The MLOps Blog

JUNE 27, 2023

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring. Data monitoring tools help monitor the quality of the data.

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Machine Learning Machine Learning ML ML 134

AWS Machine Learning Blog

APRIL 24, 2023

Dimension reduction techniques can help reduce the size of your data while maintaining its information, resulting in quicker training times, lower cost, and potentially higher-performing models. Amazon SageMaker Data Wrangler is a purpose-built data aggregation and preparation tool for ML. Choose Create.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Each step of the workflow is developed in a different notebook, which are then converted into independent notebook jobs steps and connected as a pipeline: Preprocessing – Download the public SST2 dataset from Amazon Simple Storage Service (Amazon S3) and create a CSV file for the notebook in Step 2 to run.

ML 101

ML ML Data Scientist Python 101

AWS Machine Learning Blog

AUGUST 4, 2023

Train a recommendation model in SageMaker Studio using training data that was prepared using SageMaker Data Wrangler. The real-time inference call data is first passed to the SageMaker Data Wrangler container in the inference pipeline, where it is preprocessed and passed to the trained model for product recommendation.

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ML ML AWS AI 89

Precisely

FEBRUARY 12, 2024

All that time spent on data preparation has an opportunity cost associated with it. Data Governance Drives Insights Data governance provides an important framework. Are you interested in learning more about telecom data governance benefits of a business-first data governance framework?

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Data Governance Analytics Analytics Machine Learning 64

Alation

FEBRUARY 13, 2020

Dataset Evaluation—Choosing the right datasets depends on ability to evaluate their suitability for an analysis use case without needing to download or acquire data first. It is common to shift from 80% of time spent finding data and only 20% on analysis to 20% finding and preparing data with 80% for analysis.

Data Lakes 64

Data Lakes Data Analysis Data Analysis Big Data 64

Pickl AI

DECEMBER 3, 2024

Users can download datasets in formats like CSV and ARFF. How to Access and Use Datasets from the UCI Repository The UCI Machine Learning Repository offers easy access to hundreds of datasets, making it an invaluable resource for data scientists, Machine Learning practitioners, and researchers. CSV, ARFF) to begin the download.

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Machine Learning Machine Learning Clustering Supervised Learning 52

Heartbeat

MAY 29, 2023

In this article, we will explore the essential steps involved in training LLMs, including data preparation, model selection, hyperparameter tuning, and fine-tuning. We will also discuss best practices for training LLMs, such as using transfer learning, data augmentation, and ensembling methods.

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Machine Learning Machine Learning Natural Language Processing Data Preparation 78

AWS Machine Learning Blog

AUGUST 15, 2024

In the following sections, we demonstrate how to import and prepare the data, optionally export the data, create a model, and run inference, all in SageMaker Canvas. Download the dataset from Kaggle and upload it to an Amazon Simple Storage Service (Amazon S3) bucket.

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ML ML Data Preparation AWS 119

AWS Machine Learning Blog

MARCH 29, 2023

These activities are recorded in a model recipe , which is a series of steps towards data preparation. This recipe is maintained throughout the lifecycle of a particular ML model from data preparation to generating predictions. These predictions can be previewed and downloaded for use with downstream applications.

Machine Learning 87

Machine Learning Machine Learning ML ML 87