AWS: Machine Learning & MLOps Foundations
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AWS: Machine Learning & MLOps Foundations
This course is part of Exam Prep MLA-C01: AWS Machine Learning Engineer Associate Specialization
Instructor: Whizlabs Instructor
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What you'll learn
Explore the core concepts of Machine Learning and how it differs from AI and Deep Learning.
Introduce key AWS services and MLOps practices for managing the end-to-end ML lifecycle.
Explore how to build and evaluate classification and regression models using AWS ML services.
Differentiate between batch and real-time inferencing methods and identify suitable use cases for each.
Skills you'll gain
- Machine Learning Algorithms
- Predictive Modeling
- Unsupervised Learning
- Applied Machine Learning
- MLOps (Machine Learning Operations)
- Data Processing
- Machine Learning
- Machine Learning Methods
- Artificial Intelligence and Machine Learning (AI/ML)
- Supervised Learning
- Data Preprocessing
- Model Evaluation
- Model Training
Details to know
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Build your subject-matter expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 2 modules in this course
"AWS: Fundamentals of Machine Learning & MLOps is the first course of Exam Prep (MLA-C01): AWS Certified Machine Learning Engineer – Associate Specialization. This course assists learners in building foundational knowledge of core machine learning concepts, including types of learning, data preparation, model evaluation, and operationalization. Learners gain a strong understanding of the difference between AI, Deep Learning, and Machine Learning, and how to identify and apply real-world ML use cases using AWS services.
This course allows learners to explore key topics such as model selection, classification workflows, confusion matrices, and regression evaluation techniques. In addition, learners are introduced to the concepts of MLOps and the AWS services used to streamline ML deployment and monitoring in production environments. The course is divided into two modules, and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:30–3:00 hours of video lectures that provide both theory and hands-on knowledge using AWS tools. Also, Graded and Ungraded Quizzes are provided with every module to test the understanding and application readiness of learners." Module 1: Machine Learning and MLOps Concepts Module 2 : Model Development & Evaluation Techniques By the end of this course, learners will be able to: - Apply foundational machine learning and MLOps concepts using AWS tools - Build and evaluate ML models with services like Amazon SageMaker - Understand end-to-end ML workflows, including data preparation, model training, and deployment - Strengthen their preparation for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam This course is ideal for aspiring ML practitioners, data engineers, and developers with 6 months to 1 year of AWS experience who want to build practical skills in machine learning and MLOps. It also supports learners preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam and professionals seeking hands-on knowledge of implementing and managing ML workflows using AWS services.
Welcome to Week 1 of the AWS: Machine Learning & MLOps Foundations course. This week, you’ll explore the fundamentals of Machine Learning (ML) and how it differs from AI and Deep Learning. We'll cover types of data, types of ML (supervised, unsupervised, reinforcement), and how to identify suitable ML use cases. You’ll walk through the ML lifecycle—from data ingestion to deployment—and get introduced to key AWS services that support ML workflows. We’ll also touch on MLOps concepts and AWS tools that help scale and manage ML models in production.
What's included
10 videos2 readings2 assignments
10 videos•Total 55 minutes
- Welcome to Specialization•5 minutes
- What is Machine Learning?•5 minutes
- Understanding difference - AI Vs Deep Learning Vs Machine Learning•3 minutes
- Types of Data•8 minutes
- Types of Machine Learning•5 minutes
- Identify the Machine Learing Use Case•8 minutes
- Steps for Machine Learning•7 minutes
- AWS Services for Machine Learning•6 minutes
- What is MLOps ?•5 minutes
- AWS Services for MLOps•4 minutes
2 readings•Total 45 minutes
- Welcome to the Course•30 minutes
- Overview of Machine Learning Concepts & Use Cases•15 minutes
2 assignments•Total 45 minutes
- Foundations of Machine Learning & Use Cases - Knowledge Check•25 minutes
- Machine Learning Concepts & Use Cases [Machine Learning and MLOps Concepts & Use Cases] - Assessment•20 minutes
Welcome to Week 2 of the AWS: Machine Learning & MLOps Foundations course. This week, we’ll dive into practical aspects of model building. You'll start with a classification demo, followed by learning how to select, train, and evaluate models using AWS tools. We’ll cover data preprocessing techniques, explore the confusion matrix and regression metrics, and introduce unsupervised learning through clustering. Finally, you'll understand the difference between batch and real-time inferencing, and when to apply each.
What's included
9 videos3 readings2 assignments1 discussion prompt
9 videos•Total 59 minutes
- Classification task - Demo•11 minutes
- Model Selection, Training and Evaluation•7 minutes
- Data Preprocessing Essentials•6 minutes
- Evaluating Classification Models•5 minutes
- Confusion Matrix•4 minutes
- Examples of Interpretation of Confusion Matrix•6 minutes
- Evaluation Metrics - Regression•6 minutes
- Unsupervised Learning - Clustering•5 minutes
- Types of Inferencing - When to Use What ?•9 minutes
3 readings•Total 90 minutes
- Overview of Model Development & Evaluation Techniques•30 minutes
- Course Conclusion•30 minutes
- What's Next ? •30 minutes
2 assignments•Total 45 minutes
- Building, Training & Evaluating ML Models - Knowledge Check•25 minutes
- Model Development & Evaluation Techniques - Assessment•20 minutes
1 discussion prompt•Total 10 minutes
- Meet and Greet•10 minutes
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