data warehouse


Welcome! You are invited to join a webinar: Production ML with the Autonomous Data Warehouse. After registering, you will receive a confirmation email about joining the webinar.

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We use data from a popular Kaggle competition, the Wisconsin Breast Cancer data, to build a binary classification model for the liklihood of a tumor being benign or malignant. We see how OAC's Data Visualization can be used to profile & explore the data, and can be used to do a rapid prototype of a Machine Learning model with DVML. See how ADW can be used to easily drop a Machine Learning model into production and enabled as a REST API for custom Applications and websites. By registering for this TechCast you give permission for your name and email address to be shared with the presenter and for BIWA User Community so we can inform you of future TechCasts and conferences of interest.


Introduction to Oracle Machine Learning - SQL Notebooks on top of Oracle Cloud Always Free Autonomous Data Warehouse - AMIS Oracle and Java Blog

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One of the relatively new features available with Oracle Autonomous Data Warehouse is Oracle Machine Learning Notebook. The description on Oracle's tutorial site states: "An Oracle Machine Learning notebook is a web-based interface for data analysis, data discovery, and data visualization." If you are familiar with Jupyter Notebooks (often Python based) then you may know and appreciate the Wiki like combination or markdown text and code snippets that are ideal for data lab'explorations' of data sets and machine learning models. I am quite a fan myself. Especially wrangling data, juggling with Pandas Data Frames and visualizing data with Plotly is good fun and it is quite easy to accomplish meaningful and advanced results.


Serverless Machine Learning Inference with Tika and TensorFlow

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The "strong applicant" prediction is provided by the DeepMatch API which uses features from your resume and the job. These features are computationally expensive to generate at scale and so need to be computed in advance, written to a serving store, and then combined for prediction when you visit. This post will guide you through how we built an event-driven serverless version of this architecture. It's aimed at data scientists, engineers, or anyone building ML products in production. Let's now talk more about these pieces in detail.


You Don't Need a Year of Data Cleansing - r4 Technologies

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So, does AI really require a year of data preparation? The answer is profound: No. And clients typically see results for their first use case within 3-4 months. All business applications, including AI, used to be built based on the data model that governed a specific problem. Such a data model typically resides in a data warehouse that can be located either on-premise or in the cloud.


Delivering Enterprise Analytics

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This article broadly describes the capabilities that constitute an enterprise analytics program or competency. The intention initially, was to provide tips on mitigating challenges encountered in implementing an analytics practice - but that is going to be relegated to a future article. IT projects in general, and analytics projects, in particular, are notoriously unsuccessful or "challenged". Focusing attention on the following short list prior to embarking on an analytics project or enterprise will help mitigate many challenges and obstacles encountered when delivering value through analytics projects. These outcomes are dependent on several factors, and achieving them requires implementing and orchestrating some, or all of the following core capabilities listed below.


Using technology to cut overhead costs in hospitals - Entrigna

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What is your hospital currently spending on overhead costs? Chances are it's closer to 25%. According to a study by The Commonwealth Fund, administrative costs account for 25.3% of hospitals budgets. This is money that does not typically improve patient care, but rather is associated with IT, scheduling or billing. There are multiple ways to reduce these costs by using different AI or IoT methodologies.


Why companies are in need of data lineage solutions

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Neelesh Salian will host the session "How do you evolve your data infrastructure?" Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Neelesh Salian, software engineer at Stitch Fix, a company that combines machine learning and human expertise to personalize shopping. As companies integrate machine learning into their products and systems, there are important foundational technologies that come into play.


Artifice No More? 'Intelligence' Revolves with Machine Learning

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Repeat after me: Machines are our friends; they're with us till the end! There, now doesn't that feel better? Oh, sure, the "narrative" says that machines will take jobs away from humans, but that's only somewhat true. Mostly, the machines of tomorrow will do what they've always done: streamline and expedite workflow across the spectrum of business processes. Keep in mind, the cotton gin eradicated thousands of jobs all over the Southern United States (and elsewhere), back when it stormed the market in the 1800s.


Io-Tahoe Named a Leader in the Use of Artificial Intelligence for Data Management by Enterprise Management Associates (EMA)

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Io-Tahoe, a pioneer in Smart Data Discovery and AI-Driven Data Catalog products, in its efforts to continue to transform the data discovery market, today announced it has been named a Leader in the use of artificial intelligence (AI) and machine learning (ML) for data management in a new research report and decision guide from Enterprise Management Associates (EMA). The research report, which names Io-Tahoe a Leader, says companies which deploy AI-enabled analytics and data management solutions can potentially save up to $5,000,000 a year. EMA research also finds that they can create more value through enhancements such as increased speed of innovation; the report claims that 83 per cent of the companies surveyed are already seeing cost savings, along with a significant reduction in annual person-hours required to complete analysis of the data. "AI enablement signifies a major shift from passive to active use of metadata," said John Santaferraro, EMA's Research Director, Analytics, Business Intelligence, and Data Management. "The passive use of metadata focused on definitions and documentation, while the active use of metadata focuses on the delivery of services, such as data cataloguing, data governance, data discovery, and master data services."


Io-Tahoe Named a Leader in the Use of Artificial Intelligence for Data Management by Enterprise Management Associates (EMA)

#artificialintelligence

Io-Tahoe, a pioneer in Smart Data Discovery and AI-Driven Data Catalog products, in its efforts to continue to transform the data discovery market, today announced it has been named a Leader in the use of artificial intelligence (AI) and machine learning (ML) for data management in a new research report and decision guide from Enterprise Management Associates (EMA). The research report, which names Io-Tahoe a Leader, says companies which deploy AI-enabled analytics and data management solutions can potentially save up to $5,000,000 a year. EMA research also finds that they can create more value through enhancements such as increased speed of innovation; the report claims that 83 per cent of the companies surveyed are already seeing cost savings, along with a significant reduction in annual person-hours required to complete analysis of the data. "AI enablement signifies a major shift from passive to active use of metadata," said John Santaferraro, EMA's Research Director, Analytics, Business Intelligence, and Data Management. "The passive use of metadata focused on definitions and documentation, while the active use of metadata focuses on the delivery of services, such as data cataloguing, data governance, data discovery, and master data services."