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AWS: Machine Learning & MLOps Foundations

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AWS: Machine Learning & MLOps Foundations

Ce cours fait partie de Spécialisation "Exam Prep MLA-C01: AWS Machine Learning Engineer Associate"

Instructeur : Whizlabs Instructor

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2 modules

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niveau Intermédiaire

Expérience recommandée

6 heures à compléter

Planning flexible

Apprenez à votre propre rythme

2 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

6 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Data Preprocessing
Catégorie : Model Evaluation
Catégorie : Artificial Intelligence and Machine Learning (AI/ML)
Catégorie : MLOps (Machine Learning Operations)
Catégorie : Model Training
Catégorie : Supervised Learning
Catégorie : Unsupervised Learning
Catégorie : Machine Learning
Catégorie : Data Processing
Catégorie : Machine Learning Methods
Catégorie : Applied Machine Learning
Catégorie : Machine Learning Algorithms
Catégorie : Predictive Modeling

Outils que vous découvrirez

Catégorie : Classification Algorithms
Catégorie : Amazon Web Services
Catégorie : Model Deployment
Catégorie : AWS SageMaker

Détails à connaître

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Ce cours fait partie de la Spécialisation "Exam Prep MLA-C01: AWS Machine Learning Engineer Associate"

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Il y a 2 modules dans ce cours

"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.

Détails du module

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.

Inclus

10 vidéos2 lectures2 devoirs

10 vidéosTotal 55 minutes

Welcome to Specialization5 minutes
What is Machine Learning?5 minutes
Understanding difference - AI Vs Deep Learning Vs Machine Learning3 minutes
Types of Data8 minutes
Types of Machine Learning5 minutes
Identify the Machine Learing Use Case8 minutes
Steps for Machine Learning7 minutes
AWS Services for Machine Learning6 minutes
What is MLOps ?5 minutes
AWS Services for MLOps4 minutes

2 lecturesTotal 45 minutes

Welcome to the Course30 minutes
Overview of Machine Learning Concepts & Use Cases15 minutes

2 devoirsTotal 45 minutes

Machine Learning Concepts & Use Cases [Machine Learning and MLOps Concepts & Use Cases] - Assessment20 minutes
Foundations of Machine Learning & Use Cases - Knowledge Check25 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.

Inclus

9 vidéos3 lectures2 devoirs1 sujet de discussion

9 vidéosTotal 59 minutes

Classification task - Demo11 minutes
Model Selection, Training and Evaluation7 minutes
Data Preprocessing Essentials6 minutes
Evaluating Classification Models5 minutes
Confusion Matrix4 minutes
Examples of Interpretation of Confusion Matrix6 minutes
Evaluation Metrics - Regression6 minutes
Unsupervised Learning - Clustering5 minutes
Types of Inferencing - When to Use What ?9 minutes

3 lecturesTotal 90 minutes

Overview of Model Development & Evaluation Techniques30 minutes
Course Conclusion30 minutes
What's Next ? 30 minutes

2 devoirsTotal 45 minutes

Model Development & Evaluation Techniques - Assessment20 minutes
Building, Training & Evaluating ML Models - Knowledge Check25 minutes

1 sujet de discussionTotal 10 minutes

Meet and Greet10 minutes

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