O'Reilly on Data

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. Why: Data Makes It Different.

IT 342

IT Testing Experimentation Software 342

CDW Research Hub

SEPTEMBER 15, 2020

Data is a valuable asset that can help businesses reduce costs, make informed decisions, and better understand what their customers need. However, data can easily become useless if it is trapped in an outdated technology. Another critical step is to create a framework to integrate your data. Build a Best of Breed Data Platform.

Business Intelligence 52

Business Intelligence Prescriptive Analytics Unstructured Data Data Warehouse 52

DataKitchen

APRIL 13, 2021

This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Companies that implement DataOps find that they are able to reduce cycle times from weeks (or months) to days, virtually eliminate data errors, increase collaboration, and dramatically improve productivity.

Testing 300

Testing Machine Learning Consulting Data Quality 300

IBM Big Data Hub

MAY 17, 2023

They use drones for tasks as simple as aerial photography or as complex as sophisticated data collection and processing. The complexity of the task determines the cost and availability of functions, as well as how data is processed and integrated. The global commercial drone market is projected to grow from USD 8.15

Data Collection 79

Data Collection Visualization Data-driven Unstructured Data 79

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? What is machine learning?

Machine Learning 56

Machine Learning Data Science Statistics Deep Learning 56

Birst BI

NOVEMBER 20, 2018

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. The simplest answer is that these terms refer to some of the many analytic methods available to Data Scientists.

Statistics 84

Statistics Machine Learning Deep Learning Optimization 84

Cloudera

JULY 15, 2019

At a time when machine learning, deep learning, and artificial intelligence capture an outsize share of media attention, jobs requiring SQL skills continue to vastly outnumber jobs requiring those more advanced skills. Educating Data Analysts at Scale. What We Teach.

Big Data 57

Big Data Deep Learning Data Warehouse Data-driven 57

Domino Data Lab

OCTOBER 14, 2020

Guest post by Jeff Melching, Distinguished Engineer / Chief Architect Data & Analytics. We’ve developed a model-driven software platform, called Climate FieldView , that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning in marketing and sales According to Forbes , marketing and sales teams prioritize AI and ML more than any other enterprise department. Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). Many stock market transactions use ML.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

Alation

APRIL 14, 2022

Moving data to the cloud can bring immense operational benefits. However, the sheer volume and complexity of today’s enterprise data can cause downstream headaches for data users. Semantics, context, and how data is tracked and used mean even more as you stretch to reach post-migration goals. Data pipeline orchestration.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Cloudera

SEPTEMBER 12, 2018

It’s a big week for us, as many Clouderans descend on New York for the Strata Data Conference. The week is typically filled with exciting announcements from Cloudera and many partners and others in the data management, machine learning and analytics industry. Two weeks ago, we announced the finalists.

Machine Learning 45

Machine Learning Big Data Data Science Data Warehouse 45

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20