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Pacific Data Science Launches “The Brain” – Automating Back-Office Workflows for Real Estate and Investment Management Companies

Pacific Data Science has launched its newest intelligent solution for commercial real estate and investment companies, The Brain. The Brain was originally developed in collaboration with Paladin Realty Partners, a leading private equity fund manager focused on institutional-quality real estate investments in Latin America. Built with the same reliability and security that Pacific Data Science is known for, The Brain was designed to manage the nuanced lifecycle and automate the complex reporting workflows around real estate investment and development projects.

3 Non-Obvious Keys to Being AI-Ready

Data scientists know what they are doing, and most organizations have no cause to worry about the soundness of their machine learning (ML) algorithms. Where AI readiness typically lags is in other parts of the process. In most organizations today, the process of building, deploying and maintaining AI systems bears no resemblance to traditional IT. Alegion explores three key strategies your business can employ to be AI-ready.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – March 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

The insideBIGDATA IMPACT 50 List for Q2 2019

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

Domino Data Lab Platform Enhancements Improve Productivity of Data Science Teams Across the Entire Model Lifecycle

Domino Data Lab , provider of a leading open data science platform, announced new capabilities to further empower model-driven organizations to institute data science as an enterprise-wide discipline.
Updated with three new breakthrough capabilities — Data sets, Experiment Manager, and Activity Feed — Domino helps data science teams accelerate development and delivery of high-impact models through increased collaboration, reproducibility, and reusability across their organizations.

A ‘Pre-Flight Checklist’ for Machine Learning Training Data

Machine learning is often key to success for today’s institutions that rely heavily on data for success. But often, data science teams can have a difficult time convincing their organizations of the breadth and size of a training data challenge. A new report from Alegion walks through a checklist to review before helping your enterprise take the next step in machine learning.

High School Students Beat Trained Data Scientists at UC Berkeley, Solve Real Healthcare Problems with Aible AI in Minutes

Aible, the innovators of AI for business impact, announced the UC Berkeley Real World AI Challenge winners — the top scorers include two high school students, a history major and no data scientists. Nearly 30 high school and college students competed to create a custom Artificial Intelligence (AI) based on a real-world healthcare data set […]

Labeled Training Sets for Machine Learning

It’s no secret that machine learning success is derived from the availability of labeled data in the form of a training set and test set that are used by the learning algorithm. Labels are the values of the response variables (what’s being predicted) that are used by the algorithm along with the feature variables (predictors). One consistent problem faced by data scientists is how to obtain labels for a given data set for use with machine learning. In this article we’ll see a variety of techniques used down in the trenches.

TOP 10 insideBIGDATA Articles for February 2019

In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we’ve heard from many of our followers that this feature will enable them to catch up with important news and features flowing across our many channels.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – January 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.