2 posts tagged with "Machine Learning"

This tutorial guides you through the process of using Vertex AI to train and deploy an AutoML Image Classification model. You'll use a public flower image dataset, train the model using AutoML, evaluate its performance, deploy it to an endpoint, and send predictions — all from the Google Cloud Console.

🧱 Step 1: Set Up Your Google Cloud Project​

Select or Create a Project​

1. Visit the Google Cloud Console project selector.
2. Choose an existing project or create a new one for this tutorial.

💡 Tip: If you don't plan to retain the resources, create a new project so cleanup is easier.

Open Cloud Shell​

Click the Activate Cloud Shell button in the top-right of the console. Once ready, set your project ID:

gcloud config set project PROJECT_ID
export projectid=PROJECT_ID
echo $projectid

Replace PROJECT_ID with your actual project identifier.

🔧 Step 2: Enable Required APIs​

Run the following command in Cloud Shell:

gcloud services enable   iam.googleapis.com   compute.googleapis.com   notebooks.googleapis.com   storage.googleapis.com   aiplatform.googleapis.com

🔐 Step 3: Set IAM Permissions​

Grant the necessary IAM roles:

gcloud projects add-iam-policy-binding PROJECT_ID   --member="user:your-email@example.com"   --role=roles/aiplatform.user

gcloud projects add-iam-policy-binding PROJECT_ID   --member="user:your-email@example.com"   --role=roles/storage.admin

These permissions allow you to use Vertex AI and access Cloud Storage for dataset management.

🗂️ Step 4: Create and Import Image Dataset​

Open Vertex AI > Datasets​

1. Click Create Dataset.
2. Select Image as the data type.
3. Choose Image Classification (Single-label).
4. Set region to us-central1.
5. Enter a name for the dataset (optional).
6. Click Create.

Import Data from Cloud Storage​

Use this public CSV containing image URIs and labels:

cloud-samples-data/ai-platform/flowers/flowers.csv

This CSV includes rows like:

gs://cloud-samples-data/ai-platform/flowers/daisy/10559679065_50d2b16f6d.jpg,daisy
gs://cloud-samples-data/ai-platform/flowers/dandelion/10828951106_c3cd47983f.jpg,dandelion

📌 You'll see a preview of your images and labels after import completes (takes a few minutes).

🧠 Step 5: Train AutoML Model​

1. Go to the Models section.
2. Click Train new model.
3. Select:
   1. Training method: AutoML
   2. Target dataset: Your image dataset
4. (Optional) Name your model.
5. Define training options:
   • Training budget: 8 node hours
   • Enable Incremental Training: Only if you have a base model
6. Click Start training.

📬 You’ll get an email notification when training finishes (can take several hours).

📈 Step 6: Evaluate Model​

Navigate to the Evaluate tab of your model:

• View key metrics:
  • Precision
  • Recall
  • Confusion Matrix
• Analyze false positives, false negatives, and true positives.
• Use Review Similar Images to find label inconsistencies or outliers.

🔍 Fix incorrect labels and re-train if needed to improve accuracy.

🚀 Step 7: Deploy Model to Endpoint​

1. Go to Deploy & Test tab.
2. Click Deploy to Endpoint.
3. Choose:
   • Create New Endpoint
   • Name: image-classification
   • Traffic Split: 100%
   • Compute Nodes: 1
4. Click Deploy.

Deployment takes a few minutes.

🧪 Step 8: Send a Prediction​

After deployment:

1. Go to Deploy & Test > Test your model.
2. Click Upload Image.
3. Choose a local image.
4. View the predicted label and confidence.

🧹 Step 9: Clean Up Resources​

To avoid charges, delete the resources:

Undeploy Model​

Vertex AI > Models > Deploy & Test > Undeploy

Delete Endpoint​

Vertex AI > Endpoints > Delete image-classification

9.3 Delete Model and Dataset​

Vertex AI > Models > Delete
Vertex AI > Datasets > Delete

Delete Cloud Storage Bucket​

Go to Cloud Storage and delete your bucket.

📚 Summary​

• ✅ Created dataset with labeled images
• ✅ Trained an AutoML image classification model
• ✅ Evaluated model performance
• ✅ Deployed to a live endpoint
• ✅ Sent predictions and cleaned up

Vertex AI simplifies machine learning workflows with a UI-based AutoML approach. Stay tuned for more advanced tutorials on custom training, multi-class classification, and model explainability.

💡 "Teaching AI has never been easier. With Vertex AI, all you need is your data."

In recent years, quantum computing has emerged as one of the most exciting technological advancements, attracting significant investor confidence. With the advent of Google’s quantum chips and increasing interest from cloud providers, quantum computing is becoming a focal point in technological innovation. This article explores the fundamentals of quantum computing, its potential applications, the challenges it faces, and how cloud platforms like AWS Braket are making quantum computing more accessible.

What is Quantum Computing?​

Quantum computing is a new paradigm of information processing that leverages the principles of quantum mechanics, including quantum coherence and entanglement, to solve complex problems that are beyond the reach of classical computers. Unlike traditional computing, which relies on bits (0s and 1s), quantum computing utilizes qubits (quantum bits). Qubits can exist in multiple states simultaneously, thanks to a phenomenon called superposition. This allows quantum computers to process vast amounts of information simultaneously, offering the potential for exponential speedups in specific problem domains.

##How Quantum Computers Work

Quantum computers operate fundamentally differently from classical computers. Instead of using conventional logic gates and memory models, quantum computers rely on qubits to store and process quantum information. Some of the key principles enabling quantum computing include:

• Superposition: A qubit can exist in multiple states at once, unlike classical bits that are either 0 or 1.
• Entanglement: When two qubits become entangled, the state of one qubit is dependent on the state of the other, even if they are separated by large distances.
• Quantum Interference: Quantum algorithms exploit interference to amplify correct solutions while canceling out incorrect ones.

While these properties offer immense computational power, quantum computing is still in its early stages, facing significant engineering and scalability challenges before it can be widely adopted.

The Potential of Quantum Computing​

Quantum computing has the potential to revolutionize multiple industries. Some of its promising applications include:

1. Cryptography​

Quantum computers can potentially break traditional encryption methods by quickly factoring large numbers, which is the foundation of modern cryptographic protocols. Algorithms like Shor’s Algorithm enable quantum computers to efficiently solve problems that are infeasible for classical computers, raising concerns about cybersecurity and prompting research into quantum-resistant encryption methods.

2. Optimization Problems​

Quantum computing can be applied to solve complex optimization problems in logistics, finance, and artificial intelligence. It can optimize supply chain logistics, improve financial portfolio management, and enhance AI model training through more efficient computation.

3. Drug Discovery and Material Science​

By simulating molecular interactions at a quantum level, quantum computing has the potential to accelerate drug discovery and the design of new materials. Traditional methods for simulating molecular structures are computationally intensive, but quantum computing offers a faster and more accurate approach.

4. Artificial Intelligence and Machine Learning​

Quantum computing can significantly enhance machine learning models by improving pattern recognition, optimization, and data classification, potentially leading to breakthroughs in AI applications.

5. Climate Modeling and Simulation​

Quantum computers can simulate complex climate models with greater precision, allowing scientists to better predict climate change patterns and explore solutions for environmental sustainability.

Challenges in Quantum Computing​

Despite its immense potential, quantum computing faces several hurdles before it can achieve widespread commercial adoption:

• Hardware Limitations: Building stable qubits is extremely challenging due to quantum decoherence, where external factors like temperature fluctuations can disturb quantum states.
• Error Rates: Quantum computations are highly error-prone, and developing effective quantum error correction techniques is crucial.
• Scalability: Currently, quantum computers have a limited number of qubits, making it difficult to solve large-scale problems.
• Cost: Quantum computing is still an expensive field, with only major tech companies and research institutions having access to cutting-edge quantum processors.

AWS Braket: Exploring Quantum Computing in the Cloud​

To facilitate research and development in quantum computing, Amazon Web Services (AWS) Braket provides a cloud-based platform for exploring, evaluating, and experimenting with quantum computing. AWS Braket offers access to different quantum hardware providers, including IonQ, Rigetti, and D-Wave, making quantum computing more accessible to businesses and researchers.

Getting Started with AWS Braket​

AWS Braket provides a seamless interface to experiment with quantum computing. Here's how to get started:

1. Access AWS Braket: Log into the AWS console and navigate to the Braket service.
2. Choose a Quantum Device: AWS offers various quantum computing devices, including simulators and real quantum processors.
3. Create and Execute Quantum Circuits: Use Jupyter Notebooks to write and test quantum algorithms.
4. Analyze Results: AWS Braket provides detailed logs and results, allowing users to optimize and refine their computations.
5. Run Hybrid Jobs: Braket enables users to execute hybrid quantum-classical computations, which combine the strengths of both quantum and traditional computing.

Key Features of AWS Braket​

• Multiple Quantum Devices: Supports various quantum computing architectures.
• Hybrid Job Execution: Combines classical and quantum computing for enhanced performance.
• Notebook Integration: Provides Jupyter Notebook support for seamless quantum algorithm development.
• Scalability & Flexibility: Allows users to experiment with quantum algorithms without investing in expensive quantum hardware.

Future of Quantum Computing​

The future of quantum computing remains uncertain, with estimates suggesting that practical applications could emerge within the next 20 years. While some experts believe quantum computing is still a decade away from mainstream adoption, others argue that recent advancements indicate it could become commercially viable sooner.

As research continues, the industry will likely see breakthroughs in error correction, qubit stability, and quantum algorithms, paving the way for real-world applications across multiple sectors.

Conclusion​

Quantum computing is poised to transform industries by solving complex problems that classical computers cannot handle efficiently. While challenges remain, continuous advancements in hardware, software, and cloud integration (such as AWS Braket) are making quantum computing more accessible. Businesses and researchers interested in this cutting-edge field should start exploring its potential today to stay ahead in the quantum revolution.

As quantum computing continues to evolve, staying informed about the latest developments and experimenting with quantum algorithms will be crucial for leveraging its future impact. Whether you're a researcher, developer, or technology enthusiast, now is the perfect time to start your quantum journey.

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