The trinity of errors in applying confidence intervals: An exploration using Statsmodels
Three reasons why confidence intervals should not be used in financial data analyses.
Data
Our take on the ideas, information, and tools that make data work.
One simple chart: Who is interested in Apache Pulsar?
Multi-layer architecture, scalability, multitenancy, and durability are just some of the reasons companies have been using Pulsar.
Stablecoins: Solving the cryptocurrency volatility crisis
Resolving the volatility problem will unlock the groundwork needed for blockchain-based global payment systems.
Why a data scientist is not a data engineer
Or, why science and engineering are still different disciplines.
Specialized tools for machine learning development and model governance are becoming essential
Why companies are turning to specialized machine learning tools like MLflow.
Sustaining machine learning in the enterprise
Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning.
Lessons learned building natural language processing systems in health care
NLP systems in health care are hard—they require broad general and medical knowledge, must handle a large variety of inputs, and need to understand context.
The trinity of errors in financial models: An introductory analysis using TensorFlow Probability
An exploration of three types of errors inherent in all financial models.
The data imperative
Ben Sharma shares how the best organizations immunize themselves against the plague of static data and rigid process
Wait … pizza is a vegetable? Decoding regulations using machine learning
Dinesh Nirmal explains how AI is helping supply school lunch and keep ahead of regulations.
Derive value from analytics and AI at scale
Ziya Ma discusses how recent innovations from Intel in high-capacity persistent memory and open source software are accelerating production-scale deployments.
Smarter cities through Geotab with BigQuery ML and geospatial analytics
Chad Jennings explains how Geotab's smart city application helps city planners understand traffic and predict locations of unsafe driving.
AI, ML, and the IoT will destroy the data center and the cloud (just not in the way you think)
DD Dasgupta explores the edge-cloud continuum, explaining how the roles of data centers and cloud infrastructure are redefined through the mainstream adoption of AI, ML, and IoT technologies.
The answer to life, the universe, and everything: But can you get that into production?
Ted Dunning discusses how new tools can change the way production systems work.
Leveraging the best of the past to power a better future
Drew Paroski and Aatif Din share how to develop modern database applications without sacrificing cost savings, data familiarity, and flexibility.
Learn about data governance with these books, videos, and tutorials
This collection of data governance resources will get you up to speed on the basics and best practices.
Data engineering: A quick and simple definition
Get a basic overview of data engineering and then go deeper with recommended resources.
Humans and the machine: Machine learning in context
Jean-François Puget explains why human context should be embraced as a guide to building better and smarter systems.
The case for a deliberate data strategy in today’s attention-deficit economy
Anoop Dawar shares principles successful companies are using to inspire an insight-driven ethos and build data-competent organizations.
What separates the clouds?
William Vambenepe walks through an interesting use case of machine learning in action and discusses the central role AI will play in big data analysis moving forward.
Operationalizing machine learning
Dinesh Nirmal explains how real-world machine learning reveals assumptions embedded in business processes that cause expensive misunderstandings.
To a hammer, everything is a nail: Choosing the right tool for your business problems
Tobias Ternstrom explains why you should objectively evaluate the problem you're trying to solve before choosing the tool to fix it.
Working with data in the financial industry: Legal considerations
Alysa Hutnik discusses the Fair Credit Reporting Act, the Equal Credit Opportunity Act, the Gramm-Leach Bliley Act, and the FTC’s focus on FinTech.
Modern data is continuous, diverse, and ever accelerating
How companies such as athenahealth can transform legacy data into insights.