Storing data for machine learning is challenging due to the varying formats and characteristics of data. Raw ingested data must first be transformed into the format necessary for downstream machine learning consumption, and once the data is ready to be used, it must be ingested from storage to the machine learning service.
In this blog at k21academy.com/awsml17, you will learn to choose the right AWS service for each of these data-related machine learning ML tasks for any given scenario.
The blog post covers:
• Important Characteristics to Consider in A ML Solution
• Data Storage Options for Machine Learning On AWS
• Database Options for Machine Learning On AWS
• Streaming Data Ingestion Solutions on AWS for Machine Learning
• AWS Data Pipeline
• Data Transformation Overview on AWS for Machine Learning
If you are planning to become an AWS Certified Machine Learning – Specialty Certification, then join the waitlist at https://k21academy.com/awsml02.
Also, do not forget to join our FREE Telegram group at https://t.me/k21amazonaws and be the first to receive AWS-related news and updates.
Oracle ACE, Author, Speaker and Founder of K21 Technologies & K21 Academy : Specialising in Design, Implement, and Trainings.