Dataconomy

MARCH 17, 2025

Augmented analytics is revolutionizing how organizations interact with their data. By harnessing the power of machine learning (ML) and natural language processing (NLP), businesses can streamline their data analysis processes and make more informed decisions.

Augmented Analytics 91

Augmented Analytics Analytics Analytics Natural Language Processing 91

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. Why do you need Data Preparation for Machine Learning?

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Governance 52

AWS Machine Learning Blog

DECEMBER 18, 2024

This strategic decision was driven by several factors: Efficient data preparation Building a high-quality pre-training dataset is a complex task, involving assembling and preprocessing text data from various sources, including web sources and partner companies. The team opted for fine-tuning on AWS.

Clustering 103

Clustering AWS AI AI 103

IBM Journey to AI blog

AUGUST 12, 2024

It helps companies streamline and automate the end-to-end ML lifecycle, which includes data collection, model creation (built on data sources from the software development lifecycle), model deployment, model orchestration, health monitoring and data governance processes.

Big Data 106

Big Data Big Data ML ML 106

Heartbeat

MAY 29, 2023

LLMs are one of the most exciting advancements in natural language processing (NLP). We will explore how to better understand the data that these models are trained on, and how to evaluate and optimize them for real-world use. LLMs rely on vast amounts of text data to learn patterns and generate coherent text.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

Pickl AI

OCTOBER 3, 2024

The process typically involves several key steps: Model Selection: Users choose from a library of pre-trained models tailored for specific applications such as Natural Language Processing (NLP), image recognition, or predictive analytics. Predictive Analytics : Models that forecast future events based on historical data.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Pickl AI

JUNE 18, 2024

It now allows users to clean, transform, and integrate data from various sources, streamlining the Data Analysis process. This eliminates the need to rely on separate tools for data preparation, saving time and resources. Data governance and compliance are critical aspects of Data Analysis.

Power BI 52

Power BI Data Analysis Data Analysis Data Visualization 52