"Getting Started with Google Cloud for AI-Powered Applications: Best Practices and Tips for Beginner

[sowmiya3104]

Getting Started with AI/ML on Google Cloud: Guidance for Beginners

Hello everyone,

I’m Sowmiya R, a 3rd-year ECE student keen on exploring AI/ML technologies to develop impactful applications. I’m new to Google Cloud and currently working on integrating AI into my projects.

One of my primary goals is to leverage Google Cloud’s AI/ML capabilities to build solutions like:

• Training machine learning models efficiently with Vertex AI or AutoML.
• Deploying real-time predictive models using Cloud AI APIs.
• Managing large datasets with BigQuery ML for insights and analysis.

As a beginner, I’d love some guidance on:

1. Choosing between AutoML and building custom models for small-scale projects.
2. Best practices for setting up and scaling AI/ML workflows on Google Cloud.
3. Managing costs while using services like Vertex AI or BigQuery ML.

If you have tips, tutorials, or experiences with Google Cloud’s AI/ML tools, I’d love to learn from you. Let’s collaborate and share insights to grow together in this exciting field!

Thank you in advance!
Sowmiya
@

[dawnberdan · Accepted solution]

Hi @sowmiya3104,

Welcome to Google Cloud Community!

It’s great to hear that you’re diving into AI/ML with Google Cloud! Since you’re in your third year as an ECE student, you have a lot of exciting opportunities ahead. I’d be happy to help guide you as you explore Google Cloud’s AI/ML capabilities. Below are some tips and suggestions to get you started, along with answers to the specific questions you’ve posed.

1. AutoML vs Custom Models

• AutoML: Ideal for beginners or small-scale projects. It’s easy to use, doesn’t require deep ML expertise, and handles model training automatically. Best for simple use cases (e.g., image classification, text analysis).
• Custom Models: Best for more complex projects or when you need control over model architecture. Use Vertex AI to build custom models with TensorFlow, PyTorch, etc.

2. Best Practices for AI/ML Workflows

• Vertex AI Pipelines: This tool helps you define, manage, and automate end-to-end machine learning workflows. It allows you to automate tasks like data preprocessing, training, and model evaluation.
• BigQuery ML: If your data is stored in BigQuery, you can use BigQuery ML to build ML models directly on your data without moving it. This eliminates data transfer costs and speeds up your workflow.
• Experiment Tracking: Use Vertex AI Experimentation to track models, datasets, and results.
• Auto-Scaling: Utilize auto-scaling features for cost-efficiency (e.g., preemptible VMs for cheap model training).

3. Managing Cost: Managing cloud costs while using AI/ML services can be tricky, but there are several strategies to keep it under control.

• Use Preemptible VMs: For non-time-sensitive tasks like model training, preemptible virtual machines (VMs) are a great way to reduce costs. They are much cheaper than regular VMs, though they can be stopped at any time by Google Cloud.
• Optimize Resource Usage: Always monitor your resource usage (e.g., CPU, GPU, memory). You can do this through Google Cloud’s Cost Management tools and Stackdriver. Try to adjust the compute resources (e.g., use smaller instance types or fewer GPUs) to keep the costs in check.
• BigQuery ML: BigQuery ML charges based on the amount of data processed during model training. Optimize your SQL queries to reduce unnecessary scans and make sure to clean and pre-process your data before feeding it into BigQuery.
• Take Advantage of Free Tiers: Google Cloud offers free tiers for many services. For example, BigQuery has a free tier that allows you to query up to 1 TB of data per month for free. Use this for smaller-scale experimentation.
• Monitor with Budgets & Alerts: Set up budgets and alerts in Google Cloud’s Billing Console to prevent unexpected cost spikes.

Here are a few tutorials and resources to get you started:

• Google Cloud AI Documentation is the best place to start. It contains step-by-step tutorials and examples of how to use their various services.
• Vertex AI documentation for documents on training custom models, deploying them, and building AI pipelines.
• Google offers a variety of free and paid training courses to get hands-on experience with their products. You can find them on the Google Cloud Skills Boost platform.
• Google Cloud has dedicated resources to help you integrate TensorFlow with their cloud services. Check out their TensorFlow on Cloud documentation.

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.

[sowmiya3104]

Thank youuu😊it will be very useful for me!