Lesson proof
Concept, demo, checklist, lab, and assignment evidence.
ML / MLOps / Machine Learning Engineer
Machine Learning Engineer
Learn practical ML foundations, feature engineering, model training, evaluation, deployment, monitoring, governance, and MLOps workflows.
ML foundationsModel evaluationMLOps readiness
Start domain mock testPlatform-wide module outputs
Every module now feeds portfolio proof and CV readiness.
Portfolio pack
Requirement, artifact, validation, risk note, and interview story.
CV signal
Role-specific skill statement linked to a score or artifact.
Review queue
Submitted evidence can support dashboard, readiness, and career exports.
Open materials
AWS Certified Machine Learning Engineer - AssociateLesson + portfolio pack + CV evidenceMicrosoft Azure Data Scientist AssociateLesson + portfolio pack + CV evidenceMachine learning foundationsLesson + portfolio pack + CV evidenceData preparation and featuresLesson + portfolio pack + CV evidenceTraining and validationLesson + portfolio pack + CV evidenceModel evaluation metricsLesson + portfolio pack + CV evidenceModel deploymentLesson + portfolio pack + CV evidenceMLOps monitoring and governanceLesson + portfolio pack + CV evidence
Certification objective coverage
Every provider-aligned module is connected to a lesson, labs, mock questions, and implementation proof.
This is the track-level audit view for blueprint alignment. Exact exam wording should still be checked against the current official provider guide before public exam-code claims are made.
machine-learning-engineer.aws-certified-machine-learning-engineer-associate.01 / 13% weight
Apply AWS Certified Machine Learning Engineer - Associate decisions to Machine Learning Engineer scenarios
Implementation proof
- Define AWS Certified Machine Learning Engineer - Associate in plain language and explain the provider service family it belongs to.
- Show how AWS Certified Machine Learning Engineer - Associate is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
machine-learning-engineer.microsoft-azure-data-scientist-associate.02 / 13% weight
Apply Microsoft Azure Data Scientist Associate decisions to Machine Learning Engineer scenarios
Implementation proof
- Define Microsoft Azure Data Scientist Associate in plain language and explain the provider service family it belongs to.
- Show how Microsoft Azure Data Scientist Associate is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
machine-learning-engineer.machine-learning-foundations.03 / 13% weight
Apply Machine learning foundations decisions to Machine Learning Engineer scenarios
Implementation proof
- Define Machine learning foundations in plain language and explain the provider service family it belongs to.
- Show how Machine learning foundations is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
machine-learning-engineer.data-preparation-and-features.04 / 13% weight
Apply Data preparation and features decisions to Machine Learning Engineer scenarios
Implementation proof
- Define Data preparation and features in plain language and explain the provider service family it belongs to.
- Show how Data preparation and features is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
machine-learning-engineer.training-and-validation.05 / 13% weight
Apply Training and validation decisions to Machine Learning Engineer scenarios
Implementation proof
- Define Training and validation in plain language and explain the provider service family it belongs to.
- Show how Training and validation is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
machine-learning-engineer.model-evaluation-metrics.06 / 13% weight
Apply Model evaluation metrics decisions to Machine Learning Engineer scenarios
Implementation proof
- Define Model evaluation metrics in plain language and explain the provider service family it belongs to.
- Show how Model evaluation metrics is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
machine-learning-engineer.model-deployment.07 / 13% weight
Apply Model deployment decisions to Machine Learning Engineer scenarios
Implementation proof
- Define Model deployment in plain language and explain the provider service family it belongs to.
- Show how Model deployment is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
machine-learning-engineer.mlops-monitoring-and-governance.08 / 9% weight
Apply MLOps monitoring and governance decisions to Machine Learning Engineer scenarios
Implementation proof
- Define MLOps monitoring and governance in plain language and explain the provider service family it belongs to.
- Show how MLOps monitoring and governance is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
Test readiness
Mock test by domain
Practice every domain in this track with exam-style questions, answer keys, and explanations.
Open mock testMost in-demand certification materials
High-value certificates connected to this track.
Google Cloud
Google Professional Machine Learning Engineer
Needs reviewLast verified: Not verifiedNext review: Provider source review required
ML engineers designing, training, deploying, monitoring, and governing production ML systems.
Google Professional Machine Learning Engineer is mapped to platform lessons and labs, but still needs a dated official-source review.
- ML lifecycle checklist
- Evaluation metric and model monitoring drills
- Feature engineering and deployment scenario notes
AWS
AWS Certified Machine Learning Engineer - Associate
CurrentLast verified: 2026-06-24Next review: 2026-09-22
Engineers building ML workflows and AI-enabled applications on AWS.
MLA-C01 was verified against the official AWS certification page on 2026-06-24. Keep this source check on the 90-day review cadence.
AWS official-source stamp
Associate / MLA-C01
Official domain weighting
Data Preparation for Machine Learning28%
ML Model Development26%
Deployment and Orchestration of ML Workflows22%
ML Solution Monitoring, Maintenance, and Security24%
Lab focus
- Data preparation
- Training and evaluation
- Model deployment
- Monitoring, drift, rollback, and responsible AI
Readiness gates
- ML workflow lab complete
- Evaluation metrics captured
- Drift and rollback plan complete
- ML mock domains passed
- ML data preparation notebook
- Training, tuning, and evaluation checklist
- Deployment and monitoring lab notes
Certification provider connections
Connect this learning path to the official exam provider.
Google Cloud Certification
Google Professional Machine Learning Engineer
Review the certification path, confirm the exam language and delivery option, then attach the target date to the learner study plan.
Booking partner: Google Cloud exam registration partner
- Create or confirm the Google Cloud certification profile.
- Review the official exam guide, ID policy, delivery options, and reschedule rules.
- Add target exam date, booking status, renewal date, and certificate proof to the learner record.
AWS Certification
AWS Certified Machine Learning Engineer - Associate
Use the AWS Certification account to review exam guides, book exams, manage score reports, and share verified badges.
Booking partner: Pearson VUE or PSI, depending on exam and region
- Create or confirm the AWS Certification account.
- Review the official exam guide, ID policy, delivery options, and reschedule rules.
- Add target exam date, booking status, renewal date, and certificate proof to the learner record.
01 Match
Map each Daskerel track to the official provider, exam code, registration page, and verification route.
02 Prepare
Use provider objectives with Daskerel lessons, mock exams, labs, and evidence packs before booking.
03 Book
Send learners to the official scheduling partner while keeping target dates and next actions in the dashboard.
04 Verify
Capture certificate URL, badge, expiry, renewal plan, and portfolio proof after the learner passes.
Study plan
Start with supervised learning, unsupervised learning, features, labels, bias, variance, and overfitting.
Practise splitting data, training simple models, comparing metrics, and explaining tradeoffs.
Learn how production ML needs monitoring, drift detection, governance, and safe rollback.
Hands-on labs
Prepare a dataset by cleaning fields, encoding categories, and creating train/test splits.
Compare two models using accuracy, precision, recall, F1 score, and confusion matrix.
Design an MLOps flow from dataset versioning to model monitoring.
Track learning assets
Templates and revision tools for this path.
Exam blueprint checklistMachine Learning Engineer
Weekly study plannerMachine Learning Engineer
Command and service cheat sheetMachine Learning Engineer
Architecture pattern cardsMachine Learning Engineer
Flashcard revision setMachine Learning Engineer
Mock exam review sheetMachine Learning Engineer
Lab evidence templateMachine Learning Engineer
Interview story builderMachine Learning Engineer
Portfolio project rubricMachine Learning Engineer
Final readiness checklistMachine Learning Engineer
Practice questions
Why should data be split into training and test sets?
To estimate how well the model generalises to unseen data and reduce the risk of trusting memorised training performance.
What is model drift?
Model drift happens when real-world data changes over time so model performance becomes worse than expected.