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AI/ML OpsPractitioner

MLOps with SageMaker

From notebooks to pipelines in production

Weeks
10
Lessons
48
Browser labs
12
Students
—
Rating
—
Tuition · INR
₹8,990
or 3× EMI · UPI accepted
Audit free
Free preview · 2 lessons

Try before you pay.

Two full lessons from MLOps with SageMaker — exact topics, hands-on lab pairings, same depth as the paid course. Watch the videos free; sign up to access labs + the rest of the curriculum.

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Lesson 0116 min

Why most ML projects never reach production

Industry data: 60–80% of ML projects fail to reach production. The reasons aren't model accuracy — they're data pipelines, monitoring, evals, and ownership. We frame the boring 70% no one wants to teach.

What this lesson teaches
  • · The 'POC purgatory' anti-pattern and how to escape it
  • · Why a SageMaker notebook is not a production system
  • · Eval suites in CI: the discipline that separates research from product
  • · Drift detection: feature drift vs concept drift vs label drift
  • · Who owns a deployed model — the org chart trap
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Lesson 0228 min

Your first SageMaker pipeline — train → register → deploy

Hands-on walkthrough of a real SageMaker Pipeline that trains on S3 data, registers a versioned model in the Model Registry, and deploys to a real-time endpoint with shadow traffic. The pattern we ship for clients.

What this lesson teaches
  • · SageMaker Pipelines: steps, conditions, fan-out
  • · Model Registry: versioning, approval workflow, rollback
  • · Real-time vs batch vs serverless inference — when each fits
  • · Shadow / canary / blue-green deployment patterns for models
  • · Cost gotchas: instance idle time, multi-model endpoints
● Paired lab

Build a complete SageMaker Pipeline (XGBoost on UCI dataset) in your AWS sandbox.

Sign up free to access lab + sandbox →

These are 2 of 48 lessons. Subscribe to @computerpathshala654 for new lessons + course launches. The full 46 remaining lessons are included with cohort enrolment, with a 7-day money-back guarantee.

What you’ll build

10 capstones. Reviewed by senior engineers.

01Build a VPC + EC2 from scratch● Lab
02Containerize a Node app & push to ECR● Lab
03Deploy to EKS with Helm● Lab
04Terraform a 3-tier app● Lab
05GitHub Actions: build → push → deploy● Lab
06Blue-green deploy with Route53● Lab
07Set up Prometheus + Grafana on EKS● Lab
08SLO-based alerting● Lab
09Chaos test with AWS FIS● Lab
10Cost-optimize an EC2 fleet● Lab
Curriculum

48 lessons across 10 weeks

Week 01From notebook to production: the gap5 lessons
  • 01Why most ML projects never reach productionvideo16mFREE
  • 02Your first SageMaker pipeline — train → register → deployvideo28mFREE
  • 03The 'boring 70%' map — data, eval, monitoring, ownershipvideo18m
  • 04Reading: real client MLOps audit (anonymised)reading25m
  • 05Lab: spin up a SageMaker domain + user profilelab60m
Week 02SageMaker Pipelines deep dive4 lessons
  • 06Steps, conditions, fan-out — the building blocksvideo22m
  • 07Caching: when SageMaker reuses a step output (and when it doesn't)video18m
  • 08Parameters + ParameterStrings vs hardcoded valuesvideo16m
  • 09Lab: build a 5-step pipeline with conditional deploymentlab120m
Week 03Data + feature engineering at scale4 lessons
  • 10Processing jobs: SageMaker Processing vs Glue vs EMRvideo22m
  • 11Feature Store: when it earns its complexity (and when not)video24m
  • 12Data validation in CI: Great Expectations / Panderavideo20m
  • 13Lab: process raw S3 data → Feature Store → training joblab120m
Week 04Training jobs + experiment tracking5 lessons
  • 14SageMaker training jobs: instance types, spot, distributedvideo24m
  • 15Bring-your-own-container vs pre-built frameworksvideo18m
  • 16Experiments tracking: SageMaker Experiments vs MLflow vs W&Bvideo22m
  • 17Hyperparameter tuning: Bayesian vs grid vs randomvideo20m
  • 18Lab: train XGBoost with hyperparameter tuning + W&Blab120m
Week 05Model registry + governance4 lessons
  • 19Model versioning + approval workflowvideo20m
  • 20Model cards: documenting bias, training data, intended usevideo18m
  • 21Lineage: from raw data → features → trained model → deploymentvideo22m
  • 22Lab: register model + approve via Lambda automationlab90m
Week 06Deployment patterns5 lessons
  • 23Real-time vs batch vs serverless — when each fitsvideo24m
  • 24Multi-model endpoints + multi-container endpointsvideo20m
  • 25Shadow / canary / blue-green for MLvideo26m
  • 26A/B testing inference variants with weighted endpointsvideo22m
  • 27Lab: shadow-deploy a new model version + compare predictionslab150m
Week 07Monitoring + drift5 lessons
  • 28Feature drift: KS-test, PSI, what each really meansvideo22m
  • 29Concept drift: ground-truth lag and how to detect anywayvideo20m
  • 30Data quality monitoring with Model Monitorvideo24m
  • 31Custom monitoring with CloudWatch + Lambdavideo18m
  • 32Lab: set up drift alerts for a deployed modellab90m
Week 08Cost optimisation for ML4 lessons
  • 33Training cost: spot, savings plans, instance right-sizingvideo20m
  • 34Inference cost: idle endpoint trap + multi-model endpointsvideo22m
  • 35Serverless inference: when it's 10× cheaper than always-onvideo18m
  • 36Lab: cost-optimisation audit on a sample SageMaker workloadlab90m
Week 09Retraining pipelines4 lessons
  • 37Triggered retraining: drift-detected vs schedule vs new datavideo22m
  • 38Automated promotion: from registered to productionvideo20m
  • 39Eval gating: don't auto-promote a worse modelvideo24m
  • 40Lab: build an end-to-end retraining loop with EventBridgelab180m
Week 10Capstone + job readiness8 lessons
  • 41Production-quality capstone — what 'good' looks likereading30m
  • 42MLOps Engineer interview prep — system-design questionsvideo28m
  • 43Live capstone code review with a senior ML engineervideo60m
  • 44Capstone: end-to-end churn prediction system with full MLOpsproject600m
  • 45Common MLOps anti-patterns I've seen in client auditsvideo24m
  • 46What hiring managers actually ask in MLOps interviewsvideo22m
  • 47When ML is the wrong answer — saying no professionallyvideo16m
  • 48Cohort wrap-up + KS hiring pipeline introductionvideo30m
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