Best Way to Start Learning Google Cloud for Data Engineering + AI/ML in 2025?

[keiralewis]

Hi everyone,

I'm currently exploring career opportunities in data engineering and AI/ML, and I want to build my skills using Google Cloud Platform (GCP).
I'm especially interested in hands-on learning paths that combine:

• Data pipelines (BigQuery, Dataflow, Pub/Sub)
• Machine learning tools (Vertex AI, AutoML, etc.)
• Real-world use cases and projects
• Certification guidance (like Professional Data Engineer or ML Engineer)

I’ve seen a lot of scattered resources online—Coursera, Qwiklabs, etc.—but it’s a bit overwhelming. I’d love to hear from this community:

• What learning path or combination of tools helped you the most?
• Are there any updated GCP learning tracks for 2025 you'd recommend?
• How important are certifications right now in the GCP ecosystem?
• Any tips for building a portfolio that highlights GCP data/ML skills?

Open to both beginner-friendly and advanced suggestions. Thanks in advance for sharing your insights—it really helps those of us just starting out.

Looking forward to hearing from you all!

[dawnberdan]

Hi @keiralewis,

Welcome to Google Cloud Community!

Here’s a simple guide to learning paths, tools, certifications, and portfolio tips based on the latest best practices for data engineering and AI/ML on Google Cloud:

1. Start with Google Cloud Skills Boost — This is Google’s official learning platform, offering curated learning paths, hands-on labs, and skill badges.
   
   
2. Google Cloud Next ’25 Sessions — At Google Cloud Next 2025, new hands-on workshops and challenges were introduced, including Vertex AI Studio, Gemini in BigQuery, and building chat apps using the Gemini API on Cloud Run. Make sure to check out the Next ’25 learning resources for recordings and labs to get hands-on experience with these tools.
   
   
3. GCP Certification Path for 2025 — Are certifications worth it in 2025? Definitely. GCP certifications remain highly valued by employers and can boost your credibility and earning potential.

For you to be able to build a strong GCP portfolio, focus on real projects using GCP tools. Share your code, notebooks, and architecture diagrams on GitHub, and include skill badges from Google Cloud Skills Boost. Don’t forget to clearly explain how you approached and solved each problem. 

Was this helpful? If so, please accept this answer as “Solution”. If you need additional assistance, reply here within 2 business days and I’ll be happy to help.

[keiralewis]

Hii @dawnberdan 

Thank you so much for the warm welcome and this incredibly detailed response!

The breakdown of resources like Google Cloud Skills Boost and Next ’25 sessions is super helpful—especially the hands-on labs with Vertex AI Studio and Gemini in BigQuery. I’ll definitely explore those recordings and labs for practical exposure.

Also, really appreciate the portfolio tips. I hadn’t thought of including architecture diagrams along with my GitHub projects, but that’s a great idea. I’ll also work on collecting skill badges to showcase my learning journey.

This gives me a much clearer direction—thank you again for taking the time to share this!