Career Roadmap
AWS Machine Learning Engineer: Zero to Hero
This roadmap guides you from AI fundamentals to production ML engineering on AWS. You will build end-to-end ML pipelines with SageMaker, implement MLOps practices, manage feature stores, and deploy scalable inference infrastructure. MLA-C01 validates ML engineering skills. DEA-C01 adds data engineering depth. AIF-C01 is optional for experienced practitioners. Use ExamOS practice quizzes to track progress at every stage.
Who is this roadmap for?
This roadmap is designed for Software Engineers transitioning into ML engineering who already write Python, as well as Data Engineers building ML pipelines who want to expand into MLOps. Additionally, it is tailored for Cloud Engineers working with AWS who want to add ML to their skillset, as well as Solutions Architects designing ML workloads who require hands-on credential validation.
Skills You'll Develop
Build production ML pipelines with pandas, numpy, boto3, and PySpark
Design and deploy ML workflows with Feature Store, Pipelines, Model Registry, and endpoints
Build ingestion and transformation pipelines with Glue, EMR, and Kinesis
Monitor drift, manage model lineage, and implement security controls for ML workloads
Optimize endpoints for cost, latency, and throughput with multi-model and serverless inference
Target Roles in this Roadmap
- Machine Learning Engineer: Builds and deploys ML systems on SageMaker
- MLOps Engineer: Designs CI/CD pipelines, model monitoring, and governance
- Data Engineer (ML Focus): Builds data pipelines for ML training and inference
- ML Platform Engineer: Builds internal ML platforms and tooling
Typical Employer Categories
- Financial Services: Fraud detection, risk modeling, algorithmic trading
- Healthcare & Life Sciences: Medical imaging, drug discovery, clinical NLP
- Technology & Software Firms: Recommendation engines, personalization, internal ML tools
- Retail & E-Commerce: Demand forecasting, supply chain optimization
- Consulting & Professional Services: Building ML solutions for enterprise clients
The Certification Path
Recommended Path
| Cert | When | Why |
|---|---|---|
| AWS AIF-C01 (optional) | Month 1-2 | AI and ML fundamentals. Optional if you already design or deploy ML workloads on AWS. |
| AWS MLA-C01 | Month 3-8 | The core ML engineering credential. Validates SageMaker MLOps and model deployment. |
| AWS DEA-C01 | Month 8-12 | Data engineering depth. Builds the data pipeline skills that support ML workloads at scale. |
Specialization Path
| Cert | When | Why |
|---|---|---|
| AWS AIP-C01 | Month 10-14 | Generative AI depth. Add if your work includes Bedrock and RAG. |
| AWS DevOps Engineer | Month 12-16 | CI/CD and infrastructure automation for ML pipelines. |
Milestones: Junior → Mid → Senior
| Level | Milestone | When |
|---|---|---|
| Entry Level | Basic AWS AI understanding (AIF-C01 optional) | Month 1-2 |
| Practitioner Level | MLA-C01 + SageMaker MLOps proficiency | Month 8-10 |
| Specialist Level | MLA-C01 + DEA-C01 + enterprise ML engineering skills | Month 12+ |
Step 0 - Engineering and AI foundations
Build the programming, data, and AWS foundations that every ML engineering task depends on.
~1 month~1 month
Step 0 - Engineering and AI foundations
Build the programming, data, and AWS foundations that every ML engineering task depends on.
- Python proficiency: functions, classes, pandas, numpy, boto3, virtual environments
- Data fundamentals: structured vs unstructured data, CSV, JSON, Parquet
- SQL basics: SELECT, JOIN, WHERE, GROUP BY
- Statistics basics: mean, median, variance, distributions, correlation
- Git and version control: reproducible ML workflows, experiment tracking
- AWS basics: IAM, S3, CloudWatch, CLI, and the shared responsibility model
Certifications
💡 Python fluency is non-negotiable for MLA-C01. You need to write real SageMaker SDK code.
💡 CLF-C02 (Cloud Practitioner) is optional. If you are new to AWS, take it as a 2-3 week warm-up.
💡 MLS-C01 (Machine Learning Specialty) retired March 31, 2026. This roadmap reflects the current landscape.
🏁 Entry Level Checkpoint: You can write Python code, handle data structures, and navigate core AWS services.
🛠 Project Ideas
- ▸Write a Python script using boto3 to list all S3 buckets and print their sizes.
- ▸Use pandas to load a CSV dataset, perform exploratory data analysis, and output summary statistics.
Step 1 - AI and ML fundamentals (AIF-C01) - Optional
Build foundational understanding of AI, ML, and generative AI concepts mapped to AWS services. Skip this step if you already have hands-on experience with SageMaker or Bedrock.
~1-2 months~1-2 months
Step 1 - AI and ML fundamentals (AIF-C01) - Optional
Build foundational understanding of AI, ML, and generative AI concepts mapped to AWS services. Skip this step if you already have hands-on experience with SageMaker or Bedrock.
- AI vs ML vs deep learning; supervised, unsupervised, and reinforcement learning
- The ML lifecycle: data collection, preparation, training, evaluation, deployment, and monitoring
- Foundation models and generative AI: tokens, embeddings, context windows, temperature
- AWS AI services: SageMaker, Bedrock, Rekognition, Comprehend, Textract
- Responsible AI: bias, fairness, explainability, and Bedrock Guardrails
- AI security and governance: IAM, KMS, PrivateLink, and compliance
Certifications
💡 AIF-C01 is completely optional. If you already deploy ML workloads on AWS, skip this and save 1-2 months.
💡 AIF-C01 does not require coding. It validates conceptual understanding and service selection reasoning.
🏁 Entry Level Checkpoint: You have passed AIF-C01 (or have equivalent experience). You understand AI concepts and AWS AI services.
🛠 Project Ideas
- ▸Use SageMaker Studio to launch a managed Jupyter notebook and run a simple linear regression.
- ▸Experiment with a foundation model in the Bedrock Playground.
Step 2 - ML engineering core (MLA-C01)
Build professional ML engineering skills on AWS SageMaker. MLA-C01 replaced MLS-C01 and emphasizes MLOps, GenAI, and cost-aware deployment.
~4-6 months~4-6 months
Step 2 - ML engineering core (MLA-C01)
Build professional ML engineering skills on AWS SageMaker. MLA-C01 replaced MLS-C01 and emphasizes MLOps, GenAI, and cost-aware deployment.
- Data Preparation (28%): S3, Glue, EMR Serverless, Kinesis, Feature Store, Data Wrangler, Clarify for bias detection
- Model Development (26%): built-in algorithms (XGBoost, Linear Learner), bring-your-own-container, JumpStart, hyperparameter tuning, distributed training, Bedrock model customization
- Deployment and Orchestration (22%): real-time, serverless, asynchronous, and batch endpoints; Inference Recommender; SageMaker Pipelines; EventBridge; model registry and A/B testing
- Monitoring, Maintenance, and Security (24%): Model Monitor (data drift, model quality, bias drift, feature drift), CloudWatch, retraining workflows, IAM, VPC, KMS, cost optimization
💡 MLA-C01 has 65 questions, 130 minutes, 720/1000 passing score, $150 USD. Recommended 1+ year ML engineering experience.
💡 The exam emphasizes MLOps over algorithms. Pipelines, monitoring, model registry, and drift detection are heavier than algorithm internals.
💡 Cost-aware deployment patterns — multi-model endpoints, serverless inference, and provisioned throughput — are tested.
🏁 Practitioner Level Checkpoint: You have passed MLA-C01. You can design, build, and deploy production ML systems on AWS.
🛠 Project Ideas
- ▸Build a complete SageMaker pipeline that ingests data from S3, trains an XGBoost model, registers the best version, and deploys it to a real-time endpoint with A/B testing.
- ▸Configure SageMaker Model Monitor for data quality and bias drift on a production endpoint. Set up automated retraining triggers.
Step 3 - Data engineering depth (DEA-C01)
Build the data pipeline skills that support ML workloads at scale. DEA-C01 validates ingestion, transformation, storage, and governance on AWS.
~2-3 months~2-3 months
Step 3 - Data engineering depth (DEA-C01)
Build the data pipeline skills that support ML workloads at scale. DEA-C01 validates ingestion, transformation, storage, and governance on AWS.
- Data Ingestion and Transformation (34%): Glue, EMR Serverless, Kinesis, MSK, DMS, Glue Data Catalog, Iceberg schema evolution
- Data Store Management (26%): S3 storage classes, S3 Tables (Iceberg), Redshift, DynamoDB, OpenSearch
- Data Operations and Support (22%): Step Functions, MWAA, EventBridge, CloudWatch, cost optimization, data quality
- Data Security and Governance (18%): Lake Formation, KMS, Macie, compliance, cross-account sharing
Certifications
💡 DEA-C01 has 65 questions, 130 minutes, 720/1000 passing score, $150 USD. Recommended 2-3 years data engineering experience.
💡 DEA-C01 replaced the Data Analytics Specialty (DAS-C01). It reflects modern lakehouse architecture — S3 Tables, Lake Formation, and EMR Serverless are first-class.
💡 Zero-ETL patterns (Aurora to Redshift, DynamoDB to Redshift) appear in exam questions.
🏁 Specialist Level Checkpoint: You have passed MLA-C01 and DEA-C01. You can build end-to-end ML systems from data ingestion to production inference.
🛠 Project Ideas
- ▸Build a serverless data pipeline with Kinesis and Glue that ingests streaming data, transforms it, and loads it into S3 Tables (Iceberg) for ML training.
- ▸Configure Lake Formation for fine-grained access control on a data lake used by multiple ML teams.
Step 4 - Exam consolidation and follow-on paths
Consolidate preparation through integrated scenario practice and identify follow-on credentials.
~1 month~1 month
Step 4 - Exam consolidation and follow-on paths
Consolidate preparation through integrated scenario practice and identify follow-on credentials.
- Full scenario practice: multi-service scenarios combining SageMaker, Glue, Kinesis, and security services
- Domain-weighted practice: Data Preparation (28%) and Model Development (26%) deserve the most time for MLA-C01
- Exam technique: read the full scenario, identify the binding constraint first, eliminate obviously wrong answers
- Follow-on paths: AIP-C01 (Generative AI) for Bedrock depth; AWS DevOps or Security credentials for adjacent infrastructure skills
💡 Consistent performance above 80% on Legend mode across five consecutive ExamOS sessions is the clearest readiness signal.
💡 AIP-C01 is the natural next step if your work expands into generative AI.
🏁 Specialist Level Checkpoint: You have passed your target certifications. You can design, deploy, and monitor enterprise-grade ML systems on AWS.
Final Step - What this path actually builds
This roadmap moves you from AI fundamentals to production ML engineering on AWS. MLA-C01 is not a theoretical exam. It tests your ability to build ML systems that ship. MLOps, monitoring, and cost-aware deployment carry more weight than algorithm trivia. DEA-C01 adds the data pipeline skills that separate ML engineers from data scientists. The ML engineer who masters both SageMaker and the data services that feed it is the one who delivers business value. Build real pipelines. Measure your readiness with ExamOS. Book when your scores are stable and your architecture decisions are clear.
Final Step - What this path actually builds
This roadmap moves you from AI fundamentals to production ML engineering on AWS. MLA-C01 is not a theoretical exam. It tests your ability to build ML systems that ship. MLOps, monitoring, and cost-aware deployment carry more weight than algorithm trivia. DEA-C01 adds the data pipeline skills that separate ML engineers from data scientists. The ML engineer who masters both SageMaker and the data services that feed it is the one who delivers business value. Build real pipelines. Measure your readiness with ExamOS. Book when your scores are stable and your architecture decisions are clear.
Certifications
Final Thoughts
💡 Total: 6-12 months at 2 hours/day (4-8 months if skipping AIF-C01 and studying 3-4 hours/day)
💡 MLA-C01: Typically takes 10-16 weeks for candidates with 1+ year SageMaker experience.
💡 DEA-C01: Typically takes 8-12 weeks after MLA-C01.
💡 Readiness: Consistent 80%+ on Legend mode across five sessions is your signal to book.
Honest Timeline
| Path | Minimum Study Pace | More Realistic Pace |
|---|---|---|
| MLA-C01 only (with experience, skipping AIF) | ~3 months | ~5 months |
| AIF-C01 + MLA-C01 | ~4 months | ~7 months |
| MLA-C01 + DEA-C01 (skipping AIF) | ~6 months | ~9 months |
| AIF-C01 + MLA-C01 + DEA-C01 | ~7 months | ~11 months |