I wrapped up Amazon’s Exam Readiness: AWS Certified Machine Learning – Specialty course today.
It’s the preparation course to take just before the AWS Machine Learning specialty exam, but I’m nowhere near ready. Instead my strategy was to get a high-level overview of what the AWS ML ecosystem looks like, then diving into those tools themselves.
My experience with ML so far is just creating and training and running jobs locally, so excited to see how that gets spun up in the cloud.
I’m also very curious to see how Amazon SageMaker works in practice, and if it can make something of a Machine Learning engineer out of me, very much a non-engineer.
I’m working out a checklist of my next steps – what I’m planning to follow all the way to the AWS Certified Machine Learning – Specialty examination. These are some of the templates I’m also following, just for reference:
- How I prepared for the AWS Certified Machine Learning Specialty (TowardsDataScience)
- My Path to Passing the AWS Machine Learning Certification (Adam DeJans, Medium)
I’m also looking into the PluralSight/A Cloud Guru learning paths, which are larger than just this ML specialty, but probably provide the best combo of learning/action to really understand what I’m working with.