Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Its value is so significant that scaling data science has become the new business imperative with organizations spending tens of millions of dollars on data, technology and talent. What are Data Scientists?

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Testing and Data Observability. Download the 2021 DataOps Vendor Landscape here.

Testing 307

Testing Machine Learning Consulting Data Quality 307

O'Reilly on Data

OCTOBER 13, 2020

Our previous articles in this series introduce our own take on AI product management , discuss the skills that AI product managers need , and detail how to bring an AI product to market. From a technical perspective, it is entirely possible for ML systems to function on wildly different data. I/O validation.

Management 246

Management Metrics Machine Learning Modeling 246

Domino Data Lab

AUGUST 10, 2021

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. Why Is Modeling Important?

Modeling 64

Modeling IT Risk Management Deep Learning 64

DataRobot Blog

JULY 21, 2022

Machine learning and AI empower organizations to analyze data, discover insights, and drive decision making from troves of data. Data scientists need to understand the business problem and the project scope to assess feasibility, set expectations, define metrics, and design project blueprints. Exploring and Transforming Data.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

DataRobot Blog

SEPTEMBER 13, 2022

According to IDC , 83% of CEOs want their organizations to be more data-driven. 87% of CXOs shared that becoming an intelligent enterprise was their top priority. Data scientists could be your key to unlocking the potential of the Information Revolution—but what do data scientists do? What Do Data Scientists Do?

Forecasting 52

Forecasting Data-driven Machine Learning Optimization 52

DataRobot Blog

MARCH 23, 2023

This is why businesses are increasingly investing in machine learning operations (MLOps): IDC predicts by 2024, 60% of enterprises will have operationalized their ML workflows by using MLOps. 2 What Is MLOps and How Does It Help? Should You Build or Buy an MLOps Platform?

Machine Learning 52

Machine Learning Cost-Benefit Modeling Testing 52

Cloudera

AUGUST 25, 2020

Introducing Model Monitoring & Metrics Store In Cloudera Data Science Workbench. 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.

Machine Learning 74

Machine Learning Metrics Modeling Enterprise 74

DataRobot Blog

FEBRUARY 11, 2022

In the first part of the “Ever-growing Importance of MLOps” blog, we covered influential trends in IT and infrastructure, and some key developments in ML Lifecycle Automation. DataRobot’s MLOps product offers a host of features designed to transform organizations’ user experience, firstly, through its model-monitoring agents.

Machine Learning 52

Machine Learning Cost-Benefit ROI Data Processing 52

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. How Enterprise MLOps Integrates into the DSLC. Manage Stage.

Data Science 59

Data Science Enterprise Modeling Experimentation 59

Cloudera

JULY 26, 2021

With this first article of the two-part series on data product strategies, I am presenting some of the emerging themes in data product development and how they inform the prerequisites and foundational capabilities of an Enterprise data platform that would serve as the backbone for developing successful data product strategies.

Strategy 114

Strategy Marketing Unstructured Data Data Science 114

Domino Data Lab

JUNE 15, 2021

These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. The typical data science journey for a company starts with a small team that is tasked with a handful of specific problems.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

IBM Big Data Hub

JULY 17, 2023

In this blog, I will cover: What is watsonx.ai? How can you get started today? 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. What capabilities are included in watsonx.ai? What is watsonx.data?

Machine Learning 67

Machine Learning Data Warehouse Modeling Cost-Benefit 67

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

DataRobot

SEPTEMBER 14, 2021

Time and time again, we hear about the need for AI to support cross-functional teams and users. To provide the ability to integrate diverse data sources. To support the need to connect AI-driven decisions directly with existing business applications and services, like Snowflake, Salesforce, and ServiceNow.

Unstructured Data 52

Unstructured Data Data-driven Data Processing Modeling 52