Advanced• 16 Weeks (12-14 hrs/wk)
ML Engineer
Deploy, monitor, and scale machine learning models in production environments.
7 Courses
Structured Curricula
6 Projects
Production Capstones
14 Challenges
Coding Katas
Certificate
Accredited Credential
Active Progress Tracking
1 of 6 Stages Completed (17%)
WHERE AM I?
Stage 2: Classical ML & Feature PipelinesIn Progress
scikit-learn pipelines, cross-validation, data drift
WHAT COMES NEXT?
Stage 3: Deep Neural Networks with PyTorchNext Up
Custom autograd functions, distributed training, AMP
Roadmap Progression
Follow this linear, step-by-step engineering roadmap to complete the track.
Step 1Python & Numerical SystemsCompleted
CUDA basics, memory efficiency, tensor layouts
Step 2Classical ML & Feature PipelinesActive
scikit-learn pipelines, cross-validation, data drift
Step 3Deep Neural Networks with PyTorch
Custom autograd functions, distributed training, AMP
Step 4Model Quantization & Optimization
TensorRT, ONNX runtime, pruning, INT8 quantization
Step 5MLOps, CI/CD & Model Serving
Triton server, MLflow registry, latency SLAs
Step 6Production Capstones
Sub-10ms inference pipeline with real-time logging
Target Career Outcome
View Matching RolesMachine Learning Engineer, MLOps Specialist, Inference Architect
Credential: ASCI Certified ML Engineer