Data Engineering Examples Machine Learning Metrics 
Altexsoft
JUNE 29, 2021
In a world fueled by disruptive technologies, no wonder businesses heavily rely on machine learning. For example, Netflix takes advantage of ML algorithms to personalize and recommend movies for clients, saving the tech giant billions. The role of a machine learning engineer in the data science team.
Mentormate
DECEMBER 12, 2023
In a previous blog post, we introduced a five-phase framework to plan out Artificial Intelligence (AI) and Machine Learning (ML) initiatives. The Traditional Machine Learning Workflow Initiating a traditional ML project begins with collecting data. Duplicated records are identified and rectified.
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Cloudera
FEBRUARY 16, 2022
While it is a little dated, one amusing example that has been the source of countless internet memes is the famous, “is this a chihuahua or a muffin?” In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Machine Learning Model Lineage.
Cloudera
APRIL 9, 2021
The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. Data Collection – streaming data.
CIO
APRIL 12, 2023
Real-time AI involves processing data for making decisions within a given time frame. Real-time AI brings together streaming data and machine learning algorithms to make fast and automated decisions; examples include recommendations, fraud detection, security monitoring, and chatbots. It isn’t easy.
O'Reilly Media - Ideas
NOVEMBER 13, 2018
As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. We recently conducted a survey which garnered more than 11,000 respondents—our main goal was to ascertain how enterprises were using machine learning. Model lifecycle management.
Altexsoft
MAY 27, 2020
Machine learning (ML) history can be traced back to the 1950s, when the first neural networks and ML algorithms appeared. Analysis of more than 16.000 papers on data science by MIT technologies shows the exponential growth of machine learning during the last 20 years pumped by big data and deep learning advancements.
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