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 —. Sound familiar? What is CDSW?

Data Science 71

Data Science Machine Learning Experimentation Cost-Benefit 71

CIO Business Intelligence

JUNE 14, 2023

Organization: AWS Price: US$300 How to prepare: Amazon offers free exam guides, sample questions, practice tests, and digital training. CDP Data Analyst The Cloudera Data Platform (CDP) Data Analyst certification verifies the Cloudera skills and knowledge required for data analysts using CDP.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

CIO Business Intelligence

NOVEMBER 7, 2022

CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects. Are data science teams set up for success? Have business leaders defined realistic success criteria and areas of low-risk experimentation?

Machine Learning 128

Machine Learning Data Science Experimentation IoT 128

Data Science and Beyond

SEPTEMBER 19, 2016

Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. The hardest parts of data science.

Data Science 66

Data Science Predictive Modeling Experimentation Deep Learning 66

CIO Business Intelligence

APRIL 2, 2024

Joanne Friedman, PhD, CEO, and principal of smart manufacturing at Connektedminds, says orchestrating success in digital transformation requires a symphony of integration across disciplines : “CIOs face the challenge of harmonizing diverse disciplines like design thinking, product management, agile methodologies, and data science experimentation.

Management 140

Management Digital Transformation Testing Experimentation 140

The Unofficial Google Data Science Blog

JANUARY 16, 2018

We present data from Google Cloud Platform (GCP) as an example of how we use A/B testing when users are connected. Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups.

Testing 58

Testing Experimentation Measurement Modeling 58

Domino Data Lab

MAY 15, 2019

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. These steps also reflect the experimental nature of ML product management.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

CIO Business Intelligence

DECEMBER 19, 2023

In the 2023 State of Data Science and Machine Learning Report , only 18% of respondents said that at least half their machine learning models make it into production. If CIOs don’t improve conversions from pilot to production, they may find their investors losing patience in the process and culture of experimentation.

Digital Transformation 143

Digital Transformation Unstructured Data Machine Learning Risk Management 143

The Unofficial Google Data Science Blog

APRIL 23, 2024

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Domino Data Lab

JUNE 13, 2019

Recently, Chhavi Yadav (NYU) and Leon Bottou (Facebook AI Research and NYU) indicated in their paper, “ Cold Case: The Lost MNIST Digits ”, how they reconstructed the MNIST (Modified National Institute of Standards and Technology) dataset and added 50,000 samples to the test set for a total of 60,000 samples. Did they overfit the test set?

Testing 83

Testing Data Science Experimentation Metadata 83

CIO Business Intelligence

JULY 6, 2022

Where cloud is most effective: One area I think cloud is just going to be really effective is any area which involves experimentation and has a high opportunity cost. Because when you can experiment, you can potentially enter a new business quickly, test an idea. At its core, it is sophisticated and complex math on data.

Experimentation 98

Experimentation Strategy Testing Risk 98

Data Science 101

SEPTEMBER 1, 2023

If I had more room for experimentation though, I’d definitely give svelte and solidjs a try. Everything related to spinning up a web server in development, writing code, hot reloading, running tests, cicd, deployments, etc. Honestly, the answer to this question changes every day for me. Learn JavaScript, and then TypeScript.

Experimentation 116

Experimentation Testing Technology IT 116

IBM Big Data Hub

NOVEMBER 3, 2023

To add to the complexity, there is a shortage of finding the right people with the right skills to take on development or data science projects. When you make it easier to work with events, other users like analysts and data engineers can start gaining real-time insights and work with datasets when it matters most.

Cost-Benefit 78

Cost-Benefit Experimentation Testing Digital Transformation 78

Domino Data Lab

AUGUST 6, 2021

An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. The complexity of models, and general limitations of expertise with data science among business leaders, creates an environment ripe for risk. Types of Model Risk.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

Data Science 101

JANUARY 15, 2020

Researchers/ scientists perform experiments to validate their hypothesis/ statements or to test a new product. Suppose we want to test the effectiveness of a new drug against a particular disease. Bias can cause a huge error in experimentation results so we need to avoid them. We randomly recruit subjects for that.

Experimentation 96

Experimentation Statistics Measurement Testing 96

AWS Big Data

NOVEMBER 2, 2023

AWS benchmark The AWS team performed benchmark tests on Spark workloads with Graviton2 on EMR Serverless using the TPC-DS 3 TB scale performance benchmarks. It showcases the potential of Graviton2 in delivering enhanced price-performance ratios, making it an attractive choice for organizations seeking to optimize their big data workloads.

Cost-Benefit 88

Cost-Benefit Big Data Testing Optimization 88

Cloudera

APRIL 9, 2021

To effectively leverage their predictive capabilities and maximize time-to-value these companies need an ML infrastructure that allows them to quickly move models from data pipelines, to experimentation and into the business. A/B testing). model packaging, deployment and serving. model monitoring. The ML Use Case.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Domino Data Lab

AUGUST 5, 2019

This Domino Data Science Field Note covers Chris Wiggins ‘s recent data ethics seminar at Berkeley. He also encouraged data scientists to understand how new data science algorithms rearrange power as well as how the history of data is a story of truth and power.

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

DataRobot Blog

JANUARY 10, 2023

ML model builders spend a ton of time running multiple experiments in a data science notebook environment before moving the well-tested and robust models from those experiments to a secure, production-grade environment for general consumption. 42% of data scientists are solo practitioners or on teams of five or fewer people.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Sisense

JANUARY 21, 2020

In general, it is not possible to give a rule of thumb about when data should be partitioned or combined. A new drug promising to reduce the risk of heart attack was tested with two groups. Now, let’s check a slightly different case in which grouping the data leads to incorrect results. It really depends on the circumstances.

Testing 104

Testing Risk Data-driven Statistics 104

Domino Data Lab

NOVEMBER 14, 2019

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. “Instead of general interpretability, we focus on the use of interpretations to produce insight from ML models as part of the larger data–science life cycle. ” and advocate.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”. And an LSOS is awash in data, right?

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. The AGI analyzes the data and identifies a rare genetic mutation linked to a specific disease.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

DataRobot

APRIL 19, 2021

When the machine learning algorithm is trained, data scientists take the outputs (predictions), apply business rules to turn predictions into decisions, and send these predictions and decisions into the organization’s computer systems. They need data assembly, data science, integration, and visualization skills.

Machine Learning 52

Machine Learning Visualization Data-driven Data Science 52

Domino Data Lab

DECEMBER 4, 2020

This tutorial will show how easy it is to integrate and use Pumas in the Domino Data Science Platform , and we will carry out a simple non-compartmental analysis using a freely available dataset. The Domino data science platform empowers data scientists to develop and deliver models with open access to the tools they love.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Data Science and Beyond

APRIL 5, 2021

My main "day job" focus in 2020 was on being the tech lead for Automattic’s new experimentation platform (ExPlat). Among other things, it gave me an opportunity to apply my favourite approach to Bayesian A/B testing in the wild, and get excited about other interesting causal inference work we have in the pipeline.

Experimentation 61

Experimentation Testing Publishing Measurement 61

FineReport

DECEMBER 11, 2019

In the past few years, the term “data science” has been widely used, and people seem to see it in every field. Big Data”, “Business Intelligence”, “ Data Analysis ” and “ Artificial Intelligence ” came into being. For a while, everyone seems to have begun to learn data analysis.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

Insight

APRIL 28, 2020

Originally posted on Open Data Science (ODSC). In this article, we share some data-driven advice on how to get started on the right foot with an effective and appropriate screening process. Designing a Data Science Interview Onsite interviews are indispensable, but they are time-consuming.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

Alation

SEPTEMBER 28, 2022

DataOps strategies share these common elements: Collaboration among data professionals and business stakeholders. Easy-to-experiment data development environment. Automated testing to ensure data quality. There are many inefficiencies that riddle a data pipeline and DataOps aims to deal with that. Simplicity.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

O'Reilly on Data

OCTOBER 19, 2021

It has far-reaching implications as to how such applications should be developed and by whom: ML applications are directly exposed to the constantly changing real world through data, whereas traditional software operates in a simplified, static, abstract world which is directly constructed by the developer. Data Science Layers.

IT 346

IT Testing Experimentation Software 346

Domino Data Lab

APRIL 7, 2019

If you are in research, excellent libraries like Allen NLP and NLP Architect are designed to make experimentation easier, although at the expense of feature completeness, speed and robustness.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

Experimentation 95

Experimentation Statistics Testing Strategy 95

DataKitchen

APRIL 13, 2021

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Genie — Distributed big data orchestration service by Netflix.

Testing 307

Testing Machine Learning Consulting Data Quality 307

O'Reilly on Data

MAY 14, 2020

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

Management 363

Management Experimentation B2B Machine Learning 363

Insight

MARCH 12, 2020

With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. I tested several different flavors of BERT for use as synopsis classifiers before settling on the DistilBERT model from Hugging Face. What happened next?

Modeling 59

Modeling Metadata Publishing Sales 59