Cloudera

JANUARY 6, 2021

Python is used extensively among Data Engineers and Data Scientists to solve all sorts of problems from ETL/ELT pipelines to building machine learning models. Apache HBase is an effective data storage system for many workflows but accessing this data specifically through Python can be a struggle. builder.

Machine Learning 133

Machine Learning Data Science Management Enterprise 133

datapine

MAY 14, 2019

By gaining the ability to understand, quantify, and leverage the power of online data analysis to your advantage, you will gain a wealth of invaluable insights that will help your business flourish. The ever-evolving, ever-expanding discipline of data science is relevant to almost every sector or industry imaginable – on a global scale.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

Cloudera

NOVEMBER 17, 2021

It’s no secret that Data Scientists have a difficult job. It feels like a lifetime ago that everyone was talking about data science as the sexiest job of the 21st century. There’s recognition that it’s nearly impossible to find the unicorn data scientist that was the apple of every CEO’s eye in 2012. AutoML with TPOT.

Machine Learning 123

Machine Learning Visualization Data Science Metrics 123

Rocket-Powered Data Science

MAY 21, 2024

Pure Storage empowers enterprise AI with advanced data storage technologies and validated reference architectures for emerging generative AI use cases. Summary AI devours data. See additional references and resources at the end of this article. At the NVIDIA GTC 2024 conference, Pure Storage announced so much more!

Cost-Benefit 239

Cost-Benefit Unstructured Data Enterprise Technology 239

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 80

Data Science Data Analytics Prescriptive Analytics Analytics 80

Analytics Vidhya

JUNE 27, 2022

Anish has been a Lead Data Science consultant for various Fortune 500 customers for a long time and has helped over 2000 employees into the Data Science profession. He will be explaining MLOps referred as Machine learning operations, the present and future state […].

Consulting 222

Consulting Machine Learning Data Science Interactive 222

David Menninger's Analyst Perspectives

MARCH 13, 2024

The process of managing all these parts is referred to as Machine Learning Operations or MLOps. Domino Data Lab was formed to provide a software platform for MLOps and has since expanded its capabilities into a broader enterprise AI platform. First, there is a shortage of skills.

Machine Learning 196

Machine Learning Data-driven Modeling Software 196

KDnuggets

MARCH 2, 2023

The latest KDnuggets cheat sheet covers using ChatGPT to your advantage as a data scientist. It's time to master prompt engineering, and here is a handy reference for helping you along the way.

Data Science 159

Data Science IT Machine Learning Analytics 159

Cloudera

OCTOBER 26, 2022

?. It’s no secret that advancements like AI and machine learning (ML) can have a major impact on business operations. Cloudera has seen a lot of opportunity to extend even more time saving benefits specifically to data scientists with the debut of Applied Machine Learning Prototypes (AMPs).

Machine Learning 76

Machine Learning Deep Learning Cost-Benefit Forecasting 76

Cloudera

APRIL 27, 2023

Built on Kubernetes, Cloudera Machine Learning ( CML ) gives data science teams a platform that works across each stage of Machine Learning Lifecycle, supporting exploratory data analysis, the model development and moving those models and applications to production (aka MLOps).

Machine Learning 77

Machine Learning Dashboards Deep Learning Testing 77

IBM Big Data Hub

DECEMBER 19, 2023

Anomalies are not inherently bad, but being aware of them, and having data to put them in context, is integral to understanding and protecting your business. The challenge for IT departments working in data science is making sense of expanding and ever-changing data points.

Machine Learning 67

Machine Learning Optimization Unstructured Data Sales 67

Cloudera

JANUARY 30, 2019

It’s official – Cloudera and Hortonworks have merged , and today I’m excited to announce the availability of Cloudera Data Science Workbench (CDSW) for Hortonworks Data Platform (HDP). Trusted by large data science teams across hundreds of enterprises —. What is CDSW?

Data Science 74

Data Science Machine Learning Experimentation Cost-Benefit 74

Analytics Vidhya

FEBRUARY 13, 2023

Introduction Machine learning models have come a long way in the past few decades but still face several challenges, including robustness. Robustness refers to the ability of a model to work well on unseen data, an essential requirement for real-world applications.

Modeling 254

Modeling Machine Learning Analytics Data Science 254

Cloudera

AUGUST 24, 2022

Cloudera Machine Learning (CML) is a cloud-native and hybrid-friendly machine learning platform. It unifies self-service data science and data engineering in a single, portable service as part of an enterprise data cloud for multi-function analytics on data anywhere. References.

Machine Learning 81

Machine Learning Metadata Testing Data Science 81

Domino Data Lab

JULY 15, 2021

In the multiverse of data science, the tool options continue to expand and evolve. While there are certainly engineers and scientists who may be entrenched in one camp or another (the R camp vs. Python, for example, or SAS vs. MATLAB), there has been a growing trend towards dispersion of data science tools.

Data Science 145

Data Science Visualization Modeling Enterprise 145

Cloudera

JANUARY 20, 2021

Machine learning is now being used to solve many real-time problems. One big use case is with sensor data. Corporations now use this type of data to notify consumers and employees in real-time. Make sure you read Part 1 and Part 2 before reading this installment. Background / Overview. How to Run This Demo Application.

Machine Learning 113

Machine Learning Modeling Data Science Big Data 113

Paul DeBeasi

JANUARY 25, 2019

Developers with no data science experience are now able to integrate Machine Learning (ML) with IoT. As the number of IoT endpoints proliferate, the need for organizations to understand how to architect machine learning with IoT will grow rapidly. Data scientists often focus on building ML models (e.g.,

IoT 75

IoT Machine Learning Deep Learning Data Science 75

Rocket-Powered Data Science

JUNE 17, 2022

The dominant references everywhere to Observability was just the start of awesome brain food offered at Splunk’s.conf22 event. Reference ) The latest updates to the Splunk platform address the complexities of multi-cloud and hybrid environments, enabling cybersecurity and network big data functions (e.g., is here, now!

Machine Learning 238

Machine Learning Recreation/Entertainment Risk Business Objectives 238

FineReport

JULY 28, 2021

Good data can give you keen insights, convincing evidence to make informed decisions. By observing and analyzing data, we can develop more accurate theories and formulate more effective solutions. For this reason, data science and/vs. Definition: BI vs Data Science vs Data Analytics.

Business Intelligence 98

Business Intelligence Data Science Data Analytics Analytics 98

Analytics Vidhya

SEPTEMBER 16, 2022

This article was published as a part of the Data Science Blogathon. Often referred to as the ‘Hello World’ of Computer Vision, it’s a great starting […].

Data Science 349

Data Science Publishing Analytics IT 349

Jen Stirrup

NOVEMBER 15, 2022

How can systems thinking and data science solve digital transformation problems? Understandably, organizations focus on the data and the technology since data retrieval is often viewed as a data problem. However, the thrust here is not to diminish data science or data engineering.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

Rocket-Powered Data Science

JULY 19, 2023

A similarly high percentage of tabular data usage among data scientists was mentioned here. If my explanation above is the correct interpretation of the high percentage, and if the statement refers to successfully deployed applications (i.e.,

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

Decision Management Solutions

AUGUST 8, 2019

Trust is a big deal when it comes to machine learning. Black box” algorithms, concerns about bias and a sense that data scientists may know everything about the data but nothing about the business all undermine trust in machine learning models. Deliver Results Deliver business results.

Machine Learning 60

Machine Learning Modeling Data Science Business Objectives 60

Analytics Vidhya

DECEMBER 1, 2021

This article was published as a part of the Data Science Blogathon. Image Source: GitHub Table of Contents What is Data Engineering? Components of Data Engineering Object Storage Object Storage MinIO Install Object Storage MinIO Data Lake with Buckets Demo Data Lake Management Conclusion References What is Data Engineering?

Data Lake 332

Data Lake Data Science Publishing Software 332

DataRobot Blog

JULY 28, 2022

Rapid advances in machine learning in recent years have begun to lower the technical hurdles to implementing AI, and various companies have begun to actively use machine learning. As a DataRobot data scientist , I have worked with team members on a variety of projects to improve the business value of our customers.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Cloudera

JULY 9, 2018

Some call it the “golden triangle,” but in this blog, we refer to it as the iron triangle. Most organizations struggle to unlock data science in the enterprise. It’s powerful features finally get data scientists, analysts, and business teams speaking the same language. Why the Data Science Iron Triangle Matters.

Machine Learning 42

Machine Learning Data Science Testing Visualization 42

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

Analytics Vidhya

AUGUST 25, 2021

This article was published as a part of the Data Science Blogathon Introduction Recommender System is a software system that provides specific suggestions to users according to their preferences. Items refer to any product that the recommender system suggests to its user like movies, music, news, travel […].

Data Science 281

Data Science Publishing Software Analytics 281

Analytics Vidhya

MAY 8, 2024

Introduction Have you ever thought robots would learn independently with the power of LLMs? In robotics, sim-to-real transfer refers to transferring policies learned in simulation to the real world. It’s happening now! DrEureka is automating sim-to-real design in robotics.

Analytics 269

Analytics IT Deep Learning Machine Learning 269

Analytics Vidhya

DECEMBER 14, 2021

This article was published as a part of the Data Science Blogathon Introduction In today’s digital world, social media platforms like Facebook, Whatsapp, Twitter have become a part of our everyday schedule. Many NLP techniques can be used on the text data available from Twitter.

Data Science 264

Data Science Publishing Analytics Machine Learning 264

Analytics Vidhya

DECEMBER 4, 2020

This article was published as a part of the Data Science Blogathon. Introduction Random Forests are always referred to as black-box models. Let’s try. The post Lets Open the Black Box of Random Forests appeared first on Analytics Vidhya.

Data Science 348

Data Science Publishing Modeling Analytics 348

Rocket-Powered Data Science

JULY 14, 2023

A similarly high percentage of tabular data usage among data scientists was mentioned here. As I progressed in my career into management roles for enterprise data systems, I gained a deeper understanding and appreciation of the synergies and interdependencies between system and user requirements.

Analytics 166

Analytics Internet of Things Prescriptive Analytics Data Science 166

Rocket-Powered Data Science

OCTOBER 6, 2023

In the enterprise, sentinel analytics is most timely and beneficial when applied to real-time, dynamic data streams and time-critical decisions. One could say that sentinel analytics is more like unsupervised machine learning, while precursor analytics is more like supervised machine learning. ” “91.9%

Business Analytics 157

Business Analytics Prescriptive Analytics Analytics Diagnostic Analytics 157

Domino Data Lab

APRIL 6, 2021

Training machine learning models, especially neural networks, is compute-intensive. Then I’ll briefly mention several higher-level toolkits for machine learning that leverage Ray. return name, addresses[name] start = time.time() for name in addresses: reference = ray_lookup.remote(name) # Start async.

Data Science 81

Data Science Machine Learning Optimization Modeling 81

Rocket-Powered Data Science

MARCH 19, 2019

Analytics are the products, the outcomes, and the ROI of our Big Data , Data Science, AI, and Machine Learning investments! AI strategies and data strategies should therefore focus on outcomes first. Analytics are the outcomes of data activities (data science, machine learning, AI) within the organization.

Analytics 193

Analytics Metrics Data-driven Machine Learning 193

Cloudera

JANUARY 13, 2021

Refer to configuration steps above and make sure you have Python installed on every node on your cluster as well as the environment variables set properly to the right path. There are JIRAs filed for fixing issues like these, but refer to the supported methods mentioned in this post to access HBase tables. than that in driver 3.6,

Machine Learning 52

Machine Learning Data Science Modeling IT 52

Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Data science. Session Summary.

Data Science 112

Data Science Machine Learning Advertising Modeling 112