AWS CERTIFIED MACHINE LEARNING - SPECIALTY (MLS-C01)
The AWS Certified Machine Learning - Specialty course is designed for professionals who design, build, train, deploy, and maintain machine learning (ML) solutions on Amazon Web Services (AWS). This course focuses on the end-to-end machine learning lifecycle, including data preparation, model development, training, tuning, deployment, monitoring, and optimization using AWS ML services. Participants will gain hands-on knowledge of selecting appropriate ML algorithms, designing scalable ML architectures, and implementing production-ready ML solutions. The course prepares learners for the AWS Certified Machine Learning - Specialty (MLS-C01) certification exam. Course Objectives By the end of this course, participants will be able to: Design end-to-end machine learning solutions on AWS Prepare, clean, and engineer data for ML workloads Select appropriate ML algorithms and frameworks Train, tune, and evaluate ML models at scale Deploy and monitor ML models in production Implement security, governance, and cost optimization for ML systems Prepare effectively for the AWS Machine Learning – Specialty exam
Course Curriculum
1
- Machine learning concepts and use cases
- Supervised, unsupervised, and reinforcement learning
- AWS ML ecosystem overview
2
- Data collection and ingestion
- Data preparation and feature engineering
- Amazon S3 data lakes
- AWS Glue for data transformation
3
- Data analysis techniques
- Amazon Athena
- Amazon QuickSight
- Identifying data quality issues
4
- Choosing ML algorithms
- Built-in algorithms vs custom models
- Amazon SageMaker training jobs
- Distributed training and scaling
5
- Hyperparameter optimization
- Model evaluation metrics
- Bias, variance, and overfitting
- Model selection strategies
6
- Batch vs real-time inference
- Amazon SageMaker endpoints
- Model versioning and A/B testing
- Blue/green deployments for ML models
7
- Model monitoring and drift detection
- Amazon CloudWatch and logs
- CI/CD for ML (MLOps concepts)
- Retraining and automation pipelines
8
- IAM for ML workloads
- Data encryption and access control
- Responsible AI and governance considerations
9
- Cost-efficient ML training and inference
- Instance selection and scaling strategies
- Optimizing performance at scale
10
- Natural Language Processing (NLP)
- Computer Vision
- Recommendation systems
- Time-series forecasting
11
- Exam domains and structure
- Scenario-based practice questions
- Exam tips and strategies
12
- Machine learning engineers
- Data scientists and data engineers
- Cloud architects designing ML solutions
- Software engineers working with ML workloads
- Professionals preparing for AWS Machine Learning certification
13
- AWS Certified Cloud Practitioner or equivalent knowledge
- Strong understanding of ML fundamentals
- Experience with Python and basic data science libraries
- Familiarity with statistics and data analysis
14
- 4-5 days (Instructor-led)
- 32-40 hours of training
15
- Instructor-led advanced technical sessions
- Hands-on ML labs using Amazon SageMaker
- Real-world ML use cases and scenarios
- Exam-focused learning approach
16
- This course prepares participants for the AWS Certified Machine Learning - Specialty (MLS-C01) certification exam
17
- Comprehensive training materials
- Hands-on lab guides
- Practice exam questions
- Certificate of course completion
This course includes
- 17+ Activity Modules
- 40 hours + lessons
- Lifetime access
- Certificate of completion
- Available on desktop and mobile
What Our Participants Say
Hear from participants who have experienced our training.
The facilitator’s wealth of experience was both striking and beneficial. He really helped translate concepts to real life.
The practical session where step by step of the lecture is broken down in visual and practice
I enjoyed the interactivity. It was also very direct, pointing out to us the key things to look for in the bidding, tender and proposal administration process, while also ensuri...
The practical session of the class which helped in giving a live run through of the necessary bidding and tender platform
The atmosphere was very pleasant, as it was an interactive and engaging session, which I personally enjoyed.
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