HIGAET MLOps
Learn pipelines, registries, and deployment automation to operate reliable machine learning systems with monitoring and incident response through HIGAET Practical Training.
Duration
8 weeks · 6-8 hours/week
Level
Advanced
Delivery
Online
Status
Open for enrollment
Why this technology matters.
MLOps is the discipline of operating machine learning systems reliably with pipelines, registries, deployment automation, monitoring, and incident response. It matters now because many models work in a notebook but fail silently once deployed.
Teams use it to build automated training pipelines with versioned data, code, and artifacts, manage model registries and promotion across staging and production, and run CI and CD for ML services. Automation alone does not fix bad models or unclear ownership: pipelines do not rescue poor validation, and dashboards do not help without rollback criteria and someone on call.
By the end you will be able to build automated training pipelines with versioned artifacts, registry and promotion workflows across staging and production, and monitored ML services with drift detection, quality gates, and rollback plans.
Why this course exists
The gap is between a hand-deployed model file and a production system that retrains, releases, and recovers safely. This course teaches the arc from versioned training to registry promotion to automated release to monitored operations: Model to Pipeline to Deployment to Evaluation to Production with rollback.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and ML model basics
- Familiarity with Git and CI concepts
- Basic cloud and container awareness
Technologies & tools
Skills you'll gain
A 8 weeks arc, module by module.
- Module 01
Module 01 — Foundations: MLOps Lifecycle, Environments, and Production Readiness
- Module 02
Module 02 — Core: Experiment Tracking, Versioning, and Reproducible Training
- Module 03
Module 03 — Core: Model Registries, Approval Gates, and Release Management
- Module 04
Module 04 — Engineering: CI and CD Pipelines for Machine Learning Services
- Module 05
Module 05 — Engineering: Feature Stores, Data Versioning, and Training Automation
- Module 06
Module 06 — Advanced: Deployment Strategies, Scaling, and Inference Management
- Module 07
Module 07 — Production: Monitoring, Drift Detection, Alerting, and Incident Response
- Module 08
Module 08 — Capstone: Production MLOps Pipeline with Registry and Monitoring
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build automated training pipelines with versioned data, code, and artifacts
- Design model registries and promotion workflows across staging and production
- Develop CI and CD workflows for testing, packaging, and releasing ML services
- Evaluate production models with drift detection, quality gates, and rollback criteria
- Automate retraining triggers, batch scoring, and endpoint deployment routines
- Optimize inference cost, latency, and resource use for serving workloads
- Integrate feature stores and observability tooling into ML platforms
- Secure model services with access controls, audit trails, and environment isolation
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Versioned training pipeline
- Project 02
Model registry with promotion workflow
- Project 03
CI/CD release for ML service
- Project 04
Drift detection and rollback setup
- Capstone
Monitored production ML pipeline with incident response
Speak the language first.
- Training Pipelines
- Automated workflows that prepare data, train models, and package versioned artifacts.
- Model Registry
- A versioned catalog that tracks model artifacts, metadata, and promotion status.
- Staging and Production Promotion
- Gated steps that move a model from testing to live serving after quality checks pass.
- CI for ML
- Automated tests for data, features, and models that run on every code change.
- CD for ML Services
- Automated packaging and release steps that deploy approved models as services.
- Drift Detection
- Monitoring that flags when live data or predictions shift away from training baselines.
- Quality Gates
- Threshold checks a model must pass before promotion or continued serving.
- Rollback Strategy
- Plans and automation to revert to a prior model version when production quality drops.
- Incident Response
- Defined steps to detect, triage, and resolve ML production failures.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Production metrics drift after deployment
Compare live feature distributions to training baselines, confirm the drift source, then retrain or roll back per quality gates.
Unregistered model artifact deployed to production
Halt promotion, register the artifact with data and code versions, and require registry-gated releases going forward.
Training pipeline succeeds locally but fails in automation
Pin dependencies, version data snapshots, and reproduce with pipeline logs before re-enabling scheduled runs.
Failed deployment leaves a bad model serving traffic
Trigger the rollback to the last approved registry version, verify health checks, then investigate with deployment logs.
Stale model serves because retraining never triggered
Add schedule or drift-based triggers with freshness monitors, then backfill and verify the new version before promotion.
Before you move on, you should be able to
- Build automated training pipelines with versioned data, code, and artifacts
- Design model registries and promotion workflows across staging and production
- Develop CI and CD workflows for testing, packaging, and releasing ML services
- Evaluate production models with drift detection and quality gates
- Deploy rollback plans when production quality drops
- Respond to ML incidents with monitoring and documented follow-up
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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View CourseReady to start HIGAET MLOps?
A 8 weeks course — Data & Machine Learning.