Who is an AI / ML Engineer,
An AI engineer is a professional who designs, builds, and implements artificial intelligence (AI) models and applications. They work with various AI technologies, including machine learning (ML), deep learning, natural language processing (NLP), and computer vision, to create systems that can perform tasks that usually require human intelligence. AI engineers collaborate with data scientists, software developers, and domain experts to develop solutions that automate processes, solve complex problems, or enhance decision-making.
Key Responsibilities of an AI Engineer,
- Data Collection and Preprocessing
- AI Model Development and Training
- Application Development
- Model Deployment and Integration
- Performance Monitoring and Maintenance
🔥AI / ML Engineer Roadmap 🔥
1. Mathematical Foundations 🖊️
Mathematics is the backbone of artificial intelligence and machine learning. AI engineers need strong mathematical skills to understand and optimize algorithms.
- Linear Algebra
- Calculus
- Probability and Statistics
📚 Resources:
- Khan Academy – Calculus
- Khan Academy – Linear Algebra
- Khan Academy – Probability and Statistics
- Essential Mathematics for Machine Learning
2. Programming Fundamentals 💻
Programming is crucial for implementing AI solutions, and AI engineers must be proficient in various programming skills.
- Variables and Data Types
- Data Structures (List, Tuple, Set, Dictionary in Python)
- Control Flow (if-else, loops)
- Functions
- Object Oriented Programming
Programming fundamentals are crucial for developing AI models, automating data workflows, and creating scalable applications.
📚 Resources:
3. Version Control (Git and GitHub) ⏳
Version control, typically using Git, is critical for collaborative work and experiment tracking in AI projects.
- Git and GitHub
- Git Commands
- Working with Branches
📚 Resources:
4. Databases 🛜
AI engineers work with massive amounts of data stored in databases, so database knowledge is essential.
- Relational Databases (MySQL, PostgreSQL)
- NoSQL Databases (MongoDB)
- Vector Databases (Chroma, Pinecone)
- Graph Databases (Neo4j)
📚 Resources:
- MySQL Database – Programming with Mosh
- NoSQL Databases – freeCodeCamp.org
- Vector Databases – CodePRO LK
- Graph Databases – CodePRO LK
5. Data Manipulation and Analysis Libraries 📊
AI engineers rely on specialized libraries to process and analyze data. Those libraries streamline data handling, enabling AI engineers to prepare data for machine learning in a quick and efficient manner.
- NumPy
- Pandas
- Matplotlib, Seaborn
📚 Resources:
- NumPy, Matplotlib, Pandas – CodePRO LK
- NumPy – tutorialspoint
- Matplotlib – tutorialspoint
- Pandas – tutorialspoint
- Seaborn – tutorialspoint
6. Machine Learning 💡
Machine learning (ML) is the core discipline of AI engineering, encompassing various techniques. Those skills allow AI engineers to develop models that can make predictions, classify data, detect anomalies, and provide insights.
- Preprocessing Techniques
- Data Cleaning (Handling missing values, Outliers)
- Data Transformation (Feature scaling, Encoding)
- Feature Engineering (Feature selection, Feature extraction)
- Algorithms
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
- Regression (Linear Regression, Decision Tree, Random Forest)
- Classification (Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Random Forest)
- Clustering (K-Means Clustering, Hierarchical Clustering, DBSCAN)
- Ensemble Models (Random Forest, Gradient Boosting, XGBoost)
- Model Evaluation Techniques
- Regression (Mean Squared Error, Mean Absolute Error)
- Classification (Accuracy, Precision, Recall, F1-score, ROC Curve, Confusion Matrix)
- Clustering (Silhouette score)
- Hyperparameter Tuning (GridSearchCV, RandomSearchCV)
📚 Resources:
- Machine Learning – CodePRO LK
- Regression Project – Laptop Price Prediction – CodePRO LK
- Classification Project – Sentiment Analysis – CodePRO LK
7. Deep Learning 🌐
Deep learning (DL) is a subset of ML that powers complex AI applications through neural networks. Deep learning skills empower AI engineers to create advanced models for tasks involving complex patterns, making them invaluable in applications like vision and language understanding.
- Neural Networks
- Convolutional Neural Networks
- Transfer Learning
- Object Detection (YOLO)
- Recurrent Neural Networks (LSTM, GRU)
- Transformer Networks
- Encoder, Decoder, Attention
- Python Deep Learning Frameworks (TensorFlow, Pytorch)
📚 Resources:
- Deep Learning – CodePRO LK
- Convolution Neural Networks – CodePRO LK
- Recurrent Neural Networks – CodePRO LK
- Transformer Neural Networks – CodePRO LK
- Tensorflow
- Pytorch
8. MLOps 🖥️
MLOps (Machine Learning Operations) ensures that models are reliably deployed and maintained in production. MLOps is critical for moving models from development to production, ensuring they are scalable, efficient, and maintainable over time.
- Server-side development (Flask, FastAPI)
- Docker and Kubernetes
- Model Deployment
- AWS Sage maker, Azure ML Studio
- Model Monitoring
- DVC (Data Versioning Control)
📚 Resources:
9. Generative AI 🤖
Generative AI involves creating models that can generate new content, providing innovative solutions across many domains. Generative AI skills enable AI engineers to push the boundaries of creativity and automation, developing applications that can synthesize realistic and novel media, assist with design tasks, or even produce synthetic training data.
- Large Language Models
- Large Vision Models
- LLM Application framework (LangChain, LlamaIndex)
- Prompt Engineering
- RAG Architecture
- Agents and Tools
- LLM Quantization
- LLM Finetuning
- LLM Evaluation
📚 Resources:
