Video Highlights: Improving ML Systems Beyond First A/B Test
Presented by Vijay Pappu, Senior ML Engineering Manager, Personalization Lead at Peloton, this talk focuses on any ML systems that rely on a feedback loop for improvement. How do we measure the efficacy of an ML system?
Featured Stories
Your Business’s Data Strategy is Hosed, You Just May Not Know It Yet
In this special guest feature, Nick Bonfiglio, CEO of Syncari, discusses the key takeaway of a recent cross-functional executive panel: data interoperability is the key to effective operational data. Cloud data warehouses are here to stay, so rather than dedicating them to reporting business intelligence insights, businesses should think about their warehouse as a part of an overall data solution and unified data model.
Best of arXiv.org for AI, Machine Learning, and Deep Learning – September 2021
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.
Webinar: How to Build Customer-oriented Applications Using Third-party Data
[SPONSORED POST] Join this webinar to learn how using third-party data enhances applications to better prioritize your target customer. Thought leaders from Foursquare and NextDoor will present real-world examples on leveraging datasets to create a customer-centric strategy and improve business outcomes.
Featured Resource
Enterprise Search in 2025
This white paper by enterprise search specialists Lucidworks, discusses how data is eating the world and search is the key to finding the data you need. The enterprise search industry is consolidating and moving to technologies built around Lucene and Solr. In the next few years we’ll see nearly all search become voice, conversational, and predictive. Search will surround everything we do and the right combination of signal capture, machine learning, and rules are essential to making that work. Fortunately, much of the technology to drive this is available to us today!
Industry Perspectives
Embracing AI in the World of Auto Insurance
In this special guest feature, John Goodson, Chief Technology Officer for CCC Intelligent Solutions, discusses how in the world of auto insurance, AI provides unprecedented clarity to reduce risk and elevate the customer experience by enabling faster decision-making than ever before – helping to keep people’s lives moving forward when it matters most.
Low Code Data Science is Not the Same as Automated Machine Learning
[SPONSORED POST] In this special guest feature, Rosaria Silipo, Ph.D., Principal Data Scientist at KNIME, discusses the difference between automated Machine Learning and low code tools for data science. Low code tools are becoming more and more popular. They allow to perform certain tasks without the need of coding. They are based on visual programming, that is on a drag & drop interface to build a pipeline of blocks, each block implementing a dedicated task.
Featured from insideHPC
- Cambridge Quantum Releases Quantum NLP Toolkit and LibraryCAMBRIDGE, UK — Cambridge Quantum today announced the release of what it said is the world’s first toolkit and library for Quantum Natural Language Processing (QNLP). The toolkit is called lambeq, named after the late mathematician and linguist Joachim Lambek. CQ said lambeq converts sentences into a quantum circuit and is designed to accelerate the development […]
News from insideHPC
- Intel Confirms Damkroger Out as Head of HPC; McVeigh to Lead Newly Formed Super Computer Group
- Appentra and Fujitsu in HPC Collaboration
- 2 Univ. of Minnesota Professors Win SC21 Test of Time Award
- Sandia Researcher Drew Kouri Wins Early-Career Research Grant
- Kinetica Database Now on Azure
- NXP Semiconductors Selects AWS for EDA
- Comparing Price-performance of HPE GreenLake for HPC vs. the Public Cloud
Editor’s Choice
The insideBIGDATA IMPACT 50 List for Q4 2021
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 [READ MORE…]
The Amazing Applications of Graph Neural Networks
In this contributed article, editorial consultant Jelani Harper points out that a generous portion of enterprise data is Euclidian and readily vectorized. However, there’s a wealth of non-Euclidian, multidimensionality data serving as the catalyst for astounding machine learning use cases.
Infographic: The Rise of No-Code Development Platforms
Our friends over at Saas Platform company in Ireland called TeamKonnect have developed new infographic called “The Rise of No-Code Development Platforms” which is provided below. This infographic is a 101 guide to No-Code Development Platforms. Rising in popularity in the last decade, these platforms offer an exciting opportunity for businesses and organizations to develop apps that meet their needs without the engagement of software engineers.
Big Data Industry Predictions for 2021
2020 has been year for the ages, with so many domestic and global challenges. But the big data industry has significant inertia moving into 2021. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming.
What is the Difference Between Business Intelligence, Data Warehousing and Data Analytics
In this contributed article, Christopher Rafter, President and COO at Inzata,, writes that in the age of Big Data, you’ll hear a lot of terms tossed around. Three of the most commonly used are “business intelligence,” “data warehousing” and “data analytics.” You may wonder, however, what distinguishes these three concepts from each other so let’s take a look.
