Machine Learning Architect: For all your AI and GenAI needs
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Use Case – Medical Image Classification
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…
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Clustering Use Cases – Unsupervised Machine Learning on GCP
Clustering is one of the most common patterns in Unsupervised machine learning. Some areas / use cases where we can apply clustering include: Market segmentation Social network analysis Search result grouping Medical imaging Image segmentation Anomaly detection BigQuery ML: Is ideal for clustering use cases (and SQL-based machine learning use cases). Apart from BQML, GCP…
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Use Case – Text Generation, NLP, Sentiment Analysis – Transformers
Transformers BERT (Bidirectional Encoder Representations from Transformers): Used for a variety of NLP tasks like sentiment analysis and question answering. GPT (Generative Pre-trained Transformer): Used for text generation and completion tasks. Vision Transformers (ViT): Adapted for image classification tasks, where the image is divided into patches that the transformer processes as sequences.Architecture: Self-Attention Mechanism: The…
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Use Case – Remove noise from images, generate new sample images
Autoencoders are a type of artificial neural network used for unsupervised learning tasks. They are designed to learn efficient representations of data, typically for the purpose of dimensionality reduction or data compression. The basic idea is to encode the input data into a lower-dimensional representation and then decode it back to the original input data.…
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Use Case – Online Forecasting Model for Various Interfaces
Online Forecasting Model that needs to work across Web UI, Google Assistant as well as Dialogflow. Vertex AI Prediction Service is a fully managed service – designed to handle at scale requests. It supports both online and batch predictions. On the other hand, if it is not an online model and your dataset is related…
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Use Case – Labeling large datasets
For Large Datasets – Use Vertex AI’s Data Labeling for your classification model
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Use Case – Sudden Inference Degradation
If the quality of inferences suddenly goes down, ensure that you have Model Monitoring turned on (in Vertex AI). It continuously tracks performance of your model in Production Monitored Metrics include: ACCURACY PRECISION RECALL
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Use Case – Build a Recommendation Engine – Different Model Performances against Test and Training Data
Use Vertex AI Experiment – to explore the output of different models, while keeping track of the inputs and various stages of the runs.
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Use Case – Small dataset in Machine Learning , Quick Prototype to build
For less than 10,000 records, use Jupyter notebooks for model prototyping. Interactive Development and Testing of the model is possible.
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Supervised or Unsupervised – for CNN and Feedforward NNs?
Feedforward neural networks (FNNs) and convolutional neural networks (CNNs) can both be used in supervised learning or unsupervised learning, depending on how they are applied and the nature of the task at hand. ### Supervised Learning: – **Feedforward Neural Networks (FNNs)**: Typically used in supervised learning tasks where the goal is to predict a target…
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