This story was originally published on HackerNoon at: https://hackernoon.com/seamlessly-migrate-your-on-premise-data-pipeline-to-azure-with-these-key-steps.
Scaling AI/ML Data Needs: Migrating On-Premise Data Engineering Workloads to Azure Cloud
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This story was written by: @amlanpatnaik. Learn more about this writer by checking @amlanpatnaik's about page, and for more stories, please visit hackernoon.com.

This guide details the process of migrating an on-premise Cloudera data system to Azure, covering key considerations, challenges, and best practices to ensure a smooth and secure transition.

This story was originally published on HackerNoon at: https://hackernoon.com/data-collection-for-product-managers.
Discover how product managers can bridge the gap between intuition and data to optimize product improvement with best practices and real-world examples.
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This story was written by: @carolinagarcia. Learn more about this writer by checking @carolinagarcia's about page, and for more stories, please visit hackernoon.com.

Discover how product managers can bridge the gap between intuition and data to optimize product improvement. This guide explores the importance of data-driven decision-making, offering best practices and real-world examples from companies like NuBank, Monzo, Deliveroo, and Booking.com. Learn how to acquire insights from customer feedback, track performance metrics, monitor market trends, and refine product roadmaps through iterative experimentation. Become a data-driven PM and create products that users will love.

This story was originally published on HackerNoon at: https://hackernoon.com/data-collection-for-product-managers.
Discover how product managers can bridge the gap between intuition and data to optimize product improvement with best practices and real-world examples.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-collection, #startups, #data-product-management, #data-driven-insights, #data-driven-decision-making, #product-manager, #product-management-tips, #how-to-collect-data, and more.

This story was written by: @carolinagarcia. Learn more about this writer by checking @carolinagarcia's about page, and for more stories, please visit hackernoon.com.

Discover how product managers can bridge the gap between intuition and data to optimize product improvement. This guide explores the importance of data-driven decision-making, offering best practices and real-world examples from companies like NuBank, Monzo, Deliveroo, and Booking.com. Learn how to acquire insights from customer feedback, track performance metrics, monitor market trends, and refine product roadmaps through iterative experimentation. Become a data-driven PM and create products that users will love.

This story was originally published on HackerNoon at: https://hackernoon.com/leveraging-data-granularity-distribution-and-modeling-for-effective-product-management.
These three fundamental concepts are exceptionally needed for being able to use data to enhance product strategy.
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This story was written by: @gevorgkazaryan. Learn more about this writer by checking @gevorgkazaryan's about page, and for more stories, please visit hackernoon.com.

Granularity determines the level of detail available in the data, which directly impacts what you can observe and analyze. For instance, finer granularity provides more detailed insights but may require more sophisticated handling and processing techniques. Distribution helps identify the patterns and spread of data, which is critical for selecting the appropriate analysis techniques and ensuring the accuracy of predictive models. Data Modeling uses the insights gained from understanding granularity and distribution to build predictive or descriptive models that inform decision-making and strategy.

This story was originally published on HackerNoon at: https://hackernoon.com/how-vectors-rag-and-llama-3-are-changing-first-party-data.
In the battle for the best data, is first-party better? Not by itself, but it could be with vectors, frameworks like RAG, and open-source models
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This story was written by: @danielsvonava. Learn more about this writer by checking @danielsvonava's about page, and for more stories, please visit hackernoon.com.

The push for first-party data generally goes that companies need to become better stewards of data acquisition and management. Consumers increasingly want to know who is hanging onto their personal information, how they got it, why they have it, and what is being done with it. The push to take back control of data seems essential, but is it practical?

This story was originally published on HackerNoon at: https://hackernoon.com/16-best-sklearn-datasets-for-building-machine-learning-models.
Sklearn datasets are included as part of the scikit-learn (sklearn) library, so they come pre-installed with the library.
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This story was written by: @datasets. Learn more about this writer by checking @datasets's about page, and for more stories, please visit hackernoon.com.

Sklearn is a Python module for machine learning built on top of SciPy. It is unique due to its wide range of algorithms and ease of use. Data powers machine learning algorithms and scikit-learn. Sklearn offers high quality datasets that are widely used by researchers, practitioners and enthusiasts.

This story was originally published on HackerNoon at: https://hackernoon.com/enhancing-audit-processes-with-advanced-analytical-tools.
Discover how advanced analytical tools streamline audit processes, boosting accuracy and efficiency for tech professionals.
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This story was written by: @devinpartida. Learn more about this writer by checking @devinpartida's about page, and for more stories, please visit hackernoon.com.

Developers can leverage advanced analytics tools to streamline and improve software, compliance and internal controls auditing. Advanced analytics tools like artificial intelligence, complex event processing and data mining enable 100% population testing. They eliminate the need for sampling, thereby reducing bias and error risks. Autonomous technologies like AI are particularly beneficial since they eliminate human error.

This story was originally published on HackerNoon at: https://hackernoon.com/go-clean-to-be-lean-data-optimization-for-improved-business-efficiency.
The article discusses cost optimization with clean data, explaining how businesses can save resources by reducing the workload for data analysts and more.
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This story was written by: @karolisdidziulis. Learn more about this writer by checking @karolisdidziulis's about page, and for more stories, please visit hackernoon.com.

This article discusses cost optimization with clean data. It explains how businesses can save resources by decreasing the load for data analysts, among other opportunities. It also discusses the differences between raw and clean data and who can benefit from switching to the latter. You'll also find 4 ways in which clean data reduces time to value.