In this video
What you'll learn
Best Practices and Setting up
Learn the essential tips and techniques for seamless and effective cloud deployment.
Deploy End-to-End Data Science Projects
Master the process of turning your models into fully functional, cloud-hosted applications.
Showcasing Your Work
Understand how to share your deployed projects and build a professional portfolio that stands out.
Why this topic matters
Deploying projects in the cloud is crucial for Data Scientists to build a strong portfolio. It enables them to showcase work as interactive, accessible applications for stakeholders and recruiters. By bridging the gap between model development and real-world usability, cloud deployment highlights their ability to deliver end-to-end solutions, enhancing professional credibility.
You'll learn from
Siddarth Ranganathan
Principal Data Science Manager at Microsoft
Siddarth brings 20 years of experience spanning Healthcare, eCommerce, and Tech. He currently leads a team of 20 data scientists and engineers, driving high-impact initiatives for Azure, delivering scalable solutions and measurable outcomes.
Go deeper with a course
AI-ML Projects for Data Professionals
Manisha Arora and Siddarth Ranganathan
Data Science Lead, Google | Founder, PrepVector |
MIT, UT Austin & Univ of Cincinnati. Director of Data Science, Microsoft | Founder, PrepVector | USC Marshall
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