Use Case – Medical Image Classification
Uses models based on Convolutional Neural Net and Transformer Models.
On Google Cloud Platform (GCP), several services support Convolutional Neural Networks (CNNs) and Transformer models, enabling you to train, deploy, and scale these deep learning models. Here are the primary GCP services that are most relevant for CNNs and Transformer-based models:
1. Vertex AI
- Custom Model Training: Vertex AI supports custom deep learning models, including CNNs and Transformers, with popular frameworks like TensorFlow, PyTorch, and Keras. You can leverage pre-built notebooks, distributed training, and hyperparameter tuning on this platform.
- Managed Pipelines: Vertex AI provides a managed pipeline to orchestrate complex workflows that involve training CNN or Transformer models, including data preprocessing, model training, and deployment.
- Model Deployment: You can deploy trained models to Vertex AI endpoints for scalable online predictions.
2. TensorFlow Enterprise
- Optimized for GCP: TensorFlow Enterprise is specifically optimized for GCP and supports training and deploying CNNs and Transformers. TensorFlow is particularly well-suited for building models like CNNs for image classification or Transformers for NLP tasks.
- TPU Support: TensorFlow Enterprise integrates seamlessly with TPUs, providing faster training times for CNNs and large Transformer models.
3. TPU (Tensor Processing Units)
- High Performance for Training and Inference: TPUs are specialized hardware accelerators designed to speed up deep learning workloads. They are particularly beneficial for training large-scale CNNs and Transformers, offering significant speed improvements over traditional GPUs.
- TensorFlow and PyTorch Compatibility: TPUs are optimized for TensorFlow but also support PyTorch, making it easier to train CNN and Transformer models that require large-scale computation.
4. AI Platform Deep Learning VM Images
- Preconfigured VMs: These virtual machines come preinstalled with popular deep learning frameworks like TensorFlow, PyTorch, and JAX, along with support for CNN and Transformer model training.
- GPU and TPU Support: You can choose VMs with GPUs (NVIDIA) or TPUs to accelerate the training of CNNs and Transformer models.
5. Deep Learning Containers
- Customizable Environments: GCP offers containerized environments pre-configured with TensorFlow, PyTorch, and other ML libraries. These containers support CNNs and Transformers and can be deployed on Google Kubernetes Engine (GKE) or AI Platform for training and inference.
6. Google Kubernetes Engine (GKE)
- Scalable Model Deployment: GKE is a good choice for deploying large-scale CNN and Transformer models in production. You can deploy containerized deep learning models using the flexibility of Kubernetes while managing scalability and reliability.
7. BigQuery ML
- Basic Support for Neural Networks: While BigQuery ML is primarily focused on traditional ML, it has support for basic neural networks and can be extended to support CNN-like and Transformer-like architectures. However, for complex deep learning models, Vertex AI is more suitable.
8. AutoML Vision (for CNNs) and AutoML Natural Language (for Transformers)
- No-Code Model Training: AutoML services provide an easy-to-use interface for training CNNs (for image classification) and Transformer models (for NLP tasks) without requiring in-depth knowledge of ML model building. AutoML handles the model architecture and training pipeline automatically.