Data Modeling, Data Preparation and Events

Predictive Analytics: 4 Primary Aspects of Predictive Analytics

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. From a predictive analytics standpoint, you can be surer of its utility. Deep Learning, Machine Learning, and Automation.

Predictive Analytics

Predictive Analytics Analytics Analytics Decision Trees

How Light & Wonder built a predictive maintenance solution for gaming machines on AWS

AWS Machine Learning Blog

JUNE 22, 2023

Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to stream telemetry and machine health data from roughly half a million electronic gaming machines distributed across its casino customer base globally when LnW Connect reaches its full potential.

AWS

AWS ML ML Machine Learning

How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

SEPTEMBER 23, 2021

This enables employees to see data details like definitions and formulas, lineage and ownership information, as well as important data quality notifications, from certification status to events, like if a data source refresh failed and the information isn’t up to date. Data modeling. Data migration .

Data Governance

Data Governance Analytics Analytics Tableau

Webinars

How to Achieve High-Accuracy Results When Using LLMs

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How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

SEPTEMBER 23, 2021

This enables employees to see data details like definitions and formulas, lineage and ownership information, as well as important data quality notifications, from certification status to events, like if a data source refresh failed and the information isn’t up to date. Data modeling. Data migration .

Data Governance

Data Governance Analytics Analytics Tableau

Unlocking Tabular Data’s Hidden Potential

ODSC - Open Data Science

MAY 10, 2023

Although tabular data are less commonly required to be labeled, his other points apply, as tabular data, more often than not, contains errors, is messy, and is restricted by volume. One might say that tabular data modeling is the original data-centric AI!

Data Scientist

Data Scientist Data Science Deep Learning Deep Learning

Introducing our New Book: Implementing MLOps in the Enterprise

Iguazio

DECEMBER 14, 2023

There are 6 high-level steps in every MLOps project The 6 steps are: Initial data gathering (for exploration). Exploratory data analysis (EDA) and modeling. Data and model pipeline development (data preparation, training, evaluation, and so on). Deploy according to various strategies.

ML

ML ML Data Science Data Preparation

Discover the Most Important Fundamentals of Data Engineering

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering

Data Engineering Data Engineering Data Engineering Data Engineer

LLMOps vs. MLOps: Understanding the Differences

Iguazio

FEBRUARY 8, 2024

Data Pipeline - Manages and processes various data sources. Application Pipeline - Manages requests and data/model validations. Multi-Stage Pipeline - Ensures correct model behavior and incorporates feedback loops. ML Pipeline - Focuses on training, validation and deployment.

ML

ML ML Data Scientist AI

AI Models as a Service (AIMaaS): A Detailed Overview

Pickl AI

OCTOBER 3, 2024

Predictive Analytics : Models that forecast future events based on historical data. Model Repository and Access Users can browse a comprehensive library of pre-trained models tailored to specific business needs, making it easy to find the right solution for various applications.

Machine Learning

Machine Learning Machine Learning AI AI

A Step-by-Step Guide: Efficiently Managing TensorFlow/Keras Model Development with Comet

Heartbeat

NOVEMBER 28, 2023

MLOps is a set of principles and practices that combine software engineering, data science, and DevOps to ensure that ML models are deployed and managed effectively in production. MLOps encompasses the entire ML lifecycle, from data preparation to model deployment and monitoring. Why Is MLOps Important?

ML

ML ML Machine Learning Machine Learning

Why is Git Not the Best for ML Model Version Control

The MLOps Blog

NOVEMBER 30, 2022

It requires significant effort in terms of data preparation, exploration, processing, and experimentation, which involves trying out algorithms and hyperparameters. It is so because these algorithms have proven great results on a benchmark dataset, whereas your business problem and hence your data is different.

ML

ML ML Machine Learning Machine Learning

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

JUNE 27, 2023

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring. Is it fast and reliable enough for your workflow?

Machine Learning

Machine Learning Machine Learning ML ML

Five winning Tableau tips from the Gartner BI Bake-Off

Tableau

MAY 6, 2021

Use Tableau Prep to quickly combine and clean data . Data preparation doesn’t have to be painful or time-consuming. Tableau Prep offers automatic data prep recommendations that allow you to combine, shape, and clean your data faster and easier. . Visually analyze data to explore, test, and discover the unexpected

Tableau

Tableau Natural Language Processing Machine Learning Machine Learning

Five winning Tableau tips from the Gartner BI Bake-Off

Tableau

MAY 6, 2021

Use Tableau Prep to quickly combine and clean data . Data preparation doesn’t have to be painful or time-consuming. Tableau Prep offers automatic data prep recommendations that allow you to combine, shape, and clean your data faster and easier. . Visually analyze data to explore, test, and discover the unexpected

Tableau

Tableau Natural Language Processing Machine Learning Machine Learning

Implementing Knowledge Bases for Amazon Bedrock in support of GDPR (right to be forgotten) requests

AWS Machine Learning Blog

MAY 31, 2024

Data preparation Before creating a knowledge base using Knowledge Bases for Amazon Bedrock, it’s essential to prepare the data to augment the FM in a RAG implementation. You can help safeguard sensitive data that’s ingested by CloudWatch Logs by using log group data protection policies.

AWS

AWS Machine Learning Machine Learning Database

On the implementation of digital tools

Dataconomy

OCTOBER 15, 2024

I’ve found that while calculating automation benefits like time savings is relatively straightforward, users struggle to estimate the value of insights, especially when dealing with previously unavailable data. We were developing a data model to provide deeper insights into logistics contracts.

Data Modeling

Data Modeling Data Models Analytics Analytics

Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

AWS Machine Learning Blog

NOVEMBER 13, 2024

ML development – This phase of the ML lifecycle should be hosted in an isolated environment for model experimentation and building the candidate model. Several activities are performed in this phase, such as creating the model, data preparation, model training, evaluation, and model registration.

ML

ML ML AWS Data Preparation

Data Science Current

Predictive Analytics: 4 Primary Aspects of Predictive Analytics

How Light & Wonder built a predictive maintenance solution for gaming machines on AWS

Webinars

Trending Sources

How to: Focus on three areas for a holistic data governance approach for self-service analytics

Webinars

How to: Focus on three areas for a holistic data governance approach for self-service analytics

Unlocking Tabular Data’s Hidden Potential

Introducing our New Book: Implementing MLOps in the Enterprise

Discover the Most Important Fundamentals of Data Engineering

LLMOps vs. MLOps: Understanding the Differences

AI Models as a Service (AIMaaS): A Detailed Overview

A Step-by-Step Guide: Efficiently Managing TensorFlow/Keras Model Development with Comet

Why is Git Not the Best for ML Model Version Control

MLOps Landscape in 2023: Top Tools and Platforms

Five winning Tableau tips from the Gartner BI Bake-Off

Five winning Tableau tips from the Gartner BI Bake-Off

Implementing Knowledge Bases for Amazon Bedrock in support of GDPR (right to be forgotten) requests

On the implementation of digital tools

Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

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