Data and AI

MLOps and Model Deployment

Take a working model from a notebook into a genuine, monitored production system serving real users

20h4 lessonsTier 2 hireabilityTier 2 certificate

What you'll be able to do

You can package and deploy a model behind a genuine API, choose appropriate serving infrastructure for different latency/scale needs, monitor a deployed model for both technical and data-drift problems, and design a safe rollback and retraining process.

Job titles this qualifies you for

MLOps EngineerMachine Learning EngineerAI Engineer
Market intelligence
Demand
High
Remote
70% remote roles
Nigeria salary
Not a primary local hiring category
Remote (USD)
$101,500–$155,000/year for the typical remote range (25th-75th percentile); averages reported between $128,769 and $195,475/year depending on source; specialised skills (AI agent architecture, LLM fine-tuning) can increase pay up to 25%

Lessons

Stage 1 — Foundation
1
Packaging and Serving a Model Behind an API
40 min
2
Choosing Serving Infrastructure for Genuine Needs
35 min
Stage 2 — Applied
1
Monitoring Deployed Models for Drift and Degradation
35 min
2
Safe Rollback and Retraining Processes
30 min
Tier 2 Proof Submission

MLOps and Model Deployment Portfolio

Demonstrate genuine model-packaging/serving judgement, infrastructure trade-off awareness, drift-monitoring understanding, and safe deployment/retraining discipline.

1.A Docker/serving-pattern explanation for a latency-sensitive fraud system
2.A cold-start and GPU-versus-CPU serving explanation
3.A data-drift-versus-concept-drift explanation with examples
4.A canary deployment justification and a principled retraining trigger
Create a free account to join this programJoin free →