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 Science 300

DataRobot Blog

DECEMBER 6, 2022

Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

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 76

Data Science Machine Learning Experimentation Visualization 76

Domino Data Lab

JUNE 10, 2020

Today, we announced the latest release of Domino’s data science platform which represents a big step forward for enterprise data science teams. You can identify data drift, missing information, and other issues, and take corrective action before bigger problems occur.

Data Science 82

Data Science Enterprise Experimentation Modeling 82

Rocket-Powered Data Science

JULY 7, 2019

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics).

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Domino Data Lab

JANUARY 24, 2019

In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. Over the last couple years, it would be hard to blame anyone for being overwhelmed looking at the data science platform market landscape. Proprietary (often GUI-driven) data science platforms.

Data Science 65

Data Science Marketing Data-driven Statistics 65

DataRobot

JANUARY 20, 2022

The following is a summary list of the key data-related priorities facing ICSs during 2022 and how we believe the combined Snowflake & DataRobot AI Cloud Platform stack can empower the ICS teams to deliver on these priorities. Key Data Challenges for Integrated Care Systems in 2022. Building data communities.

Data-driven 145

Data-driven Experimentation Predictive Modeling Data Warehouse 145

CIO Business Intelligence

JUNE 14, 2023

Data and big data analytics are the lifeblood of any successful business. Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

O'Reilly on Data

OCTOBER 13, 2020

From a technical perspective, it is entirely possible for ML systems to function on wildly different data. For example, you can ask an ML model to make an inference on data taken from a distribution very different from what it was trained on—but that, of course, results in unpredictable and often undesired performance. I/O validation.

Management 317

Management Machine Learning Metrics Modeling 317

CIO Business Intelligence

MARCH 7, 2023

Some IT organizations elected to lift and shift apps to the cloud and get out of the data center faster, hoping that a second phase of funding for modernization would come. There are similar concerns for CIOs looking to build data and analytics capabilities. Release an updated data viz, then automate a regression test.

Digital Transformation 145

Digital Transformation Data Governance Machine Learning Experimentation 145

IBM Big Data Hub

JANUARY 17, 2023

Due to the convergence of events in the data analytics and AI landscape, many organizations are at an inflection point. Furthermore, a global effort to create new data privacy laws, and the increased attention on biases in AI models, has resulted in convoluted business processes for getting data to users. Data governance.

Data-driven 56

Data-driven Enterprise Data Governance Data Science 56

CIO Business Intelligence

AUGUST 20, 2024

Pre-pandemic, high-performance teams were co-located, multidisciplinary, self-organizing, agile, and data-driven. These teams focused on delivering reliable technology capabilities, improving end-user experiences, and establishing data and analytics capabilities.

IT 141

IT Digital Transformation Experimentation Risk 141

CIO Business Intelligence

MAY 12, 2022

The tools include sophisticated pipelines for gathering data from across the enterprise, add layers of statistical analysis and machine learning to make projections about the future, and distill these insights into useful summaries so that business users can act on them. Visual IDE for data pipelines; RPA for rote tasks. Highlights.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

datapine

JANUARY 31, 2022

Because things are changing and becoming more competitive in every sector of business, the benefits of business intelligence and proper use of data analytics are key to outperforming the competition. BI software uses algorithms to extract actionable insights from a company’s data and guide its strategic decisions.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

CIO Business Intelligence

DECEMBER 19, 2023

After all, every department is pressured to drive efficiencies and is clamoring for automation, data capabilities, and improvements in employee experiences, some of which could be addressed with generative AI. As every CIO can attest, the aggregate demand for IT and data capabilities is straining their IT leadership teams.

Digital Transformation 143

Digital Transformation Unstructured Data Machine Learning Risk Management 143

DataRobot Blog

MARCH 7, 2023

DataRobot on Azure accelerates the machine learning lifecycle with advanced capabilities for rapid experimentation across new data sources and multiple problem types. This generates reliable business insights and sustains AI-driven value across the enterprise.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

Cloudera

AUGUST 20, 2020

Businesses had to literally switch operations, and enable better collaboration and access to data in an instant — while streamlining processes to accommodate a whole new way of doing things. This year, we hope to see even more stories of ML and AI driven innovation among the finalists. That’s really important.

Enterprise 121

Enterprise Experimentation Uncertainty Deep Learning 121

AWS Big Data

JUNE 20, 2023

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) and data sources residing in AWS, on-premises, or other cloud systems using SQL or Python. Solution overview Data scientists are generally accustomed to working with large datasets.

Data Lake 103

Data Lake Data Science Recreation/Entertainment Data-driven 103

CIO Business Intelligence

NOVEMBER 21, 2023

After all, 41% of employees acquire, modify, or create technology outside of IT’s visibility , and 52% of respondents to EY’s Global Third-Party Risk Management Survey had an outage — and 38% reported a data breach — caused by third parties over the past two years. There may be times when department-specific data needs and tools are required.

IT 137

IT Risk Cost-Benefit Data Science 137

The Unofficial Google Data Science Blog

APRIL 23, 2024

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Domino Data Lab

FEBRUARY 12, 2020

This post is for people making technology decisions, by which I mean data science team leads, architects, dev team leads, even managers who are involved in strategic decisions about the technology used in their organizations. Suppose you have an “expensive” function to run repeatedly over data records. Introduction. ray.remote?

Experimentation 137

Experimentation Modeling Data Science Machine Learning 137

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

CIO Business Intelligence

OCTOBER 1, 2022

The partners say they will create the future of digital manufacturing by leveraging the industrial internet of things (IIoT), digital twin , data, and AI to bring products to consumers faster and increase customer satisfaction, all while improving productivity and reducing costs. Data and AI as digital fundamentals.

Manufacturing 145

Manufacturing Digital Transformation IoT Internet of Things 145

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.

Data Science 59

Data Science Enterprise Modeling Experimentation 59

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

CIO Business Intelligence

DECEMBER 19, 2024

In especially high demand are IT pros with software development, data science and machine learning skills. Government agencies and nonprofits also seek IT talent for environmental data analysis and policy development.

IT 52

IT IoT Machine Learning Optimization 52

Domino Data Lab

AUGUST 5, 2019

This Domino Data Science Field Note covers Chris Wiggins ‘s recent data ethics seminar at Berkeley. Data Scientists, Tradeoffs, and Data Ethics. As more companies become model-driven , data scientists are uniquely positioned to drive innovation and to help their companies remain economically strong.

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

Cloudera

APRIL 9, 2021

This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

DataRobot Blog

OCTOBER 18, 2022

Organizations are looking for AI platforms that drive efficiency, scalability, and best practices, trends that were very clear at Big Data & AI Toronto. DataRobot Booth at Big Data & AI Toronto 2022. Monitoring and Managing AI Projects with Model Observability. Accelerating Value-Realization with Industry Specific Use Cases.

Big Data 96

Big Data Forecasting Machine Learning Experimentation 96

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 Deep Learning Modeling Testing 99

CIO Business Intelligence

AUGUST 1, 2023

But with all the excitement and hype, it’s easy for employees to invest time in AI tools that compromise confidential data or for managers to select shadow AI tools that haven’t been through security, data governance, and other vendor compliance reviews.

Digital Transformation 98

Digital Transformation Unstructured Data Strategy Data Science 98

CIO Business Intelligence

SEPTEMBER 27, 2023

The decision to launch this pilot phase was driven by a desire to stay ahead in the field, assess the potential applications of gen AI, and subsequently transition into targeted proof-of-concept projects,” says Vlad-George Iacob, vice president of engineering at Hackajob. IT leaders should create a “safe space” for innovation, Iacob says.

Strategy 126

Strategy Data-driven Risk Technology 126

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. As a business becomes model driven, Model Risk Management can help limit risks for sustainable growth in almost any industry vertical. Types of Model Risk. Model implementation.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

Sisense

JANUARY 21, 2020

Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. How can good data lead to faulty conclusions? How does this happen? 9/10 = 90%.

Testing 104

Testing Data-driven Risk 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. Yet, despite the complexity (or because of it), data scientists and researchers curate and use different languages, tools, packages, techniques, and frameworks to tackle the problem they are trying to solve.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

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.

Big Data 52

Big Data Experimentation Machine Learning Data-driven 52

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Without large amounts of labeled training data solving most AI problems is not possible.

Management 110

Management Machine Learning Experimentation Metrics 110