Getting To Trusted Data Via AI, Machine Learning And Blockchain
Bruno Aziza
JUNE 17, 2018
Establishing trust in data is critical. Organizations are now employing AI, Machine Learning, Blockchain to ensure data reliability and integrity.
Bruno Aziza
JUNE 17, 2018
Establishing trust in data is critical. Organizations are now employing AI, Machine Learning, Blockchain to ensure data reliability and integrity.
IBM Big Data Hub
JUNE 21, 2018
Running a machine learning pilot project is a great early step on the road to full adoption. To get started, you’ll need to build a cross-functional team of business analysts, engineers, data scientists and key stakeholders. From there, the process looks a lot like the scientific method taught in school.
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ScienceSoft
JUNE 20, 2018
Similar at first glance, Cassandra and HBase actually are quite different in terms of architecture, performance and data models. What are these differences and how do they influence the tasks that HBase and Cassandra perform? It’s all here.
DMBS2
JUNE 20, 2018
Every system — computer or otherwise — needs to deal with possibilities of damage or error. If it does this well, it may be regarded as “robust”, “mature(d), “strengthened”, or simply “improved” * Otherwise, it can reasonably be called “brittle” *It’s also common to use the word “harden(ed)” But I think that’s a poor choice, as brittle things are often also hard. 0.
Speaker: Timothy Chan, PhD., Head of Data Science
Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.
CIO Business Intelligence
JUNE 22, 2018
Investors, whether they be day traders at home or managers of large hedge funds, are data hungry. They pore over earnings reports and company filings and jump to read news alerts. If they’re on the super-sophisticated side, they may be using data models. And if they’re on the cutting edge, they may be using new “alternative” data sets to inform their decisions.
IBM Big Data Hub
JUNE 19, 2018
The modern data landscape demands more than one type of database. That’s IBM has rolled out JSON-document-based databases in Db2 and Cloudant, as well as partnered with select database providers to offer developer-focused database services through the IBM Compose platform.
Data Leaders Brief brings together the best content for data, strategy, and BI professionals from the widest variety of industry thought leaders.
DataRobot Blog
JUNE 19, 2018
by Jen Underwood. Embedded analytics is everywhere. From consumer gadgets, intelligent things, and applications to the rapidly expanding Everything as a Service (XaaS) subscription economy, analytics has been ubiquitously embedded into all areas. Read More.
Birst BI
JUNE 18, 2018
Retailers are focused more than ever on quickly adjusting to changing customer preferences and demand. Specialty’s Café and Bakery is a great example of a retailer that is using data to drive decisions related to product development and selection, inventories, staffing, and more to attract and keep customers. For example, retailers rely on business intelligence (BI) tools to predict future demand for products around known factors such as special events or holidays.
IBM Big Data Hub
JUNE 18, 2018
Data is business. The pace at which an organization can process data improves its ability to react to business events in real time.
Smarten
JUNE 19, 2018
Self-Serve Data Prep Should be Just That – Self-Serve! Self-serve has many meanings. You can pump your own gas, you can serve yourself at a buffet, and sometimes you can even do your own data preparation. You will notice that I said ‘sometimes’ That is because you have to choose the right tool if you want to really participate in self-serve data preparation.
Speaker: Anne Steiner and David Laribee
As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.
DMBS2
JUNE 20, 2018
In my initial post on brittleness I suggested that a typical process is: Build something brittle. Strengthen it over time. In many engineering scenarios, a fuller description could be: Design something that works in the base cases. Anticipate edge cases and sources of error, and design for them too. Implement the design. Discover which edge cases and error sources you failed to consider.
IBM Big Data Hub
JUNE 18, 2018
Data is driving business. And as volumes climb with no end in sight, companies have a decision to make: harness and extract insight from that data, or watch your competitors do it as they pass you by.
IBM Big Data Hub
JUNE 20, 2018
Data democratization allows data to be accessed across the organization and empowers individuals to use the data in their decision making and gain critical business insights. Data democratization is fast becoming a game changer as it’s moving towards a user centric micro-services based architecture.
Smarten
JUNE 21, 2018
This article provides a brief explanation of the Holt-Winters Forecasting model and its application in the business environment. What is the Holt-Winters Forecasting Algorithm? The Holt-Winters algorithm is used for forecasting and It is a time-series forecasting method. Time series forecasting methods are used to extract and analyze data and statistics and characterize results to more accurately predict the future based on historical data.
Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage
Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.
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