Machine Learning Pipeline on AWS

KEY FEATURES

The AWS Certified Machine Learning course is intended for individuals who perform a development or data science role. Successfully passing the exam validates a candidate's ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

What's Included

Your course package is designed to provide maximum learning and convenience. This is included in the price of your course:

• PRE-READING
• INSTRUCTOR-LED TRAINING
• ALL COURSE MATERIALS
• EXAM
• CERTIFICATION GUARANTEE
• PRE-READING
• INSTRUCTOR-LED TRAINING
• ACCOMMODATION
• ALL COURSE MATERIALS
• ALL MEALS
• EXAM ONSITE
• CERTIFICATION GUARANTEE

EXAMS & CERTIFICATIONS

Your expert instructor will get you ready for the following exam and certification, which are included in your course package and covered by the Certification guarantee:

• AWS Certified Machine Learning Specialty Exam (MLS-C01)
• AWS Certified Machine Learning – Specialty

PREREQUISITES

You´ll have the perfect starting point for your training with these prerequisites:

- 1-2 years of experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud
- The ability to express the intuition behind basic ML algorithms
- Experience performing basic hyperparameter optimization
- Experience with ML and deep learning frameworks
- The ability to follow model-training best practices
- The ability to follow deployment and operational best practices

Full curriculum

Using our engaging learning methodology including a variety of tools, we’ll cover the entire curriculum.

Domain 1: Data Engineering

1.1 Create data repositories for machine learning.
1.2 Identify and implement a data-ingestion solution.
1.3 Identify and implement a data-transformation solution.
Domain 2: Exploratory Data Analysis

2.1 Sanitize and prepare data for modeling.
2.2 Perform feature engineering.
2.3 Analyze and visualize data for machine learning.
Domain 3: Modeling

3.1 Frame business problems as machine learning problems.
3.2 Select the appropriate model(s) for a given machine learning problem.
3.3 Train machine learning models.
3.4 Perform hyperparameter optimization.
3.5 Evaluate machine learning models.
Domain 4: Machine Learning Implementation and Operations

4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
4.2 Recommend and implement the appropriate machine learning services and features for a given problem.
4.3 Apply basic AWS security practices to machine learning solutions.
4.4 Deploy and operationalize machine learning solutions.