Remove 2015 Remove Data Preparation Remove Data Science
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MLOps and the evolution of data science

IBM Journey to AI blog

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How MLOps will be used within the organization.

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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning Blog

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 110
professionals

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The 2016 Crystal Ball – What’s Next in Data?

Alation

With the year coming to a close, many look back at the headlines that made major waves in technology and big data – from Spark to Hadoop to trends in data science – the list could go on and on. So, we cheer to 2015 and welcome the new year with open arms, ready to embrace the data landscape ahead in 2016.

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A Guide to Convolutional Neural Networks

Heartbeat

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.

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Top 10 Deep Learning Platforms in 2024

DagsHub

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. Notable Use Cases in the Industry H2O.ai Guidance for Use H2O.ai Further Reading and Documentation H2O.ai

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Why is Git Not the Best for ML Model Version Control

The MLOps Blog

These days enterprises are sitting on a pool of data and increasingly employing machine learning and deep learning algorithms to forecast sales, predict customer churn and fraud detection, etc., Data science practitioners experiment with algorithms, data, and hyperparameters to develop a model that generates business insights.

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Operationalizing responsible AI principles for defense

IBM Journey to AI blog

Detailing ethics practices throughout the AI lifecycle, corresponding to business (or mission) goals, data preparation and modeling, evaluation and deployment. The method merges best practices in data science, project management, design frameworks and AI governance. The CRISP-DM model is useful here.

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