DEV Community: Huzaifa Bin Tariq

My Journey Through Google Cloud GenAI Academy APAC Edition Cohort 3 — 2026

Huzaifa Bin Tariq — Sun, 20 Sep 2026 16:41:07 +0000

My journey with the Google Cloud GenAI Academy APAC Edition Cohort 3, organized by Hack2Skill in collaboration with Google Cloud, was my first proper learning experience with Google Cloud and AI agents. Before this program, I had worked with generative AI and built AI-based projects, but this academy gave me an opportunity to understand how AI agents can connect models with tools, data, applications, and cloud infrastructure.

The academy was not just about watching videos or reading theory. It included hands-on workshops, codelabs, and quizzes, followed by an Ideathon Prototype Submission where we had to turn what we learned into a working project.

I received my Gen AI Master Certificate from the Google Cloud GenAI Academy APAC Edition Cohort 3.

The program was divided into three main tracks:

Track 1 — Build and Deploy a Customer-Facing AI Agent
Track 2 — Turn Business Data into Strategic Decisions
Track 3 — Automate Daily Operations with a Productivity Agent

Each track included workshops, hands-on codelabs, and quizzes. The biggest learning for me was seeing how an AI agent can move beyond a simple chatbot and actually work with data, tools, and cloud services.

Track 1 — Build and Deploy a Customer-Facing AI Agent

The first track introduced me to building an AI agent for customer-facing applications.

One of the hands-on codelabs focused on deploying a RAG AI Agent in Streamlit using Google ADK and Cloud Run.

The example was based on a coffee shop. The goal was to build an AI Barista agent that could answer customer questions and make recommendations based on a menu dataset.

The codelab introduced several important concepts:

Google Agent Development Kit (ADK) for building the AI agent
Gemini as the underlying AI model
Retrieval-Augmented Generation (RAG) to ground the agent's responses in external information
A menu dataset containing coffee items, tags, and allergens
Streamlit for creating the chat interface
Cloud Run for deploying the application

What I found interesting was that the agent was not simply generating answers from the model's general knowledge. It could retrieve information from the menu data and use that information to provide more relevant recommendations, including allergen-related information.

This was my first practical experience seeing how an AI agent, RAG, an application interface, and cloud deployment can work together as one system.

Track 2 — Turn Business Data into Strategic Decisions

The second track focused on using AI agents with business data.

The codelab introduced building and deploying AI agents with Gemini and the BigQuery MCP Server on Cloud Run.

This track helped me understand how an AI agent can interact with structured data instead of only responding to normal text prompts.

The technologies and concepts included:

Agent Development Kit (ADK)
Gemini
BigQuery
BigQuery MCP Server
Model Context Protocol (MCP)
Cloud Run

The idea of MCP was especially interesting to me. Instead of keeping an AI agent isolated, MCP provides a standardized way for AI applications and agents to connect with external tools and data sources.

With the BigQuery MCP Server, an AI agent can access and analyze structured business data stored in BigQuery.

This showed me another side of AI agents: they can act as an interface between users and complex data systems, helping turn raw data into useful information and decisions.

Track 3 — Automate Daily Operations with a Productivity Agent

The third track focused on using an AI agent to automate operational tasks.

The codelab demonstrated a personal agent running on a Cloud Run service, using a coffee shop manager scenario.

The agent could analyze business data and perform tasks through a chat interface.

One of the interesting parts was the use of Cloud Run sandboxes. The agent could dynamically write and execute Python code inside a secure, isolated environment to solve analytical problems.

For example, the coffee shop manager could have the agent analyze Point-of-Sale data together with a university graduation schedule. The agent could then identify possible operational bottlenecks, such as relationships between drink complexity and cashier staffing.

The agent could generate recommendations for:

Staffing
Inventory
Operational planning

It could also prepare operational TODOs, while requiring explicit permission before making changes.

This track helped me understand that productivity agents are not limited to answering questions. They can analyze information, execute code, and assist with real operational workflows.

From Learning to Building — The Ideathon

After completing the learning tracks, the academy moved from learning concepts to actually building a prototype.

The Ideathon Prototype Submission challenged us to bring an idea to life using Google AI and Google Cloud technologies.

The submission required a working prototype along with a social post explaining the solution, technical approach, and use of Google Cloud tools.

The evaluation criteria focused on four areas:

Authenticity — an original and genuinely built solution
Usability — an intuitive and accessible experience
Stability — a reliable working prototype
Security — appropriate practices for authentication, data protection, and cloud infrastructure

For my submission, I built Reflectora-AI.

Reflectora-AI — A Personal AI Journal

Reflectora-AI is a personal AI-powered journaling application designed to help users reflect on their thoughts and turn them into useful insights.

Instead of treating journaling as just storing text, I wanted to explore how generative AI could help users interact with their own journal entries in different ways.

The application includes different AI modes such as:

Reflect — explore thoughts and provide reflective responses
Summarize — turn longer journal entries into concise summaries
Brainstorm — generate ideas based on the user's thoughts
Action Plan — turn reflections into practical next steps
Ask Gemini — interact with the AI about the journal content

The project also focuses on user authentication and personal data management.

For the prototype, I worked with Google technologies including Google AI Studio, Gemini, and Google Cloud services, while exploring concepts around authenticated AI applications and cloud deployment.

The project github repository is available here.

I also created a project demonstration video showing the prototype and how it works.

What I Learned

The biggest takeaway from the academy was understanding that AI agents are more than chat interfaces.

Through the three tracks, I saw different ways an agent can:

Interact with customers and retrieve information.
Connect with structured business data.
Execute code and assist with operational workflows.
Work with tools and external systems.
Be deployed as real applications using cloud infrastructure.

The academy also gave me more confidence in exploring Google Cloud and agentic AI development.

Before the program, concepts such as ADK, MCP, RAG, Cloud Run, and AI agents were areas I was still exploring. After completing the hands-on tracks and building my own prototype, these technologies became much more practical and understandable.

I am now looking forward to continuing my journey with Google Cloud, Gemini, AI agents, and cloud-based application development and building more practical projects along the way.

How I Cleared AWS Cloud Quest: Cloud Practitioner and What I Learned

Huzaifa Bin Tariq — Wed, 16 Sep 2026 04:55:04 +0000

I recently completed AWS Cloud Quest: Cloud Practitioner, and honestly, it was one of the most enjoyable ways I have learned cloud computing so far.

What made it different for me was the gamified learning experience. Instead of only watching lectures and taking quizzes, I was solving challenges, building cloud solutions, completing assignments, and moving through a virtual environment.

It actually felt more like playing a game while learning cloud concepts.

What I Learned

During the quest, I completed hands-on challenges covering different areas of AWS, including:

Cloud Computing Essentials
Cloud First Steps
Computing Solutions
Networking Concepts
Cloud Economics
Databases in Practice
Connecting VPCs
First NoSQL Database
File Systems in the Cloud
Core Security Concepts
Auto-Healing and Scaling Applications
Highly Available Web Applications

One thing that really stood out to me was how many different parts are involved in building a reliable cloud application.

For example, creating an EC2 instance or an S3 bucket is only one part of the process. Security is also very important. You need to understand who can access your resources, what permissions they have, and how to protect your cloud environment.

Learning about IAM, networking, security, and best practices helped me understand that in the cloud, simply making something work is not enough. It also needs to be secure, reliable, and properly managed.

I also enjoyed learning about cloud economics. Before this, I mostly thought of cloud computing as using online servers and services. But the quest showed me that choosing the right resources and understanding how they are used can have a big impact on cost.

Things like avoiding unnecessary resources, choosing the right services, and scaling based on demand are important when working with cloud platforms.

The sections on VPCs, databases, file systems, auto-healing, scaling, and highly available applications also helped me connect individual AWS services with the bigger picture.

Instead of seeing each service separately, I started to understand how different AWS services can work together to build applications that are more reliable and scalable.

My Takeaway

Overall, AWS Cloud Quest gave me a much better understanding of the basics of cloud computing and AWS.

The biggest thing I liked was that I was not just reading about concepts. I was actually doing things and solving problems, which made the learning process much easier to remember.

If you are starting with AWS or cloud computing and want something more interactive than a traditional course, I would definitely recommend giving AWS Cloud Quest: Cloud Practitioner a try.

Here's my Credly badge.

I'm looking forward to building more projects with AWS and continuing my cloud learning journey.

Attending my first-ever tech event, an AWS event!

Huzaifa Bin Tariq — Mon, 07 Sep 2026 10:48:03 +0000

Huzaifa Bin Tariq

Sep 7

My First AWS Event — A New Start

#awscommunity #devops #cloudcomputing #pakistantech

2 min read

My First AWS Event — A New Start

Huzaifa Bin Tariq — Mon, 07 Sep 2026 10:38:54 +0000

Recently, I attended my first ever tech event, and honestly, it was a very different experience for me because it was my first AWS event.

Before this event, most of my focus was on web development and full-stack development. I was mostly thinking about HTML, CSS, JavaScript, frontend, backend and building websites. But AWS introduced me to a completely different side of technology.

During the event, I heard a lot of new terms like S3, EC2, CI/CD pipelines, Cloud, DevOps and many more. At first, some of these words were totally new for me, hehe. I knew about cloud and AWS, but I never really explored them deeply.

The interesting part was that I started understanding how a website or application is not only about writing code. There is also a whole world behind it — where applications are hosted, how servers work, how data is stored, how applications are deployed, and how developers can automate these things.

This really changed my mindset.

I started thinking beyond just “How do I build this website?” and more about “How does this application actually work in the real world?”

I also got introduced to DevOps and cloud technologies in a much better way. I realized that there is so much more to learn after becoming a web or full-stack developer.

Another thing I really enjoyed was meeting new people and listening to experienced speakers. Their talks and experiences gave me a better idea about the industry and the importance of continuously improving your skills.

One thing I learned from this experience is that skills matter, but learning how to use those skills in the right way matters even more. And one more lesson for me was about AI. AI can help us a lot, but blindly trusting AI is not the right way. We still need to understand things ourselves, verify information and build our own skills.

What I liked most about this event was not just learning about AWS. It made me more interested in attending tech events, meeting people, learning from others and exploring areas outside my usual comfort zone.

This was just my first AWS event, but I feel like it opened a new door for me — towards Cloud, AWS and DevOps.

A big thanks to AWS User Group Islamabad and everyone who made this event possible. And special thanks to the amazing speakers Zunaira Shafaq and Sharoon Amin Akhtar for sharing their knowledge and experiences.

Looking forward to learning more, building more and attending more events! Have you ever attended an AWS or cloud event that changed the way you think about technology? I'd love to hear about your experience. Here’s my LinkedIn post about the event if you’d like to check it out.