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Getting Started with TensorFlow on Google Cloud
- 1. Mariam Ali Aslam- GDE Firebase TensorFlow Google Cloud
- 2. Introduction ● Mariam AliAslam ● AI SDET Architect at RAKBank ● Google Developer Expert for Firebase ● Manager GDG Cloud Emirates ● Community Champion BrowserStack ● Ambassador Cypress.io ● Ambassador K6 ● Community Leader WomenInTesting ● https://www.linkedin.com/in/mariam-aslam/
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- 5. What is AI? ●AI stands for Artificial Intelligence. ● Teaching computers to think like us seemed like a good idea at the time. Spoiler alert: They’re getting pretty good at it! ● The term "Artificial Intelligence" was coined in 1956 long before the internet.
- 6. What is TensorFlow? ●Imagine teaching your computer to think (or at least pretend to). ● It's the secret sauce behind everything cool Google does (like suggesting videos of cats). ● TensorFlow has over 160,000 stars on GitHub. That's more stars than your last science project!
- 7. What is TensorFlow? 1.Open-source machine learning framework developed by Google. 2. Supports deep learning, neural networks, and a wide range of machine learning tasks. 3. Extensive community and industry support. 4. Flexibility to run on various platforms (CPUs, GPUs, TPUs). 5. Comprehensive ecosystem with tools for model building, training, and deployment.
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- 9. Why Google Cloudfor TensorFlow? ● Easily scale from a single machine to distributed systems. ● Seamless integration with Google Cloud services like BigQuery, cloud storage, and AI platforms. (Lego Blocks) ● Pay-as-you-go model with options to reduce costs ● Fully managed services for TensorFlow, simplify deployment and scaling.
- 10. What does TensorFlowhelp with
- 11. What does TensorFlowhelp with
- 12. TensorFlow Architecture forModel Building 1. Data Preprocessing: prepare data for the purpose of feeding it to the model that you need to build. 2. Model Building: start model building, create your model by using various algorithms. 3. Model training & evaluation: start training and evaluating it to check whether it generates accurate results or not.
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- 14. Using TensorFlow: Localvs. Cloud Aspect TensorFlow Locally TensorFlow on Google Cloud Setup & Installation Install via pip on your local machine. Set up using Google Cloud AI Platform or Vertex AI. Data Handling Load and preprocess datasets locally. Store and preprocess data using Google Cloud Storage. Model Training Train models using local CPU/GPU resources. Utilize Google Cloud GPUs/TPUs for faster, scalable training. Scalability Limited to local hardware capabilities Scalable with cloud resources, including distributed training. Flexibility Ideal for small projects or development. Best for large-scale, production- level models. Cost Free for local tasks; no cloud costs. Pay-as-you-go based on cloud resource usage.
- 15. Setting Up TensorFlowon Google Cloud ● Create a Google Cloud Account. ● Set up a new project ● Install Google Cloud SDK. ● Launch a virtual machine (VM). It can also be used with Python, and JavaScript.
- 16. Training Models onGoogle Cloud AI Platform ● AI Platform ○ It’s like a gym for your AI models. Let’s make them strong and smart! ● Steps to Train a Model ○ Upload your dataset—think of it as feeding your AI. ○ Write a training script—this is like planning your AI’s workout. ○ Configure the training job—select your machine (CPU? GPU? It’s like choosing your AI’s treadmill). ○ Start the training job and cheer on your AI—go, little buddy, go!
- 17. Deploying TensorFlow Modelswith AI Because keeping your AI to yourself is like making an amazing cake and not sharing it. Deploy it so the world can enjoy! ● Steps to Deploy ○ Export your trained model—it’s like baking that cake. ○ Upload it to Google Cloud Storage—put it on display. ○ Deploy on AI Platform—now everyone can have a slice! ○ Use REST or gRPC APIs to interact with the deployed model.
- 18. Using TensorFlow Servingfor Deployment ● TensorFlow Serving ○ A flexible, high-performance serving system for machine learning models. Imagine your AI model is a chef. TensorFlow Serving is the kitchen where the chef works 24/7. ● Deploying on Compute Engine ○ Set up TensorFlow Serving on a Compute Engine VM - make sure your kitchen is ready. ○ Serve the model via REST or gRPC APIs for real-time predictions —let the chef cook and serve dishes to hungry customers!
- 19. Using TensorFlow Servingfor Deployment
- 20. Integrating TensorFlow withGoogle Cloud Services ● BigQuery ML: ○ Train models directly in BigQuery on large datasets without data transfer. ○ Train models on huge datasets. It’s like taking your AI to a buffet—so much data to chew on! ● Cloud Storage: ○ Store and manage training data and model artifacts in Google Cloud Storage. ○ Think of this as the fridge where you store all your ingredients (datasets and models). ● TensorFlow Extended (TFX): ○ TFX is the conveyor belt that automates your AI kitchen—keeping everything running smoothly.
- 21. Best Practices forTensorFlow on Google Cloud ● Optimize Model Performance: ○ Use TPUs for faster training on large models. ○ Implement hyperparameter tuning with AI Platform. ○ Use TPUs—they’re like the AI equivalent of a turbo boost! ● Cost Management: ○ Monitor resource usage and costs with Google Cloud Monitoring. ○ Use preemptible VMs for cost-effective training jobs. ○ Monitor usage to avoid a “Cloud Hangover” (aka bill shock). ● Security: ○ Keep your AI safe—like not letting your pet robot wander off on its own.
- 22. Best Practices forTensorFlow on Google Cloud
- 23. Real-World Use Cases Healthcare: AIdoctors—because even doctors need a break (and a second opinion). Finance: AI money managers—ensuring you don’t spend all your money on AI puns. Retail: AI shopping assistants—because shopping is more fun with a helpful robot.
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Editor's Notes
- #9 Scalability: Your models can go from 0 to 100 real quick! (No, seriously, it scales automatically.) Integration: Think of it like Lego blocks, but for AI. Everything fits together perfectly! Cost-Effective: Pay only for what you use—like buying just one slice of pizza instead of the whole pie. Managed Services: Google does the heavy lifting. You just sit back, sip your coffee, and watch the magic happen.
- #12 removing duplicate values, feature scaling. standardization, and many other tasks.
- #16 Interactive Element: Audience Guess: "What do you think happens if you skip leg day in AI training? (Hint: Your model might trip over numbers.)"
- #17 Fun Analogy: “Deploying is like opening a food truck—let the world taste your AI creation!”
- #21 Interactive Element: "Who here has ever accidentally left something important outside in the rain? (Hint: That’s why we need security!)"
- #23 Interactive Element: "Imagine your AI is running your life. What’s the first task you’d give it? (I’d start with making breakfast.)"