🏷Roadmap to Mastery: Machine Learning Engineer

📖Your step-by-step journey to becoming a Machine Learning Engineer in 2025.

1️⃣ Foundations

• Mathematics: Probability, Statistics, Linear Algebra, Calculus

• Computer Science basics: Data Structures & Algorithms

2️⃣ Programming & Scripting

• Python: NumPy, Pandas, Scikit-learn

• Familiarity with Java, C++ or R (for performance-focused ML)

• SQL for data handling

3️⃣ Data Engineering Skills

• Data Cleaning & Preprocessing pipelines

• Feature Engineering & Feature Selection

• Tools: Pandas, PySpark

4️⃣ Core Machine Learning

• Regression, Classification, Clustering

• Ensemble Methods (Random Forest, XGBoost, LightGBM)

• Hyperparameter Tuning

5️⃣ Deep Learning

• Neural Networks (ANNs)

• CNNs for vision, RNNs & LSTMs for sequences

• Transformers for NLP tasks

• Frameworks: TensorFlow, PyTorch

6️⃣ Model Deployment

• REST APIs with Flask/FastAPI

• Containerisation: Docker, Kubernetes

• Edge Deployment (for mobile/IoT ML models)

7️⃣ MLOps & Automation

• Versioning: Git, DVC

• MLflow for tracking experiments

• CI/CD pipelines: Jenkins, GitHub Actions

• Cloud MLOps: AWS Sagemaker, Google Vertex AI, Azure ML

8️⃣ System Design & Scalability

• Designing ML systems for production use

• Scaling models with distributed computing (Spark, Ray)

• Optimisation for latency & cost

9️⃣ Portfolio & Career

• End-to-end ML projects (from data to deployment)

• Open-source contributions (GitHub, Hugging Face)

• Participate in Kaggle, Papers with Code

• Specialise: NLP, Computer Vision, Recommender Systems

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💡 Final Note
A Machine Learning Engineer is the bridge between research and production. By combining data skills, software engineering, and deployment expertise, you’ll bring intelligent systems to life.

📌 Next Episode Teaser
👉 Roadmap to Mastery: Data Analyst