insideBIGDATA

MARCH 7, 2024

today announced that NVIDIA CUDA-X™ data processing libraries will be integrated with HP AI workstation solutions to turbocharge the data preparation and processing work that forms the foundation of generative AI development. HP Amplify — NVIDIA and HP Inc.

Data Science 221

Data Science Data Preparation AI AI 221

Dataconomy

MARCH 4, 2025

Methods of generating synthetic data There are various methods for generating synthetic data, each suitable for different use cases and contexts. Organizations can take advantage of numerous open-source tools available for data synthesis.

Decision Trees 91

Decision Trees Machine Learning Machine Learning Deep Learning 91

Dataconomy

MARCH 4, 2025

By utilizing algorithms and statistical models, data mining transforms raw data into actionable insights. The data mining process The data mining process is structured into four primary stages: data gathering, data preparation, data mining, and data analysis and interpretation.

Data Mining 91

Data Mining Data Mining Data Mining Decision Trees 91

DagsHub

JULY 25, 2024

Source: Author Introduction Deep learning, a branch of machine learning inspired by biological neural networks, has become a key technique in artificial intelligence (AI) applications. Deep learning methods use multi-layer artificial neural networks to extract intricate patterns from large data sets.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Pickl AI

AUGUST 3, 2023

Introduction to Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning techniques that are designed to automatically learn and represent data in multiple layers of abstraction. This process is known as training, and it relies on large amounts of labeled data.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Data Science Dojo

AUGUST 30, 2023

CRISP-DM methodology Cross-Industry Standard Process for Data Mining (CRISP-DM) is a commonly used methodology in Applied Data Science. It consists of six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Data Science 195

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

The process begins with data preparation, followed by model training and tuning, and then model deployment and management. Data preparation is essential for model training and is also the first phase in the MLOps lifecycle.

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AWS Data Preparation ML ML 125

IBM Data Science in Practice

MARCH 21, 2023

Deep learning models built using Maximo Visual Inspection (MVI) are used for a wide range of applications, including image classification and object detection. These models train on large datasets and learn complex patterns that are difficult for humans to recognize. It is more specific as they train artificial neural networks.

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DagsHub

FEBRUARY 29, 2024

Data, is therefore, essential to the quality and performance of machine learning models. This makes data preparation for machine learning all the more critical, so that the models generate reliable and accurate predictions and drive business value for the organization. million per year.

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Governance 52

Pickl AI

SEPTEMBER 15, 2024

They are effective in face recognition, image similarity, and one-shot learning but face challenges like high computational costs and data imbalance. Introduction Neural networks form the backbone of Deep Learning , allowing machines to learn from data by mimicking the human brain’s structure.

Deep Learning 52

Deep Learning Deep Learning Data Preparation Machine Learning 52

Data Science Dojo

APRIL 3, 2023

Data Robot also provides visualizations and diagnostic tools to help users understand their models’ performance. It offers a wide range of pre-built models, including deep learning and gradient boosting, that can be easily selected and configured using the drag-and-drop interface. H2O.ai H2O.ai

ML 195

ML ML Machine Learning Machine Learning 195

Data Science Dojo

AUGUST 28, 2023

The scope of LLMOps within machine learning projects can vary widely, tailored to the specific needs of each project. Some projects may necessitate a comprehensive LLMOps approach, spanning tasks from data preparation to pipeline production. This includes tokenizing the data, removing stop words, and normalizing the text.

Exploratory Data Analysis 195

Exploratory Data Analysis Data Preparation Machine Learning Machine Learning 195

AWS Machine Learning Blog

DECEMBER 24, 2024

Trainium chips are purpose-built for deep learning training of 100 billion and larger parameter models. Model training on Trainium is supported by the AWS Neuron SDK, which provides compiler, runtime, and profiling tools that unlock high-performance and cost-effective deep learning acceleration.

AWS 111

AWS Clustering Deep Learning Deep Learning 111

IBM Data Science in Practice

APRIL 9, 2024

Instead, we use pre-trained deep learning models like VGG or ResNet to extract feature vectors from the images. Image retrieval search architecture The architecture follows a typical machine learning workflow for image retrieval. Data Preparation Here we use a subset of the ImageNet dataset (100 classes).

Deep Learning 130

Deep Learning Deep Learning Database Data Preparation 130

AWS Machine Learning Blog

OCTOBER 23, 2023

Given this mission, Talent.com and AWS joined forces to create a job recommendation engine using state-of-the-art natural language processing (NLP) and deep learning model training techniques with Amazon SageMaker to provide an unrivaled experience for job seekers. It’s designed to significantly speed up deep learning model training.

AWS 128

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Smart Data Collective

SEPTEMBER 16, 2020

Regardless of your industry, whether it’s an enterprise insurance company, pharmaceuticals organization, or financial services provider, it could benefit you to gather your own data to predict future events. Deep Learning, Machine Learning, and Automation.

Predictive Analytics 139

Predictive Analytics Analytics Analytics Decision Trees 139

Data Science Dojo

AUGUST 16, 2024

Generative AI for Data Analytics – Understanding the Impact To understand the impact of generative AI for data analytics, it’s crucial to dive into the underlying mechanisms, that go beyond basic automation and touch on complex statistical modeling, deep learning, and interaction paradigms.

Analytics 195

Analytics Analytics Power BI AI 195

Towards AI

APRIL 17, 2025

In this piece, we explore practical ways to define data standards, ethically scrape and clean your datasets, and cut out the noise whether youre pretraining from scratch or fine-tuning a base model. If youre working on LLMs, this is one of those foundations thats easy to overlook but hard to ignore. 👉 Read the post here!

AI 69

AI AI Data Preparation Deep Learning 69

PyImageSearch

OCTOBER 21, 2024

We will start by setting up libraries and data preparation. Setup and Data Preparation For this purpose, we will use the Pump Sensor Dataset , which contains readings of 52 sensors that capture various parameters (e.g., detection of potential failures or issues). temperature, pressure, vibration, etc.) Download the code!

Algorithm 113

Algorithm Deep Learning Deep Learning Data Preparation 113

ODSC - Open Data Science

MARCH 13, 2023

Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.

Machine Learning 94

Machine Learning Machine Learning Data Wrangling Data Science 94

AWS Machine Learning Blog

SEPTEMBER 12, 2023

In the following sections, we break down the data preparation, model experimentation, and model deployment steps in more detail. Data preparation Scalable Capital uses a CRM tool for managing and storing email data. Relevant email contents consist of subject, body, and the custodian banks. Use Version 2.x

Data Science 134

Data Science Data Scientist AWS ML 134

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.

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AWS ML ML AI 116

AWS Machine Learning Blog

OCTOBER 19, 2023

Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII).

Machine Learning 126

Machine Learning Machine Learning ML ML 126

Heartbeat

FEBRUARY 1, 2023

First, we have data scientists who are in charge of creating and training machine learning models. They might also help with data preparation and cleaning. The machine learning engineers are in charge of taking the models developed by data scientists and deploying them into production.

Machine Learning 96

Machine Learning Machine Learning Data Scientist ML 96

Dataconomy

MARCH 27, 2023

RPA uses a graphical user interface (GUI) to interact with applications and websites, while ML uses algorithms and statistical models to analyze data. On the other hand, ML requires a significant amount of data preparation and model training before it can be deployed.

ML 133

ML ML Machine Learning Machine Learning 133

ODSC - Open Data Science

MAY 1, 2023

Managing Data Possibly the biggest reason for MLOps in the era of LLMs boils down to managing data. Given they’re built on deep learning models, LLMs require extraordinary amounts of data. Regardless of where this data came from, managing it can be difficult.

Data Scientist 97

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phData

JUNE 26, 2023

While both these tools are powerful on their own, their combined strength offers a comprehensive solution for data analytics. In this blog post, we will show you how to leverage KNIME’s Tableau Integration Extension and discuss the benefits of using KNIME for data preparation before visualization in Tableau.

Tableau 52

Tableau Data Preparation Machine Learning Machine Learning 52

AWS Machine Learning Blog

SEPTEMBER 20, 2023

SageMaker Studio allows data scientists, ML engineers, and data engineers to prepare data, build, train, and deploy ML models on one web interface. The Docker images are preinstalled and tested with the latest versions of popular deep learning frameworks as well as other dependencies needed for training and inference.

ML 135

ML ML Azure AWS 135

Mlearning.ai

MARCH 31, 2023

In this story, we talk about how to build a Deep Learning Object Detector from scratch using TensorFlow. Most of machine learning projects fit the picture above Once you define these things, the training is a cat-and-mouse game where you need “only” tuning the training hyperparameters in order to achieve the desired performance.

Deep Learning 98

Deep Learning Deep Learning Machine Learning Machine Learning 98

ODSC - Open Data Science

NOVEMBER 19, 2024

Here’s a breakdown of ten top sessions from this year’s conference that data professionals should consider. Topological Deep Learning Made Easy with TopoX with Dr. Mustafa Hajij Slides In these AI slides, Dr. Mustafa Hajij introduced TopoX, a comprehensive Python suite for topological deep learning.

Deep Learning 52

Deep Learning Deep Learning Data Science AI 52

Flipboard

NOVEMBER 24, 2023

Data scientists and ML engineers require capable tooling and sufficient compute for their work. Therefore, BMW established a centralized ML/deep learning infrastructure on premises several years ago and continuously upgraded it.

ML 153

ML ML AWS AI 153

Smart Data Collective

NOVEMBER 9, 2022

Everyday AI is a core concept of Dataiku, where the systematic use of data for everyday operations makes businesses competent to succeed in competitive markets. Dataiku helps its customers at every stage, from data preparation to analytics applications, to implement a data-driven model and make better decisions.

Machine Learning 122

Machine Learning Machine Learning Artificial Intelligence Artificial Intelligence 122

AWS Machine Learning Blog

NOVEMBER 15, 2024

SageMaker Studio is an IDE that offers a web-based visual interface for performing the ML development steps, from data preparation to model building, training, and deployment. He focuses on developing scalable machine learning algorithms. In this section, we cover how to discover these models in SageMaker Studio.

ML 114

ML ML Python AWS 114

AWS Machine Learning Blog

JULY 5, 2023

What if we could apply deep learning techniques to common areas that drive vehicle failures, unplanned downtime, and repair costs? Solution overview The AWS predictive maintenance solution for automotive fleets applies deep learning techniques to common areas that drive vehicle failures, unplanned downtime, and repair costs.

AWS 98

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AWS Machine Learning Blog

FEBRUARY 12, 2024

SageMaker pipeline steps The pipeline is divided into the following steps: Train and test data preparation – Terabytes of raw data are copied to an S3 bucket, processed using AWS Glue jobs for Spark processing, resulting in data structured and formatted for compatibility.

ML 138

ML ML AWS Machine Learning 138

AWS Machine Learning Blog

JANUARY 6, 2023

A DataBrew job extracts the data from the TR data warehouse for the users who are eligible to provide recommendations during renewal based on the current subscription plan and recent activity. Hesham Fahim is a Lead Machine Learning Engineer and Personalization Engine Architect at Thomson Reuters.

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AWS Data Warehouse ML ML 102

Women in Big Data

APRIL 30, 2024

The Women in Big Data (WiBD) and DataCamp Donates monthly Zoom Info-Session took place last Friday. She explained the highlights of a Deep Learning project on which she has been working recently. It was a very rich and informative talk given by Dr. Sridevi Pudipeddi. It was really neat.

Big Data 52

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