AWS AuthorisedSpecialtyMLS-C01Top-paying ML Cert in MY
AWS ML Specialty MLS-C01
Build, train, deploy and operate ML solutions on AWS — SageMaker, Bedrock, AI services and MLOps — mapped to AWS MLS-C01. The senior cloud-ML credential.
⏱Duration: 4 days / 32 hrs
💻Format: Instructor-Led + ML Sandbox
🌐Delivery: On-site · Virtual · Hybrid
✅Pass rate: 90%
📅Next intake: 2 Jun 2026
🤖
SageMaker mastery
Studio, Pipelines, Feature Store, Model Registry, MLflow integration
⚡
Generative AI on AWS
Bedrock model serving, Knowledge Bases, Agents and Guardrails
🧠
ML lifecycle
Data prep, training, evaluation, deployment, monitoring and retraining
🔐
ML governance
Bias detection, model cards, audit and responsible-AI practice
What this course is
Where AWS ML stops being notebooks.
MLS-C01 is the senior AWS machine-learning credential. It validates that you can build, train, deploy, monitor and govern ML solutions on AWS at production quality — not just run a notebook.
At Nexperts, MLS-C01 is delivered on a real AWS ML sandbox with SageMaker Studio access. By day 4 you've shipped four production-shape ML products: a tabular classifier with SageMaker Pipelines, a deployment with model monitor, a Bedrock-grounded RAG application, and a model-card audit workflow.
MLS-C01 is the AWS cert that distinguishes ML platform engineers from data scientists who 'know AWS'. The exam tests platform discipline, not modelling craft.
The 2026 MLS-C01 update broadened coverage of generative AI on AWS — Bedrock, Knowledge Bases, Agents and Guardrails are now exam-relevant. We cover all four with hands-on labs.
Who should take this course
🧠
ML engineers
Already work on ML and want the formal AWS specialty credential.
📊
Data scientists
Comfortable modelling, lighter on cloud deployment. MLS is the bridge to production.
☁️
Cloud engineers
Pivoting into ML platform engineering. MLS gives the AWS-native lens.
👨💻
Backend developers
Adding ML capabilities to applications. MLS is the formal credential.
🌟
DEA holders
Natural progression. DEA + MLS is the full data-platform combo.
💼
Solutions architects
Designing AI-augmented workloads. MLS keeps you honest in technical reviews.
Prerequisites
✓ 1–2 years of professional ML or data-science experience
✓ Comfortable with Python and pandas / scikit-learn / PyTorch / TensorFlow
✓ Understanding of supervised vs unsupervised learning, evaluation metrics
✓ Basic AWS familiarity (CCP / SAA / DVA helpful but not required)
→ Don't have AWS experience? Ask about our CCP → MLS bundle.
Course Curriculum
Four domains. One ML platform toolkit.
MLS-C01 is structured into Data Engineering, Exploratory Data Analysis, Modeling, and ML Implementation & Operations. We deliver in lifecycle order — you ship a real ML product by day 2.
Hands-On AWS ML Sandbox
9 builds. Real SageMaker + Bedrock.
Every learner gets an AWS ML sandbox with SageMaker Studio and Bedrock model access pre-approved. By day 4 you've shipped four real ML products end-to-end.
01
Feature Store Build
Build a Feature Store with online + offline ingestion for a real-time use-case.
Features
02
Bias Audit
Run a Clarify bias audit on a credit-decision model. Document mitigations.
Fairness
03
HPO Cost Sprint
Fine-tune a JumpStart model with HPO under a strict cost cap. Document trade-offs.
Modelling
04
SageMaker Pipelines
Build a 5-step Pipeline with conditional branching and model approval gate.
MLOps
05
Model Monitor + Drift
Deploy a model with Model Monitor. Inject drift. Trigger automated retrain.
Monitoring
06
Bedrock RAG Build
Build a grounded GenAI assistant with Bedrock + Knowledge Bases on your corpus.
GenAI
07
Bedrock Agent
Build a Bedrock Agent that calls 3 tools and a Knowledge Base.
GenAI
08
Multi-Model Endpoint
Deploy a multi-model endpoint and validate cost / latency vs single-model.
Inference
09
Model Card Audit
Produce a model card for a deployed model. Pass an audit-readiness review.
Governance
+ 13 micro-tasks across SageMaker SDK, boto3 and Bedrock invocation patterns.
Exam Information
One scenario-heavy exam. Modelling + platform mix.
MLS-C01 is 65 questions over 180 minutes. The exam blends modelling judgement (algorithm choice, evaluation metric, bias) with AWS-platform decisions (SageMaker, deployment mode, monitoring). Most candidates fail the platform half.
Common gotchasConfusing F1 vs ROC-AUC; mode selection trade-offs
StrategyRead the metric requirement first
OutcomeDecision score uplift averages +22%
WalkthroughPast scenario archive provided
Our 3-Mock Programme
01
Diagnostic Mock
End of day 1. Maps weak knowledge areas. Average score: 56%.
02
Decision-Heavy Mock
Mid-course. 60% algorithm + platform decisions. Average score: 70%.
03
Final Clearance
Full timed simulation. 80%+ before we book. Average score: 83%.
0%
Pass Rate
90% of our MLS candidates pass on first attempt.
The AWS global first-attempt rate for MLS-C01 sits around 62%. We hit 90% by drilling algorithm + platform decisions on a real ML sandbox and gating booking on a clearance mock.
Real ML sandbox + BedrockDecision drill90% first attemptFree retake voucherGenAI track included
Why our pass rate is 90%
Industry average: ~62%
Most candidates know the modelling but freeze on AWS-platform decisions. They pick deployment modes by feature recall, not by constraint match.
Nexperts: 90%
We drill platform decisions on the whiteboard. We deploy real models. We run real Bedrock RAG. By exam day, the platform decisions are reflexive.
Your AWS ML Path
MLS pairs with DEA and SAP.
MLS stacks naturally with DEA (data engineering) for full-stack data + ML, or with SAP (Solutions Architect Pro) for breadth. Strong second cert: Databricks ML Associate.
Before this
CCP / SAA + ML experience
Helpful but not strictly required. Most candidates have associate AWS + data-science work.
Expected salary range after MLS: RM 13,000 – RM 22,000/month for ML platform engineer and senior data-scientist roles in MY MNCs.
Student Reviews
What our MLS engineers say.
4.8
★★★★★
82 reviews
5★
83%
4★
14%
3★
3%
★★★★★
"Bedrock RAG and Agent labs were the most valuable single labs of any cert I've taken. Took the patterns straight back to my company and shipped two GenAI features."
SR
Sasidaran Ramachandran
Senior ML Engineer · Carsome
✓ Passed first attempt · 826/1000
★★★★★
"Came in as a data scientist who avoided AWS. MLS with Nexperts was the bridge — I now confidently own deployment and monitoring at work, not just modelling."
FA
Fatimah Akram
ML Engineer · Maybank Innovation
✓ Passed first attempt
★★★★
"SageMaker Pipelines + Model Monitor lab solved a problem we'd been struggling with for 6 months. Best ROI I've ever had on a course fee."
BR
Brendan Robertson
ML Platform Engineer · Standard Chartered GBS
✓ Passed first attempt · 802/1000
★★★★★
"Mock-3 simulation was tougher than the real exam. The decision drill makes you think like AWS thinks. Career-defining course."
NK
Nazreen Kassim
Senior Data Scientist · PETRONAS Digital
✓ Passed first attempt · 858/1000
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