Data Mining


Manager, Data Integration (Data Warehouse ) - IoT BigData Jobs

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Cars.com is a leader in the automotive digital marketplace. Since 1997, we have built our B2B and B2C brand to preeminent status in the industry. While enjoying great stability, we continue to grow. Our workforce has more than doubled since 2006, and our revenue has increased more than 150% in that same time. Our highly engaged workforce enjoys our dedication to work/life balance, wellness and career growth as well as a rich set of employee programs.


Manager, Data Integration (Data Warehouse ) - IoT BigData Jobs

#artificialintelligence

Cars.com is a leader in the automotive digital marketplace. Since 1997, we have built our B2B and B2C brand to preeminent status in the industry. While enjoying great stability, we continue to grow. Our workforce has more than doubled since 2006, and our revenue has increased more than 150% in that same time. Our highly engaged workforce enjoys our dedication to work/life balance, wellness and career growth as well as a rich set of employee programs.


How Startups Can Use AI-Powered Tools to Scale Up

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What comes to your mind when you think of "artificial intelligence?" And you are right, sort of. AI is capable of doing all those things, plus it's capable of revolutionizing the business landscape. "For many people, artificial intelligence, or AI, is a mere concept, something that will happen in the future. But, in reality, AI has already become a part of mainstream businesses", says Michael Georgio, CMO of Imaginovation, a Raleigh, N.C.-based AI development company.


The Digital Elephant And The CFO

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Digital transformation is not new, and most companies have been on a digital journey since the 1990s. The digital transformation came in many forms -- Web, Web 2.0, SaaS, cloud, mobile, business intelligence, and big data for starters. Digital touches every operational aspect of a business in a similar way that the CFO role does. Eating this new digital elephant is a massive undertaking for any business. In the next two years, the CFO may well be the driver of this evolution of digital -- analytics, automation, and artificial intelligence (AI).


How to Build an Optimal Machine Learning Team - InformationWeek

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Machine learning solutions and workflows are meant to save time and vastly improve operational efficiency, but you still need the right human team to ensure every aspect is optimized and running on all cylinders. Before getting started with finding the right people, you should take stock of the business problem at hand. The goal of an ML initiative may be to optimize rote business processes (e.g. No matter the case, it is imperative to first establish how the ML model fits within the greater workflow. Once your organization understands the implications of ML on the business, then it can begin to assemble the optimal team.


Time-Series Anomaly Detection Kaggle NYC

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About the speaker: Kevin Chen is currently a self-taught ML practitioner concentrating on anomaly detection, time-series, streaming data, and (later) predictive analytics. He has a diverse technology and domain background spanning both enterprise and deep-tech. Previously, Kevin was a blockchain researcher/advocate involved with projects including IOTA and Fetch.AI. Before blockchain, he was in the financial sector as a back-end developer and data analyst at Citigroup and Aristeia Capital respectively. Kevin graduated from UVA with a Bachelors in CS. https://github.com/Kevin-Chen0


Multiprocessing with OpenCV and Python - PyImageSearch

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In this tutorial, you will learn how to use multiprocessing with OpenCV and Python to perform feature extraction. You'll learn how to use multiprocessing with OpenCV to parallelize feature extraction across the system bus, including all processors and cores on your computer. Today's tutorial is inspired by PyImageSearch reader, Abigail. Hey Adrian, I just read your tutorial on image hashing with OpenCV and really enjoyed it. I'm trying to apply image hashing to my research project at the university.


Deloitte: Public Sector Early Adopters Consider AI Vital to Organizational Success

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A new Deloitte survey found that approximately 74 percent of early adopters of artificial intelligence in the public sector believe AI will be "critically" or "very" important to the success of their organizations in the next couple of years. Deloitte said Friday the respondents cited quality control, workforce management and cybersecurity as top AI-related use cases in the public sector. Other use cases identified by respondents are information technology automation, predictive analytics, risk management and customer service. The survey respondents in the public sector identified enhanced features and functions, optimization of internal business operations and the capability to free up workers to be more creative through task automation as the top three benefits of AI. Seventy-six percent of early adopters in the public sector said they think AI will help augment human workers and 68 percent said AI will empower employees to make better decisions.


AXA International and New Markets: Customer-focused Tech and Data transformation across the globe

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In the age of digital transformation, global insurance provider AXA has adopted a decentralised approach to innovation. AXA International and New Markets (AXA INM) takes charge of AXA's operations in emerging and developing markets, covering Eastern European territories, Latin America, the GCC (Gulf Cooperation Council), Africa, India, Singapore, Malaysia and more. "There's a significant amount of Transformation to deliver across the 20-25 entities," says Kuldeep Kaushik, Chief Operating and Transformation Officer at AXA INM. "We have very different maturity levels across the businesses and very different technology landscapes as well. Part of my role is evaluating each of those entities and defining programmes which are specific to their maturity, business strategy, and needs and capabilities."


12 Impactful Ways To Incorporate Machine Learning Into Business Intelligence

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However, as essential to a company's success as BI is, many businesses don't take advantage of the tools that can improve their BI efforts. Combining machine learning with BI can have a far-reaching impact on the insights a business gets from its available data, making BI a true game-changer in helping companies improve productivity, quality, customer service and more. Machine learning has the ability to improve many operational processes, such as customer service, finance, marketing and much more. Machine learning can collect and use data from all of these aspects of a business and help you automate processes to increase productivity. Business intelligence professionals don't seem to realize that machine learning can seriously affect both the top and bottom of your customer funnel.