Course Program Overview
AWS Certified: Machine Learning – Specialty (MLS-C01)
- Duration: 3 Months
- Format: Live Online / Classroom / Corporate
- Sessions: 5 per week
- Session Length: 01 Hour each
- Tech Stack: AWS SageMaker, AWS Glue, Athena, Redshift, Kinesis, EMR, Lambda, S3, DynamoDB, CloudWatch, IAM, Step Functions, Comprehend, Rekognition, Polly, Lex, Forecast, Personalize, Security Hub
- Outcome: Enterprise-grade portfolio + AWS Certification + Career acceleration
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MLS-C01: AWS Certified Machine Learning – Specialty Overview
The MLS-C01 exam validates advanced technical skills in building, training, tuning, and deploying machine learning models on AWS. It is intended for professionals seeking to demonstrate expertise in data engineering, ML modeling, deployment, and optimization using AWS services.
Certification benefits include career advancement, proof of AWS ML mastery, and alignment with the world’s leading cloud platform.
📌 Key Exam Information
Certification Name
AWS Certified Machine Learning – Specialty
Exam Code
MLS-C01
Level
Specialty
Platform
AWS Cloud
Role
ML Engineer / Data Scientist / AI Specialist
Prerequisites
Recommended: 1–2 years of experience in ML/DL and AWS services
Familiarity with Python, data preprocessing, and ML frameworks (TensorFlow, PyTorch, MXNet)
Exam Format
Multiple-choice, multiple-response
Duration
~180 minutes
Passing Score
Scaled score (750/1000 approx.)
🎯 Who Should Take MLS-C01
- Data scientists and ML engineers specializing in AWS
- Developers building and deploying ML models at scale
- Cloud professionals responsible for AI/ML adoption in enterprises
- IT leaders driving machine learning innovation across organizations
- Professionals preparing for senior ML and AI roles
🧠 MLS-C01 Exam Domain Weightage
Data Engineering
Design and implement data ingestion, transformation, and storage solutions.
Exploratory Data Analysis
Perform feature engineering, data visualization, and statistical analysis.
Modeling
Select, train, tune, and evaluate ML models.
Machine Learning Implementation & Operations
Deploy, monitor, and optimize ML solutions in production.
🏆 Benefits of MLS-C01 Certification
- Future-Proof Skills: Validates advanced AWS ML expertise across industries.
- Career Advancement: Opens roles like ML Engineer, Data Scientist, AI Architect.
- Enterprise Demand: AWS ML adoption is dominant; certified professionals are highly sought after.
- Global Recognition: AWS certifications are valued worldwide, boosting credibility.
- Placement Advantage: Demonstrates readiness for enterprise-scale ML projects.
- Continuous Learning: Certification renewal ensures ongoing alignment with evolving AWS services.
🌟 Top 5 Reasons to Choose Adian Soft Solutions for this course
Comprehensive Exam Alignment
Our 3-month roadmap is mapped directly to AWS’s official MLS-C01 exam blueprint. Every domain, every percentage weightage, and every skill is covered in detail, ensuring you’re exam-ready with no gaps.
Certification Guarantee & Retake Support
If you don’t pass MLS-C01 on your first attempt, we offer extended access to our learning materials and personalized retake support at no extra cost.
Hands-On Real-World Learning
Every module includes practical labs and industry-aligned case studies, ensuring students actively build, train, and deploy ML models.
Capstone Project & Portfolio Development
Learners complete a comprehensive capstone project mirroring enterprise ML challenges. The finished project becomes part of their professional portfolio.
Career & Placement Support
Adian provides structured career pathways, resume workshops, interview preparation, and direct placement assistance.
Skills You Will Get
Data Engineering
Data Engineering
Exploratory Data Analysis
Exploratory Data Analysis
Modeling
Modeling
Deployment & Operations
Deployment & Operations
Portfolio Development
Portfolio Development
Course Design – AWS Certified Machine Learning – Specialty (MLS-C01)
● Month 1
Exam Coverage: Data Engineering (20%) + Exploratory Data Analysis (24%)
Data Engineering & Exploratory Data Analysis
Focus:
Learners begin with data ingestion, transformation, and storage using Glue, Kinesis, and Redshift. They gain hands-on exposure to feature engineering, visualization, and statistical analysis with Athena and SageMaker.
Practical Labs:
- Build ETL pipelines with AWS Glue
- Stream data with Kinesis
- Perform queries with Athena
- Feature engineering with SageMaker notebooks
Case Studies:
- A retail company building real-time recommendation pipelines
- A healthcare provider analyzing patient data for predictive insights
Career Readiness:
- Resume tips for highlighting ML data engineering skills
- Mapping MLS-C01 to ML Engineer roles
● Month 2
Exam Coverage: Modeling (36%)
Modeling & Training
Focus:
This month emphasizes ML model selection, training, tuning, and evaluation. Learners explore SageMaker built-in algorithms, hyperparameter tuning, and ML frameworks like TensorFlow and PyTorch.
Practical Labs:
- Train ML models with SageMaker built-in algorithms
- Perform hyperparameter tuning
- Evaluate models with metrics (accuracy, precision, recall, F1)
- Use TensorFlow/PyTorch for custom models
Case Studies:
- A fintech company predicting credit risk with ML models
- An e-commerce platform optimizing product recommendations
Career Readiness:
- Workshops on ML interview questions
- Guidance on positioning ML modeling expertise in resumes
● Month 3
Exam Coverage: ML Implementation & Operations (20%) + Consolidation of 100%
Deployment, Operations & Career Readiness
Focus:
The final month focuses on deploying ML models, monitoring performance, and optimizing solutions. Learners explore SageMaker endpoints, CloudWatch monitoring, and cost optimization strategies. The capstone project consolidates all skills: students design and implement an end-to-end ML solution integrating data engineering, modeling, and deployment.
Practical Labs:
- Deploy ML models with SageMaker endpoints
- Monitor performance with CloudWatch
- Automate workflows with Step Functions
- Optimize ML workloads for cost and scalability
Case Studies:
- Enterprises deploying ML models for fraud detection
- Media companies using ML for personalized content delivery
Career Readiness:
- Resume workshops tailored to ML cloud roles
- Mock interviews for ML Engineer and Data Scientist positions
- Alumni success stories and placement pathways
- Capstone project presentation to showcase portfolio-ready ML expertise
Real Roles. Real Results.
Explore Your Post-Course Career
After completing the course, learners can unlock high-impact roles such as:
- Machine Learning Engineer
- Data Scientist
- AI Specialist
- ML Solutions Architect
- Applied Scientist
Salary Benchmark
AWS Certified Machine Learning – Specialty (MLS-C01)
- India: ML engineers typically earn ₹18–30 LPA, with higher packages for enterprise-scale roles.
- United States: The median salary is $145,000/year, with most roles ranging between $130,000–160,000/year.
- Global Outlook: Certified ML professionals often exceed $170,000/year when advancing to senior AI architect or specialized ML roles.
Frequently Asked Questions
MLS-C01 AWS Certified Machine Learning – Specialty
It validates advanced AWS ML expertise including data engineering, modeling, deployment, and optimization.
Data scientists, ML engineers, developers, and cloud professionals aiming to master ML on AWS.
Data pipelines, feature engineering, ML modeling, deployment, monitoring, and portfolio-ready projects.
Multiple-choice and multiple-response questions, ~180 minutes, passing score ~750/1000 (scaled).
- Data Engineering (20%)
- Exploratory Data Analysis (24%)
- Modeling (36%)
- ML Implementation & Operations (20%)
No mandatory prerequisites, but AWS recommends 1–2 years of hands-on experience with ML/DL and AWS services. Associate-level certifications (Solutions Architect, Developer, SysOps) are helpful but not required.
Start with Foundational (CLF-C02), progress to Associate (Solutions Architect, Developer, SysOps), then Professional (Solutions Architect, DevOps Engineer), and finally Specialty certifications like Machine Learning, Security, and Data Analytics.
Adian’s structured 3-month roadmap covers all domains with labs, case studies, and exam-aligned content.
Hands-on labs with SageMaker, Glue, Athena, Redshift, TensorFlow/PyTorch, and ML deployment pipelines, plus a capstone project simulating enterprise ML challenges.
Resume workshops, interview prep, alumni network, and direct placement assistance with global enterprises.
Global recognition, career advancement, proof of AWS ML mastery, and enterprise demand for AI/ML-skilled professionals.
It ensures progressive skill development from foundational cloud knowledge to advanced enterprise ML expertise.
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Partnering with Adian Soft Solutions transformed our AI adoption journey. Their training programs gave our team the confidence to deploy advanced ML models in production. The hands-on labs and real-world case studies were game changers.
Priya Sharma, CTO Global Tech Innovators