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External Data Supports More Accurate Planning

David Menninger's Analyst Perspectives

External data is necessary for many functions, including useful and accurate competitive intelligence used by sales and marketing groups. It is also essential for the effective application of AI using ML for business-focused planning and budgeting and predictive analytics.

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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

The hype around large language models (LLMs) is undeniable. Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. Even basic predictive modeling can be done with lightweight machine learning in Python or R. This article reflects some of what Ive learned.

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6 Ways to Improve Your Predictive Models in Data Science

KDnuggets

Whether you aim for building the perfect image classifier, sales predictor, or price estimator, these six pracitcal tips and insights will help you get there!

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Automating the Automators: Shift Change in the Robot Factory

O'Reilly on Data

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

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Proptech and the proper use of technology for house sales prediction

KDnuggets

Using the ATTOM dataset, we extracted data on sales transactions in the USA, loans, and estimated values of property. We developed an optimal prediction model from correlations in the time and status of ownership as well as the time of the year of sales fluctuations.

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Data Insights for Everyone — The Semantic Layer to the Rescue

Rocket-Powered Data Science

For example, if I am searching for customer sales numbers, different datasets may label that “ sales ”, or “ revenue ”, or “ customer_sales ”, or “ Cust_sales ”, or any number of other such unique identifiers. The semantic layer bridges the gaps between the data cloud, the decision-makers, and the data science modelers.

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Nvidia speeds AI, climate modeling

CIO Business Intelligence

Nvidia is hoping to make it easier for CIOs building digital twins and machine learning models to secure enterprise computing, and even to speed the adoption of quantum computing with a range of new hardware and software. Nvidia claims it can do so up to 45,000 times faster than traditional numerical prediction models.

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