AWS Machine Learning Blog

NOVEMBER 19, 2024

This session covers the technical process, from data preparation to model customization techniques, training strategies, deployment considerations, and post-customization evaluation. Explore how this powerful tool streamlines the entire ML lifecycle, from data preparation to model deployment.

AWS 102

AWS ML ML AI 102

DataRobot Blog

APRIL 1, 2018

This new platform will also serve many different use cases, including but not limited to analytics, application and data migrations, data monetization, and master data creation. . [1] 1] Gartner, Augmented Analytics Is the Future of Data and Analytics , Published: 27 July 2017, Analyst(s): Rita L. DataRobot Data Prep.

Analytics 40

Analytics Analytics Data Preparation Augmented Analytics 40

Alation

FEBRUARY 20, 2020

Additionally, given the massive volume of new data, more than any organization could reasonably accommodate, enterprises will still struggle with the proverbial ‘needle in the haystack’ problem of stewarding such a large amount of data. Get the latest data cataloging news and trends in your inbox. Subscribe to Alation's Blog.

Data Warehouse 52

Data Warehouse Hadoop Data Science ETL 52

IBM Journey to AI blog

AUGUST 11, 2023

Because the machine learning lifecycle has many complex components that reach across multiple teams, it requires close-knit collaboration to ensure that hand-offs occur efficiently, from data preparation and model training to model deployment and monitoring.

Data Science 68

Data Science Machine Learning Machine Learning ML 68

DagsHub

JULY 25, 2024

TensorFlow The Google Brain team created the open-source deep learning framework TensorFlow, which was made available in 2015. Developed by François Chollet, it was released in 2015 to simplify the creation of deep learning models. Further Reading and Documentation H2O.ai Documentation H2O.ai

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Heartbeat

AUGUST 21, 2023

ResNet is a deep CNN architecture developed by Kaiming He and his colleagues at Microsoft Research in 2015. Training a Convolutional Neural Networks Training a convolutional neural network (CNN) involves several steps: Data Preparation : This method entails gathering, cleaning, and preparing the data that will be utilized to train the CNN.

Natural Language Processing 52

Natural Language Processing Deep Learning Deep Learning ML 52

The MLOps Blog

NOVEMBER 30, 2022

Starting from AlexNet with 8 layers in 2012 to ResNet with 152 layers in 2015 – the deep neural networks have become deeper with time. It requires significant effort in terms of data preparation, exploration, processing, and experimentation, which involves trying out algorithms and hyperparameters.

ML 52

ML ML Machine Learning Machine Learning 52