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The KDnuggets team hopes you enjoyed the 'Back to Basics' series. Finally, we have an extra week for those who want to go the extra mile and increase their knowledge base.
If you haven't already, check out:
Moving on to the bonus week,
- Bonus 1: Introduction to Google Platform in five steps
- Bonus 2: Deploying your machine learning model to production in the AWS cloud
Bonus Week: Part 1: Getting Started with Google Cloud Platform in Five Steps
Explore the basics of Google Cloud Platform for data science and machine learning, from setting up accounts to deploying models, with practical project examples.
This article aims to provide a step-by-step overview on how to get started using Google cloud platform (GCP) for data science and machine learning. We'll provide an overview of GCP and its key analytics capabilities, explain account setup, and explore essential services such as Great consultation and Cloud storageCreate a sample data project and use GCP for machine learning.
Whether you're new to GCP or looking for a quick refresher, read on to learn the basics and get started with Google Cloud.
Bonus Week: Part 2: Deploying Your Machine Learning Model to Cloud Production
Discover an easy way to have a live model hosted on AWS.
AWS, or Amazon Web Services, is a cloud computing service used in many businesses for storage, analytics, applications, deployment services, and many others. It is a platform that uses various services to support serverless businesses with pay-as-you-go schemes.
The machine learning modeling activity is also one of the activities supported by AWS. With various services, modeling activities can be supported, such as developing the model into production. AWS has demonstrated versatility, which is essential for any business that needs scalability and speed.
This article will discuss deploying a machine learning model to the AWS cloud in production. How could we do that? Let's explore further.
And that's a wrap!
Congratulations on completing the Back to Basics series bonus week.
The KDnuggets team hopes that the Back to Basics path has provided readers with a comprehensive and structured approach to mastering the fundamentals of data science.
If you liked the Back to Basics series, let us know in the comments so the team can create another series. Please send suggestions too!
nisha arya is a data scientist and freelance technical writer. She is particularly interested in providing professional data science advice or tutorials and theory-based data science insights. She also wants to explore the different ways in which artificial intelligence can benefit the longevity of human life. A great student looking to expand her technological knowledge and writing skills, while she helps guide others.
