Livingdatalab is the professional blog of Pranath Fernando, Data Scientist. Filter articles by topic on the Tags page.

Posts

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  Advanced Model Deployment on AWS - A/B testing traffic shifting and autoscaling

  AWS Sagemaker offers many options for deploying models, in this project we will create an endpoint for a text classification model, splitting the traffic between them. Then after testing and reviewing the endpoint performance metrics, we will shift the traffic to one variant and configure it to autoscale.

  Feb 22, 2023

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  Optimize Models in the Cloud using AWS Automatic Model Tuning

  When training ML models, hyperparameter tuning is a step taken to find the best performing training model. In this article we will apply a random algorithm of Automated Hyperparameter Tuning to train a BERT-based natural language processing (NLP) classifier. The model analyzes customer feedback and classifies the messages into positive, neutral, and negative sentiments.

  Feb 14, 2023

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  Building an AWS SageMaker Pipeline to train and deploy a BERT-Based text classifier

  In this project we train and deploy a BERT-Based text classifier using AWS Sagemaker pipelines, and describe how this can help with MLOPS to provide the most efficient path to production for training, deploying and maintaining machine learning models at scale in production.

  Feb 12, 2023

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  Train a Review Classifier with BERT and Amazon SageMaker

  We train a text classifier using a variant of the BERT deep learning model architecture called RoBERTa - a Robustly Optimized BERT Pretraining Approach, within a PyTorch model ran as a SageMaker Training Job.

  Feb 11, 2023

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  Feature Transformation with Amazon SageMaker Processing Job and Feature Store

  We will prepare to train a BERT-based natural language processing (NLP) model converting review text into machine-readable features used by BERT. With the required feature transformation we will configure an Amazon SageMaker processing job to perform the task.

  Feb 8, 2023

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  Creating a Sentiment Analysis Text Classification Model using AWS SageMaker BlazingText

  In this article we will use the AWS SageMaker BlazingText built-in deep learning model to predict the sentiment for customer text reviews. BlazingText is a variant of FastText which is based on word2vec.

  Feb 6, 2023

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  Train a model quickly with Amazon SageMaker Autopilot

  We will use Amazon Sagemaker Autopilot to automatically train a natural language processing (NLP) model. The model will analyze customer feedback and classify the messages into positive (1), neutral (0) and negative (-1) sentiment.

  Feb 5, 2023

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  Detect data bias with Amazon SageMaker Clarify

  In Data Science and machine learning, bias can be present in data before any model training occurs. In this article we will analyze bias on a dataset, generate and analyze bias reports, and prepare the dataset for the model training.

  Feb 4, 2023

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  Loading & Transforming Clothing Reviews Text Data with AWS

  In this project we will explore text reviews for clothing products using tools from the cloud data science service AWS Sagemaker to load and visualise the data and to gain key insights from it.

  Feb 3, 2023

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  Using Satellite Images and Deep Learning to Track Deforestation in the Amazon

  In this project we will be using a deep learning model to classify satellite images of the amazon rain forest. Here the main objective is not to get the best results for this task, rather to use this dataset to illustrate the use of the Fastai deep learning library

  Jan 15, 2023