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The team at KDnuggets hope you have been enjoying the ‘Back to Basic’ series. To end it off, we have a bonus week for those who want to go that extra mile and increase their knowledge base.
If you haven’t already, have a look at:
Moving onto the bonus week,
- Bonus 1: Getting Started with Google Platform in 5 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 5 Steps
Explore the essentials of Google Cloud Platform for data science and ML, from account setup to model deployment, with hands-on project examples.
This article aims to provide a step-by-step overview of getting started with Google Cloud Platform (GCP) for data science and machine learning. We’ll give an overview of GCP and its key capabilities for analytics, walk through account setup, explore essential services like BigQuery and Cloud Storage, build 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 hit the ground running with Google Cloud.
Bonus Week – Part 2: Deploying Your Machine Learning Model to Production in the Cloud
Learn a simple 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’s a platform utilizes several services to support business in a serverless way with pay-as-you-go schemes.
Machine learning modeling activity is also one of the activities that AWS supports. With several services, modeling activities can be supported, such as developing the model to making it into production. AWS has shown versatility, which is essential for any business that needs scalability and speed.
This article will discuss deploying a machine learning model in the AWS cloud into production. How could we do that? Let’s explore further.
And that’s a wrap!
Congratulations on completing the Bonus Week to the Back to Basic series.
The team at KDnuggets hope that the Back to Basics pathway has provided readers with a comprehensive and structured approach to mastering the fundamentals of data science.
If you have enjoyed the Back to Basic series, let us know in the comments so the team can craft another series. Please drop suggestions too!
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.
