AWS Certified Machine Learning - Specialty
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AWS Certified Machine Learning - Specialty at AWS Overview
Duration | 3 hours |
Mode of learning | Online |
Credential | Certificate |
AWS Certified Machine Learning - Specialty at AWS Course details
- Individuals who perform a development or data science role and have more than one year of experience developing, architecting, or running machine learning/deep learning workloads in the AWS Cloud
- Analytics
- Compute
- Containers
- Database
- Internet of Things (IoT)
- Machine Learning
- Management and Governance
- Networking and Content Delivery
- Security, Identity, and Compliance
- Serverless
- Storage
- This credential helps organizations identify and develop talent with critical skills for implementing cloud initiatives. Earning AWS Certified Machine Learning - Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.
AWS Certified Machine Learning - Specialty at AWS Curriculum
Domain 1: Data Engineering
Create data repositories for machine learning
Identify and implement a data ingestion solution
Identify and implement a data transformation solution
Domain 2: Exploratory Data Analysis
Sanitize and prepare data for modeling
Perform feature engineering
Analyze and visualize data for machine learning
Domain 3: Modeling
Frame business problems as machine learning problems
Select the appropriate model(s) for a given machine learning problem
Train machine learning models
Perform hyperparameter optimization
Evaluate machine learning models
Domain 4: Machine Learning Implementation and Operations
Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance
Recommend and implement the appropriate machine learning services and features for a given problem
Apply basic AWS security practices to machine learning solutions
Deploy and operationalize machine learning solutions
AWS Certified Machine Learning - Specialty at AWS Entry Requirements
- Recommended:
- At least two years of hands-on experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud
- Ability to express the intuition behind basic ML algorithms
- Experience performing basic hyperparameter optimization
- Experience with ML and deep learning frameworks
- Ability to follow model-training, deployment, and operational best practices
- Not mentioned
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