DataRobot Blog

NOVEMBER 15, 2022

Organizations are looking to deliver more business value from their AI investments, a hot topic at Big Data & AI World Asia. At the well-attended data science event, a DataRobot customer panel highlighted innovation with AI that challenges the status quo. Automate with Rapid Iteration to Get to Scale and Compliance.

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Alation

SEPTEMBER 28, 2022

The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In essence, DataOps is a practice that helps organizations manage and govern data more effectively. What exactly is DataOps ?

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Cloudera

AUGUST 8, 2022

In June 2022, Cloudera announced the general availability of Apache Iceberg in the Cloudera Data Platform (CDP). The general availability covers Iceberg running within some of the key data services in CDP, including Cloudera Data Warehouse ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ).

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Cloudera

SEPTEMBER 13, 2018

It’s a bit surprising to note, then, that perhaps the most limiting factor in data science and machine learning today is people. Feature engineering, or the process of transforming raw data into a chosen representation of the underlying problem to be solved, is the data science process most ripe for automation.

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Sisense

AUGUST 14, 2020

Certain proven methods and use cases are invaluable in helping product teams understand how to implement AI in apps to reproduce and enhance existing successes. Developing the right data model for your in-app AI is now a critical branch of programming, because correctly prepared data is vital to the algorithm model.

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IBM Big Data Hub

AUGUST 11, 2023

The advancement of computing power over recent decades has led to an explosion of digital data, from traffic cameras monitoring commuter habits to smart refrigerators revealing how and when the average family eats. Both computer scientists and business leaders have taken note of the potential of the data. How the models are stored.

Data Science 57

Data Science Machine Learning Cost-Benefit Deep Learning 57

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Its value is so significant that scaling data science has become the new business imperative with organizations spending tens of millions of dollars on data, technology and talent. What are Data Scientists?

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Cloudera

MAY 22, 2018

Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. Data scientists often develop models using a variety of Python/R open source packages. Experiments.

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The Unofficial Google Data Science Blog

OCTOBER 31, 2016

By PATRICK RILEY For a number of years, I led the data science team for Google Search logs. Some people seemed to be naturally good at doing this kind of high quality data analysis. To answer those questions, I put together a document shared Google-wide which I optimistically and simply titled “Good Data Analysis.”

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Domino Data Lab

OCTOBER 7, 2020

Danger of Big Data. Big data is the rage. This could be lots of rows (samples) and few columns (variables) like credit card transaction data, or lots of columns (variables) and few rows (samples) like genomic sequencing in life sciences research. Statistical methods for analyzing this two-dimensional data exist.

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Domino Data Lab

JANUARY 6, 2019

In Paco Nathan ‘s latest column, he explores the theme of “learning data science” by diving into education programs, learning materials, educational approaches, as well as perceptions about education. He is also the Co-Chair of the upcoming Data Science Leaders Summit, Rev. Learning Data Science.

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Alation

FEBRUARY 20, 2020

For those of us who champion the power of data, the past five years have been an incredible ride thanks to the rise of big data. Of course, data hasn’t accomplished any of this. Data isn’t the protagonist in any of the stories above. People use data. Humans are. Consumption Requires Context.

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DataRobot Blog

JUNE 16, 2022

Airflow is a perfect tool to orchestrate stages of the DataRobot machine learning (ML) pipeline, because it provides an easy but powerful solution to integrate DataRobot capabilities into bigger pipelines, combine it with other services, as well as to clean your data, and store or publish the results. Local file intake/output. References. *

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Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. This blog post provides a concise session summary, a video, and a written transcript. data science’s emergence as an interdisciplinary field – from industry, not academia. Session Summary. Key highlights from the session include.

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Data Science Machine Learning Data Governance Modeling 109

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Testing and Data Observability. Reflow — A system for incremental data processing in the cloud.

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Domino Data Lab

JUNE 15, 2021

These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. The typical data science journey for a company starts with a small team that is tasked with a handful of specific problems.

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CIO Business Intelligence

JULY 11, 2023

These cutting-edge models can potentially transform industries, especially in regulated sectors like healthcare and life sciences, where they could be used for drug discovery, clinical trial analysis, improved diagnostics, personalized patient care, and more. This allows human users to verify the information and make informed decisions.

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Domino Data Lab

JULY 21, 2019

Julia Lane , NYU Professor, Economist and cofounder of the Coleridge Initiative , presented “ Where’s the Data: A New Approach to Social Science Search & Discovery ” at Rev. Lane described the approach that the Coleridge Initiative is taking to address the science reproducibility challenge. Session Summary.

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Domino Data Lab

JULY 28, 2021

The data science lifecycle (DLSC) has been defined as an iterative process that leads from problem formulation to exploration, algorithmic analysis and data cleaning to obtaining a verifiable solution that can be used for decision making. The data science process in a business environment begins with the Manage stage.

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DataKitchen

MAY 10, 2021

Data errors impact decision-making. Data errors infringe on work-life balance. Data errors also affect careers. If you have been in the data profession for any length of time, you probably know what it means to face a mob of stakeholders who are angry about inaccurate or late analytics.

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Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. 1998) and others).

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IBM Big Data Hub

JUNE 28, 2023

Today, the rate of data volume increase is similar to the rate of decrease in sequencing cost. Processing terabytes or even petabytes of increasing complex omics data generated by NGS platforms has necessitated development of omics informatics. What is Amazon Omics?

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Domino Data Lab

APRIL 21, 2021

Historically, fraud detection has been performed by analysts who are responsible for identifying and tracing suspicious patterns in the data. In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data. Data Ingestion & Exploratory Data Analysis.

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Statistics Machine Learning Modeling Metrics 69

Smart Data Collective

OCTOBER 14, 2019

Regardless of if you’re a data science professional or an IT department who wants to help your company have more successful data science projects, it’s essential to have some data science tools under your belt to avail of when needed. Here are some open-source options to consider. Kubernetes.

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DataRobot Blog

FEBRUARY 21, 2022

With growing pressure on data scientists, every organization needs to ensure that their teams are empowered with the right tools. Data science notebooks have become a crucial part of the data science practice. Governance and Security: How can I ensure security of my data? How can I mitigate security risks?

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IBM Big Data Hub

OCTOBER 20, 2023

AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually. AI platforms assist with a multitude of tasks ranging from enforcing data governance to better workload distribution to the accelerated construction of machine learning models.

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Machine Learning Manufacturing Cost-Benefit Deep Learning 78

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

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Data Science Experimentation Data-driven Machine Learning 66

IBM Big Data Hub

JULY 17, 2023

In this blog, I will cover: What is watsonx.ai? While they require task-specific labeled data for fine tuning, they also offer clients the best cost performance trade-off for non-generative use cases. What capabilities are included in watsonx.ai? What is watsonx.data? What capabilities are included in watsonx.data?

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Machine Learning Data Warehouse Modeling Cost-Benefit 66

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

by NIALL CARDIN, OMKAR MURALIDHARAN, and AMIR NAJMI When working with complex systems or phenomena, the data scientist must often operate with incomplete and provisional understanding, even as she works to advance the state of knowledge. There has been debate as to whether the term “data science” is necessary.

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datapine

SEPTEMBER 4, 2023

Table of Contents 1) The Benefits Of Data Visualization 2) Our Top 27 Best Data Visualizations 3) Interactive Data Visualization: What’s In It For Me? 4) Static vs. Animated Data Visualization Data is the new oil? No, data is the new soil.” ” – David McCandless Humans are visual creatures.

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Smart Data Collective

NOVEMBER 9, 2022

Based on the changes it has created, most experts agree that the primary goal of Machine Learning in this context is to reproduce more accurate predictions. As a result of the activity of artificial intelligence, the machine learns, remembers, and reproduces the correct option. Top ML Companies.

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Domino Data Lab

AUGUST 1, 2021

For example, article 22 of the General Data Protection Regulation (GDPR) introduces the right of explanation – the power of an individual to demand an explanation on the reasons behind a model-based decision and to challenge the decision if it leads to a negative impact for the individual. A14 : no checking account.

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Modeling Deep Learning Machine Learning Knowledge Discovery 139

O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. Why is high-quality and accessible data foundational? Re-analyzing existing data is often very bad.”

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Machine Learning Statistics Data Quality Modeling 346

datapine

DECEMBER 28, 2021

Statistics are infamous for their ability and potential to exist as misleading and bad data. Exclusive Bonus Content: Download Our Free Data Integrity Checklist. Get our free checklist on ensuring data collection and analysis integrity! To get this journey started let’s look at the misleading statistics definition.

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