IBM Journey to AI blog

MARCH 14, 2024

Try Db2 Warehouse SaaS on AWS for free   Netezza SaaS on AWS IBM® Netezza® Performance Server is a cloud-native data warehouse designed to operationalize deep analytics, data mining and BI by unifying, accessing and scaling all types of data across the hybrid cloud. Netezza

AWS 93

AWS Database ETL AI 93

Pickl AI

OCTOBER 3, 2024

Predictive analytics refers to the use of statistical algorithms and Machine Learning techniques to analyse historical data and predict future events or outcomes. It involves various processes, including data mining, predictive modelling, and statistical analysis.

Predictive Analytics 52

Predictive Analytics Analytics Analytics Machine Learning 52

Pickl AI

JULY 12, 2024

Pandas: A powerful library for data manipulation and analysis, offering data structures and operations for manipulating numerical tables and time series data. Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

phData

FEBRUARY 7, 2024

Snowpark Use Cases Data Science Streamlining data preparation and pre-processing: Snowpark’s Python, Java, and Scala libraries allow data scientists to use familiar tools for wrangling and cleaning data directly within Snowflake, eliminating the need for separate ETL pipelines and reducing context switching.

Python 52

Python ML ML SQL 52

IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

Data Science 68

Data Science Machine Learning Machine Learning ML 68

DagsHub

APRIL 21, 2024

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Data Science Dojo

AUGUST 30, 2023

Key Concepts of Applied Data Science Read more –> 33 ways to stunning data visualization Methodologies of applied data science 1. CRISP-DM methodology Cross-Industry Standard Process for Data Mining (CRISP-DM) is a commonly used methodology in Applied Data Science.

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Data Science Hypothesis Testing Machine Learning Machine Learning 195

Dataconomy

FEBRUARY 26, 2025

Data mining has emerged as a vital tool in todays data-driven environment, enabling organizations to extract valuable insights from vast amounts of information. As businesses generate and collect more data than ever before, understanding how to uncover patterns and trends becomes essential for making informed decisions.

Data Mining 91

Data Mining Data Mining Data Mining Data Preparation 91

Dataconomy

MARCH 5, 2025

Citizen Data Scientist: Uses existing analytics tools but may lack formal training and earn a salary more aligned with general activities. Major areas of data science Data science incorporates several critical components: Data preparation: Ensuring data is cleansed and organized before analysis.

Data Scientist 91

Data Scientist Citizen Data Scientist Exploratory Data Analysis Machine Learning 91

AWS Machine Learning Blog

MARCH 18, 2025

At its core, NeMo Framework provides model builders with: Comprehensive development tools : A complete ecosystem of tools, scripts, and proven recipes that guide users through every phase of the LLM lifecycle, from initial data preparation to final deployment.

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Clustering AWS Deep Learning Deep Learning 83