.
Pourquoi c’est important: In ML, running a standard Python for-loop over 10 million rows will crash or take hours. Vectorized NumPy operations do it in milliseconds. This is the difference between a failing pipeline and a scalable one.
Liste de contrôle
- Gain advanced proficiency in Python: generators, decorators, matrix operations
- Master NumPy: broadcasting, vectorization, array slicing, dot products without loops
- Master Pandas: data wrangling, missing data imputation, handling time series
- Master Matplotlib & Seaborn: effectively plotting relationships to visualize distributions
Linear Algebra, Calculus & Probability
You need to understand the theoretical engines of ML algorithms. Black boxes are dangerous in production.
Tâche concrète: Watch the 3Blue1Brown Linear Algebra series. Then, write out the chain rule calculation by hand to find the gradient of a toy neural network with exactly one hidden layer containing 2 neurons.
Pourquoi c’est important: When a neural network fails to converge (loss doesn't decrease), you cannot debug it without understanding gradients. When your model is biased, you cannot fix it without statistics.
Liste de contrôle
- Linear Algebra: Review scalars, vectors, matrices, dot products, matrix multiplication
- Linear Algebra: Understand vector spaces, orthogonality, and eigenvectors
- Calculus: Understand partial derivatives, gradients, and the chain rule
- Probability: Understand Bayes' theorem, normal distributions, expectation, and variance
- Statistics: Understand sampling, hypothesis testing, and the bias-variance tradeoff
Machine Learning Fundamentals 2 étapes
Supervised & Unsupervised Learning
Before Deep Learning, you must master classical ML. These models are often faster, cheaper, and perfectly adequate for tabular data.
Tâche concrète: Download a dataset representing credit card defaults. Train a Logistic Regression model and a Random Forest. Generate a classification report. Since credit defaults are a rare class (imbalanced data), identify why Accuracy is a terrible metric here, and optimize the model for Recall instead.
Pourquoi c’est important: Most enterprise ML is tabular data hitting XGBoost—not LLMs. If you jump straight to deep learning without mastering tree-based models, you will overcomplicate simple problems.
Liste de contrôle
- Understand supervised learning techniques: Linear regression, Logistic regression, SVMs, Decision Trees
- Understand unsupervised learning techniques: K-means clustering, Principal Component Analysis (PCA)
- Learn Ensemble methods: Random Forests and Gradient Boosted Trees (XGBoost / LightGBM)
- Understand evaluation metrics: Confusion Matrices, Precision vs Recall, F1 Score, ROC-AUC, RMSE
Feature Engineering & Pipelines
Models are only as good as the data fed into them. 'Garbage in, garbage out.'
Tâche concrète: Create a Scikit-Learn Pipeline object that chains together a SimpleImputer (for missing data), a StandardScaler (for scaling), a OneHotEncoder (for categories), and an XGBoost classifier. Cross-validate the entire pipeline object, not just the model.
Pourquoi c’est important: Preventing data leakage and writing robust preprocessing pipelines ensures your model performs exactly the same way in production as it did in your Jupyter Notebook.
Liste de contrôle
- Handle categorical variables (One-Hot Encoding, Label Encoding, Embedding)
- Implement Feature Scaling (StandardScaler, MinMaxScaler)
- Perform aggressive Feature Selection to drop unhelpful dimensions
- Understand data leakage (when future info bleeds into training data) and how to prevent it
- Build reusable Scikit-Learn Pipelines that bundle preprocessing and modeling
Deep Learning & Neural Networks 2 étapes
Neural Networks & PyTorch
Step away from Scikit-Learn and build networks from scratch using PyTorch.
Tâche concrète: Using PyTorch, build a fully connected Multi-Layer Perceptron (MLP). Write the custom training loop by hand (zeroing gradients, backward pass, optimizer step). Train it on the MNIST handwritten digit dataset until you hit 95%+ accuracy.
Pourquoi c’est important: PyTorch is the undisputed king of AI research and production. Knowing how to write a training loop from scratch gives you full control over complex custom architectures.
Liste de contrôle
- Understand network architecture: perceptrons, hidden layers, weights, biases
- Learn activation functions (ReLU, Sigmoid, Softmax) and loss functions (CrossEntropy, MSE)
- Understand the forward pass and backpropagation (gradient descent optimizers like Adam or SGD)
- Learn to build custom Dataset and DataLoader classes in PyTorch
- Grasp regularization to prevent overfitting in deep networks (Dropout, Weight Decay)
CNNs, RNNs, and Transformers
Different data modalities require specific network topologies.
Tâche concrète: Use the Hugging Face `transformers` library. Download a pre-trained BERT model. Write a script that fine-tunes it on a custom dataset of 1,000 specific customer support tickets to route them into categorized buckets.
Pourquoi c’est important: No one trains models from scratch anymore. The industry is defined by Transfer Learning—taking foundational models created by OpenAI/Google/Meta and adapting them to hyper-specific enterprise tasks.
Liste de contrôle
- Understand Convolutional Neural Networks (CNNs): filters, pooling, and strides for Image data
- Understand Recurrent Neural Networks (RNNs) and LSTMs for sequential / time-series data
- Understand the Transformer Architecture, Self-Attention mechanisms, and positional encoding
- Learn Transfer Learning: take a massive pre-trained model (like ResNet or BERT) and fine-tune its final layers
- Use the Hugging Face API to load and utilize state-of-the-art transformer models
MLOps & Production (RAG, GenAI) 2 étapes
Model Serving & Containerization
A Jupyter Notebook is not a software product. You need to turn your model into a robust microservice.
Tâche concrète: Take an image classification CNN you built. Wrap it in a FastAPI server where users can POST an image and receive a JSON prediction. Write a Dockerfile for it. Ensure the Docker container boots cleanly and serves predictions locally.
Pourquoi c’est important: Software engineers and product managers can only interface with your ML work if it's exposed as a sterile, reliable API. Containerization bridges the gap.
Liste de contrôle
- Serialize and save models in standard formats (ONNX, TorchScript, Pickle)
- Wrap algorithms in a high-performance REST API using FastAPI
- Write a Dockerfile that bundles the API, the model weights, and the Python dependencies
- Understand GPU resource allocation inside Docker containers
- Deploy the containerized model to a cloud endpoint (AWS SageMaker, Google Vertex AI, or raw EC2)
Monitoring, Pipelines, and RAG
Models degrade over time. Furthermore, modern AI engineering involves chaining LLMs alongside search systems.
Tâche concrète: Build a RAG app. Spin up a local Vector Database (like ChromaDB). Chunk and ingest a PDF document (e.g., an employee handbook) into embeddings. Write an API that takes a user query, performs a cosine-similarity search against the DB, retrieves chunks, and passes them to the OpenAI API (or a local LLaMa model) as context to answer the question.
Pourquoi c’est important: Pure model training is shifting towards 'Compound AI Systems'. Knowing how to orchestrate vector databases, retrieval logic, and foundation models is exactly what 'AI Engineers' do today.
Liste de contrôle
- Use MLflow or Weights & Biases to track experiments, hyperparameters, and model versions
- Understand Data Drift and Concept Drift: triggering retraining when production data diverges from training data
- Build automated CI/CD pipelines for ML models (testing inference latency)
- Master Retrieval-Augmented Generation (RAG): combining Vector Databases (Pinecone/Weaviate) with LLMs
- Learn basic LangChain or LlamaIndex workflows
Questions fréquentes : AI / ML Engineer
Des réponses claires sur la durée, les prérequis, les salaires et les méthodes d’étude.
Combien de temps faut-il pour devenir AI / ML Engineer ?
Ce parcours estime 8–14 months. Il compte 4 phases et 8 étapes, de « Python, Math & Statistics » à « MLOps & Production (RAG, GenAI) ».
Comment devenir AI / ML Engineer ?
Suivez les 4 phases dans l’ordre, en commençant par la phase 1, « Python, Math & Statistics ». Votre première tâche : Given a large CSV of real estate data, write a pure NumPy and Pandas script (NO for-loops) that cleans missing values using column medians, normalizes all numerical columns via Min-Max scaling, and computes the…
Combien gagne un AI / ML Engineer ?
Le salaire dépend du pays. L’encadré salaire de cette page indique l’estimation de DraftMyCareer, en monnaie locale, pour le pays choisi dans le menu.
De quoi ai-je besoin avant de commencer ?
Ce parcours indique : Solid Python programming skills; High school level statistics (mean, variance, distributions).
De quelles compétences un AI / ML Engineer a-t-il besoin ?
Ce parcours couvre : Python, Statistics, Scikit-learn, PyTorch, MLOps, SQL, Data Preprocessing.
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