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How to get datasets for Machine Learning?

Knowledge Hut

Datasets play a crucial role and are at the heart of all Machine Learning models. Machine Learning without data sets will not exist because ML depends on data sets to bring out relevant insights and solve real-world problems. Quality data is therefore important to ensure the efficacy of a machine learning model.

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Troubleshooting Kafka In Production

Data Engineering Podcast

Elad Eldor has experienced these challenges first-hand, leading to his work writing the book "Kafka: : Troubleshooting in Production" In this episode he highlights the sources of complexity that contribute to Kafka's operational difficulties, and some of the main ways to identify and mitigate potential sources of trouble.

Kafka 245
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Operationalizing Machine Learning from PoC to Production

KDnuggets

Most companies haven’t seen ROI from machine learning since the benefit is only realized when the models are in production. Here’s how to make sure your ML project works.

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How Data Engineering Teams Power Machine Learning With Feature Platforms

Data Engineering Podcast

Summary Feature engineering is a crucial aspect of the machine learning workflow. In this episode Razi Raziuddin shares how data engineering teams can support the machine learning workflow through the development and support of systems that empower data scientists and ML engineers to build and maintain their own features.

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Big Data vs Machine Learning: Top Differences & Similarities

Knowledge Hut

Big data vs machine learning is indispensable, and it is crucial to effectively discern their dissimilarities to harness their potential. Big Data vs Machine Learning Big data and machine learning serve distinct purposes in the realm of data analysis.

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A Full End-to-End Deployment of a Machine Learning Algorithm into a Live Production Environment

KDnuggets

How to use scikit-learn, pickle, Flask, Microsoft Azure and ipywidgets to fully deploy a Python machine learning algorithm into a live, production environment.

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Surveying The Market Of Database Products

Data Engineering Podcast

In recent years the selection of database products has exploded, making the critical decision of which engine(s) to use even more difficult. In this episode Tanya Bragin shares her experiences as a product manager for two major vendors and the lessons that she has learned about how teams should approach the process of tool selection.

Database 189