Why You NEED to Learn AI Engineering

In Today’s Cloudbites:

🧠 Why You NEED to Learn AI Engineering

🧭 How to Start Learning AI Engineering

☁️ Online AI & Cloud Events to Look Forward to

⚡ PLUS: Google Just Made Quantum Computing 13,000× Faster

Read time: 5 minutes

Hi friends, welcome back to Cloudbites

In this newsletter, we’ll look at why AI is becoming an important part of every cloud and tech career.

You’ll see how AI is changing the way we build, deploy, and automate systems across the industry.

Plus, we’ll explore Google’s new quantum leap that’s 13,000× faster than today’s supercomputers.

CLOUD COMPUTING ☁️

🧠 Why You NEED to Learn AI Engineering

AI engineering is one of the fastest-growing and highest-paying fields in technology.

Learning it doesn’t mean you have to change your career, it’s a skill that makes you really strong in your work.

What is AI Engineering

AI Engineering is the process of turning ideas into real products with AI, from developing models to building complete AI systems.

AI Engineers know a bit of everything: machine learning, cloud, and software engineering.

Why Learn AI Engineering?

AI and machine learning roles are among the fastest-growing in tech, with salaries well above traditional Cloud or IT roles.

According to PwC, workers with AI skills, including prompt engineering, receive a 56% wage premium.

AI Engineering gives you leverage, it lets you automate work, speed up projects, and build tools others rely on.

🎥 Watch the full breakdown:

🎥 Check out my video on Why You NEED to Learn AI Engineering (& How to Get Started), where I explain what AI Engineering is, a beginner’s roadmap you can follow, as well as some resources to get you started.

🧭 How to Start Learning AI Engineering

Here’s the roadmap to learn AI Engineering:

#1 Learn Python and basic machine learning:

Get comfortable with libraries like NumPy and Pandas, they’ll help you understand how data flows through a model.

#2 Get comfortable with APIs and AI frameworks:

Learn how to use APIs like OpenAI and Hugging Face, and integrate them into small projects like a chatbot, summarizer, or AI-powered tool.

#3 Learn LLMOps and deployment:

If you know cloud, use services like AWS Lambda, Google Cloud Run, or Azure ML to deploy your models. Learn Docker and Git for containerization and version control.

#4 Build projects:

Create 2-3 small, real-world projects that combine AI and cloud. The best project is something that makes your own life easier.

#5 Share your work:

Document your journey on LinkedIn, publish on GitHub, and build your portfolio, your skills only matter if people can see them.

⏳ Timeline:

If you’re putting in 10-15 hours a week, you can go from beginner to building your first AI app in 3 - 4 months, the key is consistency over intensity.

☁️ Online AI & Cloud Events to Look Forward to

#1 AWS Edge Services Immersion Day (November 18, 2025)

This event is a hands-on workshop focused on improving your architecture with Amazon CloudFront and securing your applications at the edge.

Click here to register.

#2 Microsoft Ignite (November 18-21, 2025)

Microsoft's premier conference for developers and IT professionals. The free virtual experience focuses on the future of AI (Copilot), Azure, Microsoft Cloud, security, & hybrid multi-cloud solutions.

Click here to register.

#3 Google Cloud: AI Automation Workshop (Nov 19) (Nov 19, 2025)

Learn how small teams can use AI for workflow automation with Google Cloud’s tools.

Click here to register.

ARTIFICIAL INTELLIGENCE (AI) 🤖

⚡ PLUS: Google Just Made Quantum Computing 13,000× Faster

Google has unveiled a new quantum-computing algorithm called Quantum Echoes, which reportedly runs 13,000× faster than the world’s most powerful supercomputers, powered by its Willow QPU (Quantum Processing Unit).

💡 Here’s what you should know:

• It marks a major leap toward useful quantum computing, not just lab research.

• For cloud and AI engineers, this signals the rise of quantum + cloud convergence, a whole new compute frontier.

• It could fundamentally reshape how large-scale models and infrastructure are deployed in the next decade.

THAT’S A WRAP

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Thanks for reading! 😊

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