AI/ML Engineering 90-Day Roadmap (Intermediate Level)
This 90-day roadmap is designed for intermediate learners aiming to become proficient AI/ML Engineers. It covers essential programming foundations, core machine learning algorithms, deep learning, and practical MLOps principles for building and deploying production-ready AI systems.
Phase 1: Python & Data Science Stack Refresher
Days 1-9Solidify Python proficiency for data science and master core data manipulation and visualization libraries.
- Advanced Python (Decorators, Generators, Context Managers)
- NumPy for efficient array operations and broadcasting
- Pandas for data cleaning, transformation, and aggregation
- Matplotlib & Seaborn for exploratory data analysis and visualization
- Introduction to SciPy for scientific computing and statistics
- Implement complex data structures and algorithms in Python
- Perform advanced data cleaning and feature engineering with Pandas
- Generate various statistical plots and dashboards using Matplotlib/Seaborn
- Solve linear algebra problems using NumPy
Successfully preprocess and visualize a moderately complex dataset using Python's data science stack.
Phase 2: Core Machine Learning Concepts & Supervised Learning
Days 10-18Master key supervised learning algorithms, model evaluation techniques, and hyperparameter tuning.
- Revisiting Linear & Logistic Regression (advanced interpretation, assumptions)
- Decision Trees and Ensemble Methods (Random Forests, Gradient Boosting like XGBoost/LightGBM)
- Support Vector Machines (kernel trick, regularization)
- Model evaluation metrics (ROC-AUC, Precision-Recall, F1-score, RMSE, MAE)
- Cross-validation techniques, Bias-Variance Tradeoff, Regularization (L1, L2)
- Hyperparameter tuning strategies (Grid Search, Random Search, Bayesian Optimization)
- Implement and compare various classification and regression models on real-world datasets
- Evaluate model performance rigorously using appropriate metrics and cross-validation
- Tune hyperparameters to optimize model performance and prevent overfitting
- Interpret model predictions and feature importance using techniques like SHAP (introduction)
Build, evaluate, and interpret at least three different supervised learning models on both classification and regression tasks, demonstrating robust evaluation practices.
Phase 3: Unsupervised Learning & Feature Engineering Mastery
Days 19-27Understand unsupervised learning techniques and master advanced strategies for feature engineering and selection.
- Clustering algorithms (K-Means, DBSCAN, Hierarchical Clustering)
- Dimensionality Reduction (PCA, t-SNE, UMAP)
- Anomaly Detection techniques (Isolation Forest, One-Class SVM)
- Advanced Feature Engineering (polynomial features, interaction terms, cyclical features, time-based features, embedding categorical data)
- Feature Selection methods (Filter, Wrapper, Embedded methods)
- Imbalanced data handling techniques
- Apply various clustering algorithms to segment customer data or identify patterns
- Utilize dimensionality reduction to visualize high-dimensional data and reduce model complexity
- Develop and apply custom feature engineering pipelines to improve model performance significantly
- Implement feature selection to identify the most impactful features for a given problem
Successfully apply feature engineering and unsupervised learning techniques to extract insights and significantly improve the performance of a predictive model on a challenging dataset.
Phase 4: Review & Catch-up (Buffer Phase)Buffer Phase
Days 28-36Consolidate all learned classical ML concepts, review challenging topics, and complete any pending tasks or deeper explorations.
- Revisit foundational ML algorithms and statistical concepts
- Common pitfalls in data preprocessing and model evaluation
- Advanced model interpretation and explainability concepts
- Exploring edge cases and limitations of various algorithms
- Redo challenging assignments or projects from Phases 1-3
- Explore alternative implementations or advanced theoretical aspects of previously covered topics
- Work on a mini-project integrating diverse classical ML concepts
- Rest and recharge to prepare for deep learning.
Self-assessment confirming a solid and confident understanding of all classical machine learning concepts and practical skills acquired in the first three phases.
Phase 5: Deep Learning Fundamentals & ANNs
Days 37-45Grasp the core concepts of deep learning and build Artificial Neural Networks using a modern framework.
- Introduction to Deep Learning frameworks (TensorFlow/Keras or PyTorch)
- Artificial Neural Networks (ANNs): architecture, layers, forward/backward propagation
- Activation Functions (ReLU, Sigmoid, Tanh, Leaky ReLU) and their impact
- Loss Functions for classification and regression (Cross-Entropy, MSE)
- Optimizers (SGD, Adam, RMSprop, Adagrad) and learning rate scheduling
- Regularization techniques for ANNs (Dropout, L1/L2 regularization, Early Stopping)
- Set up a deep learning development environment (GPU acceleration)
- Implement a multi-layer perceptron for classification and regression tasks
- Experiment with different activation functions, optimizers, and network architectures
- Visualize training progress (loss, accuracy) and analyze model performance
- Apply regularization techniques to prevent overfitting in ANNs
Successfully train a fully connected neural network to achieve competitive performance on a standard dataset (e.g., MNIST, Fashion MNIST) and understand the impact of hyperparameters.
Phase 6: Advanced Deep Learning & Computer Vision/NLP Basics
Days 46-54Explore advanced neural network architectures and their application in Computer Vision and Natural Language Processing.
- Convolutional Neural Networks (CNNs) for Computer Vision (layers, filters, pooling)
- Popular CNN Architectures (LeNet, AlexNet, VGG, ResNet, Inception)
- Transfer Learning and Fine-tuning with pre-trained CNNs
- Recurrent Neural Networks (RNNs, LSTMs, GRUs) for sequential data
- Word Embeddings (Word2Vec, GloVe, FastText) and their generation
- Introduction to Transformer architecture and Attention mechanism
- Implement a CNN for image classification on datasets like CIFAR-10
- Utilize transfer learning with a pre-trained CNN (e.g., VGG16, ResNet50) for a custom image dataset
- Build an RNN/LSTM/GRU for sequence prediction tasks (e.g., sentiment analysis, time series)
- Experiment with different word embeddings and their impact on NLP tasks
- Explore a basic implementation or conceptual understanding of a Transformer model.
Successfully train a CNN for an image classification task and an RNN/LSTM for a basic NLP task, demonstrating the application of specialized deep learning architectures.
Phase 7: MLOps Fundamentals & Model Deployment
Days 55-63Understand the MLOps lifecycle, experiment tracking, model versioning, and deploy basic ML models as API services.
- Introduction to MLOps principles and challenges in production ML
- Experiment Tracking with MLflow or Weights & Biases (logging, comparing runs)
- Model Versioning and Data Versioning (DVC, Git LFS)
- Containerization with Docker (creating Dockerfiles, building images, running containers)
- Developing REST APIs for model serving (Flask or FastAPI)
- Basic CI/CD for ML (GitHub Actions for model testing/linting)
- Set up an experiment tracking system for a previous deep learning project
- Version control models and datasets using DVC and Git
- Containerize a trained machine learning model using Docker
- Develop a simple REST API endpoint to serve predictions from the containerized model
- Integrate basic CI checks for model code into a GitHub repository.
Deploy a simple machine learning model (e.g., a Scikit-learn or Keras model) as a Dockerized API endpoint that can serve predictions, with basic experiment tracking in place.
Phase 8: Review & Catch-up (Buffer Phase)Buffer Phase
Days 64-72Consolidate MLOps and deep learning knowledge, troubleshoot projects, and refine deployment strategies.
- Debugging deep learning models and MLOps pipelines
- Common challenges and best practices in model deployment
- Reviewing and comparing various MLOps tools and ecosystems
- Ethical considerations and bias in AI models
- Refactor and optimize previously deployed models for better performance or scalability
- Troubleshoot issues in training pipelines or API deployments
- Research and evaluate alternative MLOps tools or cloud services
- Document existing projects and reflect on lessons learned, focusing on improving MLOps workflows.
- Take a planned break.
Present a refined version of a deployed model, incorporating improvements based on review, and articulate common MLOps challenges and solutions.
Phase 9: Advanced MLOps & System Design
Days 73-81Learn about scalable ML systems, model monitoring, and advanced deployment strategies on cloud platforms.
- Cloud ML Platforms (AWS Sagemaker, GCP AI Platform, Azure ML) - focus on one primary platform
- Model Monitoring (data drift, concept drift, performance degradation)
- ML Pipeline Orchestration (Airflow, Kubeflow basics for scheduling tasks)
- Scalable data processing for ML (Spark, Dask basics for feature stores)
- A/B Testing and Canary Deployments for ML models
- Infrastructure as Code (Terraform/CloudFormation basics for ML infra)
- Deploy a model to a selected cloud ML platform using its native services
- Set up basic model monitoring dashboards to detect drift and performance issues
- Design a conceptual ML pipeline using orchestration tools like Airflow or Kubeflow
- Explore distributed training or inference concepts on the cloud
- Investigate methods for A/B testing different model versions in production.
Design a high-level architecture for a scalable, production-ready ML system, including deployment, monitoring, retraining, and orchestration components on a cloud platform.
Phase 10: Specialization & Capstone Project
Days 82-90Apply all learned concepts to a comprehensive end-to-end AI/ML engineering capstone project and explore a specialized AI/ML area.
- Choosing a specialization area (e.g., Reinforcement Learning, Generative AI, Causal Inference, Time Series Forecasting, Graph Neural Networks)
- Advanced model interpretability (LIME, SHAP deep dive)
- Ethical AI (fairness, transparency, accountability)
- Project management and presentation skills for AI/ML projects
- Develop a capstone project from concept to deployment, encompassing data acquisition, modeling, MLOps, and monitoring
- Present the capstone project, demonstrating technical skills and project insights
- Deep dive into a chosen advanced specialization area through research and a small practical exercise
- Refine resume/portfolio with project details and skills learned.
Complete and present a functional end-to-end AI/ML engineering project, demonstrating mastery of the roadmap's concepts and the ability to solve a real-world problem.