My one month experience using AWS SageMaker

<h1>&hellip; from the previous episode</h1> <p>As a follow-up to my previous Medium&nbsp;<a href="https://medium.com/p/53d216afb9cb" rel="noopener">post</a>&nbsp;on the ever-evolving nature of data science and the need for continuous upskilling, I want to share some insights on the second stage of my upskilling plan. In this post, I will be discussing the AWS implementation of a task and its relevance to modern-day data science.</p> <ul> <li>LLM &mdash; Large Language Models</li> <li><strong>Upskill from ML in AWS</strong></li> <li>Become MultiCloud Practitioner</li> </ul> <h2>Summary</h2> <p>As I continued my journey to upskill as a data scientist, I knew that I needed to find a course that could help me fast-track my learning and progress. After some research, I stumbled upon Udemy&rsquo;s &ldquo;<a href="https://www.udemy.com/course/become-an-aws-machine-learning-engineer-in-30-days-new-2022/" rel="noopener ugc nofollow" target="_blank">Become an AWS ML Engineer</a>&rdquo; course, which piqued my interest. I had been wanting to delve deeper into cloud environments like AWS, and this course seemed like the perfect opportunity to do so.</p> <p><a href="https://medium.com/@filipespacheco/my-one-month-experience-using-aws-sagemaker-ca732e1ef488"><strong>Learn More</strong></a></p>