Resources

ML Engineering Resources
Programming Language: Python; Node.js --> Typescript

Cloud Computing: Serverless Architecture --> AWS Lambda; Storage; Model Management; GPU Management; Cloud Network

Container Platform: Docker; Kubernetes

Database: SQL --> ORM, DAL; RDBMS --> MySQL; NoSQL --> Document --> MongoDB, Key-Value --> Redis

Machine Learning:
Supervised Learning --> Labeling Tool --> Active Learning; Classification; Regression; Object Detection
Unsupervised Learning --> Dimension Reduction
Reinforcement Learning --> Bandits Recommendation; RL in Production --> Scalable RL Agents

Serialization: Protobuf; JSON

Programming Techniques: Dependency Injection; Dump Analysis; Functional Programming

Performance Optimization: Distributed Training --> Horovod; Data Pipeline; Weight Compression --> Float16 Compression

ML Frameworks: OpenCV; PyTorch; TensorFlow

AutoML: Microsoft NNI

MLOps: Model Serving --> Batched; Real-Time; Scheduling; Data Preprocessing; Data Validation; Model Training --> Distributed Training; Model Validation; Model Deployment; Experiment Management; explainable AI; Monitoring; TFX

Multithreading/processing: Synchronization --> Mutex --> Semophore; Parallel Programming; Task-based

Mathematics: Linear Algebra; Probability & Stats --> Bayesian Stats; Optimization Theory