OK in the spirit of ML, I SMASHED jotted down some current notes and used Claude this time to take my notes and make a good write up:

As an experienced AWS professional going through the AWS ML Ops certification training, I’ve noticed that some of the content feels outdated and doesn’t align with the latest AWS changes, particularly regarding services like AWS Glue. The initial half of the training has primarily focused on data-related concepts, which is unsurprising given the nature of machine learning.

The training covers various topics, including data classification, different approaches to Extract, Transform, and Load (ETL) operations, and assessing whether a problem truly requires the application of artificial intelligence (AI) or could be solved programmatically using languages like Python. While the information provided on data structuring and preparation for machine learning is valuable and still relevant, it’s worth noting that as an AWS veteran, you may not necessarily utilize many of the specific tools mentioned in the certification material within your day-to-day AWS operations.

The training delves into the intricacies of data and how it needs to be structured and prepared for machine learning models. This knowledge is undoubtedly useful and applicable, even as AWS services and tooling continue to evolve. However, it’s essential to recognize that the certification content may not always reflect the most up-to-date changes and advancements within the AWS ecosystem.

Overall, while the AWS ML Ops certification training provides a solid foundation in data-related concepts and machine learning principles, it’s crucial to supplement this knowledge with hands-on experience, staying updated on the latest AWS service releases, and exploring alternative resources to ensure alignment with the most recent developments in the AWS ecosystem.