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AWS ML Workflow: Certification Best Practices
- This article explores the best practices of AWS Machine Learning (ML) workflows to help optimize and automate ML models on an advanced level and earn an AWS certification.
- The machine learning workflow on AWS involves data collection and preparation, model training and evaluation, model deployment, and monitoring and retraining the model.
- AWS Glue and S3 are used for data storage and preparation, while Amazon SageMaker is used for model training and evaluation.
- Best practices for AWS ML workflows include attaining scalability through managed services, automating data pipelines, optimizing for cost, monitoring and retraining models, leveraging SageMaker pipelines, and mastering data management.
- To earn an AWS Machine Learning certification, it is important to study the exam guide, create a study plan, focus on key concepts, gain hands-on experience, automate workflows, optimize for cost, master data management, leverage AWS training resources, study documentation and questions, and go through AWS case studies.
- A case study is also presented, wherein a healthcare organization aims to predict patient readmission using ML on AWS.
- Mastering AWS ML workflows is important for increasing prospects in the field and optimizing the deployment of models on AWS.
- Hands-on experience is essential before taking the AWS Certified Machine Learning – Specialty or AWS Certified Machine Learning Engineer Associate exam.
- One can also use the free AWS Sandbox service to build, train, and deploy different machine learning models without any risk.
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