AWS
AWS Certified Machine Learning – Specialty
Learn how to use AWS services like SageMaker, Glue, and Forecast to create, train, and implement machine learning models. Through practical, real-world projects, this course gets you ready for the AWS ML Specialty certification.
5/5(4,890 Reviews)
Level
Advanced
Duration
8 weeks
What is AWS Certified Machine Learning – Specialty?
Learn how to use AWS to create data-driven, intelligent solutions.
Get practical experience with Glue, Forecast, and SageMaker for real-world machine learning projects.
Develop your skills in data preparation, model training, and large-scale deployment.
Prepare yourself completely to obtain the AWS Machine Learning – Specialty certification.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
AWS Certified Machine Learning – Specialty Course Curriculum
.
Data Engineering for ML
Section 1
Prepare and manage data pipelines, repositories, and ingestion using AWS services to support ML workflows.
Data ingestion (batch & streaming)
Data storage (S3, Redshift, etc.)
ETL / data transformation
AWS Glue, EMR, Kinesis
.
Data Preparation & Feature Engineering
Section 2
Clean, preprocess, and engineer features to improve model training and performance.
Missing data handling & imputation
Feature scaling, normalization
Categorical encoding, one-hot / embeddings
Feature selection & extraction
.
Exploratory Data Analysis (EDA)
Section 3
Explore and understand dataset distributions, relationships, outliers, and visualize data to inform modeling decisions.
Statistical summaries & data visualization
Correlation & covariance
Detecting outliers & anomalies
Time-series analysis
.
Model Selection & Algorithm Fundamentals
Section 4
Understand different ML algorithm types and when to apply them.
Supervised learning (classification/regression)
Unsupervised learning (clustering, dimensionality reduction)
Algorithm trade-offs
Loss functions & error metrics
.
Model Training & Hyperparameter Tuning
Section 5
Train ML models using AWS tools, optimize hyperparameters, and avoid overfitting/underfitting.
SageMaker built-in algorithms & custom models
Hyperparameter tuning & search (grid, random)
Overfitting vs underfitting, regularization
Distributed training techniques
.
Performance Evaluation & Metrics
Section 6
Evaluate model performance using relevant metrics and statistical tests.
Accuracy, precision, recall, F1 score, ROC-AUC
Confusion matrix & error analysis
Cross-validation, bias/variance tradeoff
Handling imbalanced datasets
.
Deployment & Real-Time Inference
Section 7
Deploy models using AWS services and set up inference endpoints.
SageMaker endpoints & variants
Batch vs real-time inference
Asynchronous inference
Edge inference (AWS Neo, IoT)
.
ML Implementation & Operations (MLOps)
Section 8
Manage model lifecycle, versioning, monitoring, pipeline automation, and governance.
Model versioning & CI/CD pipelines
Monitoring model drift, concept drift
Endpoint monitoring & logging
Automation (SageMaker Pipelines, Step Functions)
.
Security, Cost & Scalability
Section 9
Ensure ML solutions are secure, cost-efficient, and scalable.
IAM / VPC / encryption at rest & in transit
Cost optimization (spot instances, right-sizing)
Scaling infrastructure
Secure notebook instances & data access
.
Exam Prep, Case Studies & Best Practices
Section 10
Practice with real-world scenarios, mock exams, and learn best practices from AWS ML-specialty experiences.
Hands-on labs & sample projects
Mock exam / practice questions
Case studies of end-to-end ML pipelines
AWS best practices & pitfalls
Key Projects
AWS Certified Machine Learning – Specialty
Fraud Detection System
Using Amazon Kinesis, AWS Glue, and SageMaker, a real-time fraud detection model was created. It was then deployed via API Gateway and watched over by CloudWatch to identify irregularities at scale.
Recommendation Engine
created a product recommendation system using SageMaker and Amazon Personalize, integrated it using API Gateway + Lambda, and monitored its performance using QuickSight dashboards.
Demand Forecasting
Using SageMaker DeepAR and Amazon Forecast, a sales forecasting model was developed. RDS/S3 data was processed, and QuickSight was used to provide insights for inventory optimization.
AWS Certified Machine Learning – Specialty – Associate Training Program
Category
Associate
Exam Name:
AWS Certified Machine Learning – Specialty – Associate
Exam Code:
N.A.
Exam Duration:
N.A.
Exam Format:
N.A.
Passing Score:
N.A.
Our Testimonials
Frequently Asked Questions (FAQs)
What is the AWS Certified Machine Learning – Specialty certification?
This certification validates expertise in designing, implementing, and maintaining machine learning (ML) solutions on AWS. It covers data engineering, modeling, deployment, and operationalization. Techpratham explains that it demonstrates strong ML skills in real-world cloud scenarios.
Who should take the MLS-C01 exam?
The exam is designed for data scientists, ML engineers, and AI developers with hands-on AWS experience. It is ideal for professionals building ML pipelines, training models, and deploying solutions. Techpratham highlights that it suits candidates aiming for ML roles in the cloud.
What skills are measured in MLS-C01?
The exam measures data engineering, feature engineering, model training and tuning, deployment, monitoring, and optimization. It also covers AWS ML services like SageMaker, Rekognition, and Comprehend. Techpratham notes that familiarity with algorithms, metrics, and model evaluation is essential.
Are there prerequisites for MLS-C01?
There are no formal prerequisites, but AWS recommends 1–2 years of hands-on ML experience, along with programming knowledge (Python/SQL) and statistics. Techpratham suggests practicing real ML workflows on AWS before attempting the exam.
How long is the MLS-C01 certification valid?
The certification is valid for 3 years, after which recertification is required. Techpratham emphasizes continuous learning to stay current with evolving AWS ML tools and algorithms.
How much does the MLS-C01 exam cost?
The exam costs $300 USD, reflecting its specialty-level status. Techpratham advises confirming pricing before registration and budgeting for preparation resources.
What is the format of the MLS-C01 exam?
The exam consists of multiple-choice and multiple-response scenario-based questions. It tests practical knowledge of ML workflows on AWS. Techpratham explains that real-world scenarios help gauge your ability to choose the right algorithms and services.
How should I prepare for the MLS-C01 exam?
Preparation includes AWS ML training, hands-on SageMaker labs, study of ML algorithms, and review of evaluation metrics. Techpratham recommends building end-to-end ML pipelines on AWS and experimenting with hyperparameter tuning.
What job roles can I get after earning MLS-C01?
The certification qualifies candidates for roles like ML Engineer, Data Scientist, AI Developer, and Cloud AI Architect. Techpratham notes that it enhances career growth in AI/ML-driven cloud projects.
Is the MLS-C01 exam considered difficult?
Yes, it is challenging due to the combination of ML theory, practical modeling, and AWS service knowledge. Techpratham stresses that hands-on practice with datasets and SageMaker is key to success.
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