IBM Big Data Hub

AUGUST 11, 2023

Both computer scientists and business leaders have taken note of the potential of the data. Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. MLOps is the next evolution of data analysis and deep learning. What is MLOps?

Data Science 57

Data Science Machine Learning Cost-Benefit Deep Learning 57

datapine

MARCH 31, 2022

Despite these findings, the undeniable value of intelligence for business, and the incredible demand for BI skills, there is a severe shortage of BI-based data professionals – with a shortfall of 1.5 million in the USA alone. That’s where you come in. What does a profession in this field look like? Why Shift To A Business Intelligence Career?

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FineReport

MARCH 24, 2024

These professionals collaborate with IT teams, management, or data scientists to align analytical efforts with organizational objectives across various industries. They analyze, interpret, and manipulate complex data, track key performance indicators, and present insights to management through reports and visualizations.

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CIO Business Intelligence

JUNE 1, 2022

For businesses like the McLaren Group, these two trends are at the core of the conglomerate’s digital transformation and competitive strategy, on and off the track. . and artificial intelligence (AI) and machine learning (ML) technologies. . along with other information such as ambient and track temperatures ?

Manufacturing 108

Manufacturing Digital Transformation Predictive Analytics Strategy 108

Sisense

MAY 27, 2019

So, given the choice, which analytics job title should you choose: A data engineer or a data scientist? An engineer’s focus is on the data pipeline: building it and ensuring it’s efficient, so that there’s a free flow of data that serves the needs of the data scientists and the business.

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Statistics Machine Learning Visualization Big Data 75

CIO Business Intelligence

MARCH 16, 2022

The pace of technology has evolved so rapidly over the last few years, multiplied by the pandemic, that it’s hard to keep track — and what’s most in demand today may be obsolete tomorrow. Kiran Marri, chief scientist at CSS Corp., Identifying which emerging technologies will prove most useful is a challenge, though.

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Forecasting Machine Learning Technology Enterprise 97

DataRobot

JUNE 23, 2021

As they strive to improve models, data scientists continually try new approaches to refine their predictions. To help data scientists experiment faster, DataRobot has added Composable ML to automated machine learning. Advanced data scientists can also leverage Composable ML to build and train custom blueprints.

Machine Learning 52

Machine Learning Modeling Experimentation Visualization 52

AWS Big Data

MAY 4, 2023

Client The client represents the source Region where data scientists connect. Data scientists have access to the Jupyter notebook hosted on SageMaker. Amazon’s Open Data Sponsorship Program allows organizations to host free of charge on AWS. This solution was inspired by work with a key AWS customer, the UK Met Office.

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Data Processing Metadata Informatics Interactive 92

Domino Data Lab

JULY 28, 2021

For companies creating models to scale, an enterprise Machine Learning Operation (MLOps) platform not only needs to support enterprise-grade development and production, it needs to follow the same standard process that data scientists use. How Enterprise MLOps Integrates into the DSLC. Manage Stage. Develop Stage.

Data Science 59

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Cloudera

JANUARY 30, 2019

Data scientists require on-demand access to data, powerful processing infrastructure, and multiple tools and libraries for development and experimentation. With Cloudera Data Science Workbench, data scientists can: Use R, Python, or Scala along with the scale-out processing capabilities of Apache Spark 2.X Sound familiar?

Data Science 72

Data Science Machine Learning Experimentation Cost-Benefit 72

DataKitchen

MAY 10, 2021

Best practices include continuous monitoring of machine learning models for degradations in accuracy. . And the worst part – data errors take the fun out of data science. Remember your first data science courses? Or the excitement of your first encounter with your favorite tools? Data Observability Component of DataOps.

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Testing Manufacturing Data Quality Statistics 214

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

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Management Machine Learning Experimentation Metrics 110

Jen Stirrup

MAY 5, 2020

You can also use familiar languages, such as Python, R, Scala, JavaScript and code in Visual Studio. It is not much of a jump for data scientists to adopt new technology in Azure HDInsight. We can tear HDInsight up, and then tear it back down again when we don’t need it. In the Data Science world, Jupyter is very common.

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89

The Unofficial Google Data Science Blog

NOVEMBER 19, 2015

We briefly highlight some of the things we look for in data scientists we hire at Google and give tips on ways to prepare. At Google we’re always looking for talented people, and we’re interested in hiring great data scientists. In this short post, I’ll talk about how to get a job at Google as a data scientist. Be creative!

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Statistics Data Science Machine Learning Sales 75

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. On the one hand, basic statistical models (e.g.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Cloudera

MAY 22, 2018

Two key new capabilities, Experiments and Models, let data scientists build, train, and deploy models in a unified workflow; security enhancements automate user administration. As data scientists iteratively develop models, they often experiment with datasets, features, libraries, and algorithms as well as tuning hyperparameters.

Data Science 41

Data Science Snapshot Machine Learning Metadata 41

bridgei2i

JANUARY 25, 2022

In this podcast episode, AI enthusiasts Anirudh and Janci talk about how sincere curiosity and passion for learning can help anyone understand data science and become an active contributor. They discuss real-world examples of data science applications, the difference between machine learning and data science, and the skills needed to succeed.

Data Science 52

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Domino Data Lab

AUGUST 10, 2021

At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. Many modeling tools are open source and rely on Python, although other languages are commonly used such as R, C++, Java, Perl and many more.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Data Science 101

MAY 6, 2019

At Springboard , we recently sat down with Michael Beaumier, a data scientist at Google, to discuss his transition into the field, what the interview process is like, the future of data wrangling, and the advice he has for aspiring data professionals. in physics and now you’re a data scientist. You started off with a Ph.D.

Data Science 98

Data Science Modeling Machine Learning Testing 98

Jet Global

JULY 2, 2019

This is particularly useful when capturing event tracking or IoT data; though the uses of data lakes extend beyond just those scenarios. And using Azure’s built-in U-SQL library allows businesses allows you to write code once and have it automatically parallelized for the scale you need, whether in.NET languages, R, or Python.

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Data Lake Data Warehouse Big Data Machine Learning 73

Domino Data Lab

AUGUST 6, 2021

When you factor in the requirements of a business-critical machine learning model in a working enterprise environment, the old cat-herding meme won’t even get a smile. Monitor: includes monitoring the performance of the model, tracking metrics, as well as driving adoption of the model by those it was intended to serve.

Data Science 75

Data Science Testing Modeling Publishing 75

Domino Data Lab

JUNE 15, 2021

Tracking and managing a small number of experiments that deal with data in Excel is fairly straightforward. Snowpark is a new developer experience, which enables data scientists to leverage their favourite tools and deploy code directly within Snowflake. The challenges surface once the company hits the scalability wall.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

Domino Data Lab

JUNE 2, 2019

We held Rev 2 May 23-24 in NYC, as the place where “data science leaders and their teams come to learn from each other.” The conference more than doubled from last year: 2 days, 3 tracks, 5 sponsors, 39 sessions, 65 speakers, 600 attendees. — Randi R. The many reviews, discussions, debates, etc.,

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Data-driven Data Science Machine Learning Modeling 110

Domino Data Lab

AUGUST 8, 2019

In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. Across the board, organizations struggle with hiring enough data scientists. Meanwhile, O’Reilly printed a mini guidebook of biographies for everyone attending, which nearly looked like trading cards for scientists.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Insight

MARCH 29, 2019

To get some answers, Insight’s NYC office hosted a discussion focused on the many paths of growth available to today’s data scientists. L to R: Insight alums Deepna Devkar, Shanshan Ding, and Maureen Teyssier. She quickly learned that this feat wasn’t possible? Insight Fellows in New York.

Data Science 73

Data Science Management ROI Data Processing 73

Grooper

AUGUST 19, 2019

Read on to learn how data literacy, information as a second language, and insight-driven analytics take digital strategy to a new level. C-level executives and professionals alike must learn to speak a new language - data. Incidentally, this topic came up a couple of times in this conversation with a data scientist.

Analytics 54

Analytics Machine Learning Data-driven Data Science 54

Domino Data Lab

MARCH 27, 2019

Special thanks to Addison-Wesley Professional for permission to excerpt the following “Manipulating data with dplyr” chapter from the book, Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R. Data scientists spend countless hours wrangling data. Introduction.

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Domino Data Lab

FEBRUARY 4, 2019

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. We’ve been putting together the keynotes and tracks for Rev 2 and I’m super excited about what’s in store. Of course!!”

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

CIO Business Intelligence

OCTOBER 19, 2023

With Power BI, you can pull data from almost any data source and create dashboards that track the metrics you care about the most. You can also use Power BI to prepare and manage high-quality data to use across the business in other tools, from low-code apps to machine learning.

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Slice and Dice Scorecard Metrics Visualization 121

O'Reilly on Data

FEBRUARY 11, 2020

Data scientists and analysts, data engineers, and the people who manage them comprise 40% of the audience; developers and their managers, about 22%. Almost half (48%) of respondents say they use data analysis, machine learning, or AI tools to address data quality issues. Data quality might get worse before it gets better.

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Data Quality Metadata Data Governance Publishing 304

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. 42% of data scientists are solo practitioners or on teams of five or fewer people. 42% of data scientists are solo practitioners or on teams of five or fewer people. Auto-scale compute.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

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Deep Learning Modeling Metrics Testing 111

Cloudera

AUGUST 25, 2020

With only about 35% of machine learning models making into production in the enterprise ( IDC ), it’s no wonder that production machine learning has become one of the most important focus areas for data scientists and ML engineers alike. Model Monitoring. ML Model Monitoring Service, Metric Store, and SDK.

Machine Learning 74

Machine Learning Metrics Modeling Enterprise 74

Cloudera

APRIL 13, 2021

That’s because CDP has made it possible for them to modernize their legacy data platforms and extend machine learning (ML) and real-time analytics to public cloud, all while gaining cross-functional collaboration across the enterprise. . Use case #1: A biopharmaceutical company doubles its R&D productivity.

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Enterprise Cost-Benefit Data Warehouse Data Architecture 73

DataRobot Blog

DECEMBER 15, 2022

AI-powered Time Series Forecasting may be the most powerful aspect of machine learning available today. supervised learning and time series regression). For code-first users, we offer a code experience too, using the AP—both in Python and R—for your convenience. Why is it so difficult to do it manually?

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

IBM Big Data Hub

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. IBM watsonx.ai With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

Machine Learning 66

Machine Learning Data Warehouse Modeling Cost-Benefit 66

IBM Big Data Hub

JUNE 28, 2023

Scientists can finally focus on science and let the undifferentiated heavy lifting be replaced by intelligent, scalable and purpose-built cloud services and solutions, eliminating opportunities for error arising from manual or disparate data processing. What is Amazon Omics? This process alone saves hundreds of hours of productive time.

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