Seattle Data Guy

AUGUST 9, 2023

Learning about how to data models from basic star schemas on the internet is like learning data science using the IRIS data set. It works great as a toy example. But it doesn’t match real life at all. Data modeling in real life requires you fully understand the data sources and your business use cases.… Read more The post Why Is Data Modeling So Challenging – How To Data Model For Analytics appeared first on Seattle Data Guy.

Data Science 246

Data Science Data Consulting IT 246

KDnuggets

AUGUST 9, 2023

This article explores StableCode, an innovative AI product by Stability AI, designed to enhance coding efficiency and accessibility. It delves into its unique features, underlying technology, and potential impact on the developer community.

Coding 90

Coding Designing Accessible Accessibility 90

Tweag

AUGUST 9, 2023

Why link statically against musl? Have you ever faced compatibility issues when dealing with Linux binary executables? The culprit is often the libc implementation, glibc. Acting as the backbone of nearly all Linux distros, glibc is the library responsible for providing standard C functions. Yet, its version compatibility often poses a challenge. Binaries compiled with a newer version of glibc may not function on systems running an older one, creating a compatibility headache.

Utilities 93

Utilities Building Coding Project 93

KDnuggets

AUGUST 9, 2023

Forget ChatGPT, This New AI Assistant Is Leagues Ahead and Will Change the Way You Work Forever • 7 Steps to Mastering Data Cleaning and Preprocessing Techniques • Fundamentals Of Statistics For Data Scientists and Analysts • I Created An AI App In 3 Days • Using SHAP Values for Model Interpretability in Machine Learning

Machine Learning 83

Machine Learning Data 83

Advertisement

Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

Data

Snowflake

AUGUST 9, 2023

Supply chain disruption continues to affect retailers, consumer packaged goods companies (CPGs), and customers. Constraints on the ability to produce goods have limited the availability of in-demand products, leading to inflation. Not only are manufacturers not making enough products in line with demand in industries such as automotive and electronics, at the same time, those products have become much more expensive.

Retail 52

Retail Electronics Raw Data Manufacturing 52

KDnuggets

AUGUST 9, 2023

ARIMA models are a popular tool for time series forecasting, and can be implemented in Python using the `statsmodels` library.

Python 80

Python Machine Learning 80

KDnuggets

AUGUST 9, 2023

OpenAI has recently introduced custom instructions to get the most out of ChatGPT.

88

88

Confluent

AUGUST 9, 2023

AI projects can only be successful with fresh and accurate data. Real-time data streaming powers recommendations at startup Whatnot.

Project 52

Project Data 52

Engineering at Meta

AUGUST 9, 2023

Explore is one of the largest recommendation systems on Instagram. We leverage machine learning to make sure people are always seeing content that is the most interesting and relevant to them. Using more advanced machine learning models, like Two Towers neural networks, we’ve been able to make the Explore recommendation system even more scalable and flexible.

Systems 85

Systems Media Algorithm Machine Learning 85

Monte Carlo

AUGUST 9, 2023

Your data will never be perfect. But it could be a whole heck of a lot better. From the hundreds of hours we’ve spent talking to customers, it’s clear that data quality issues are some of the most pernicious challenges facing modern data teams. In fact, according to Gartner , data quality issues cost organizations an average of $12.9 million per year.

Finance 52

Finance High Quality Data SQL Data 52

Advertisement

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

Database

Monte Carlo

AUGUST 9, 2023

Over the last 20-ish years, the DevOps methodology has become the default approach to developing, securing, scaling, and maintaining software engineering. And in the last 5-ish years, DataOps has entered the scene to help data engineers scale data management. Just like DevOps applies CI/CD (Continuous Integration and Continuous Deployment) practices to software development and operations, DataOps uses CI/CD principles and automation in the building, maintaining, and scaling of data products and

Data Engineering 52

Data Engineering Data Engineer Engineering Data Pipeline 52

Monte Carlo

AUGUST 9, 2023

Generative AI is not a new concept. It’s been studied for decades and applied in limited capacities. That is until ChatGPT shocked and awed our collective consciousness in late 2022. Still, generating a recipe for lasagna is an entirely different process than infusing generative AI capabilities across a business or integrating large language models (LLMs) into data engineering workflows.

IT 52

IT Unstructured Data SQL BI 52