DataRobot Blog

MARCH 8, 2023

Today, SAP and DataRobot announced a joint partnership to enable customers connect core SAP software, containing mission-critical business data, with the advanced Machine Learning capabilities of DataRobot to make more intelligent business predictions with advanced analytics.

Enterprise 105

Enterprise Experimentation Machine Learning Modeling 105

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

Domino Data Lab

AUGUST 6, 2021

Data science is an incredibly complex field. When you factor in the requirements of a business-critical machine learning model in a working enterprise environment, the old cat-herding meme won’t even get a smile. Let’s use an example business problem to illustrate the four steps of the data science lifecycle.

Data Science 75

Data Science Testing Modeling Publishing 75

Cloudera

OCTOBER 4, 2023

Flexible use of compute resources on analytics — which is even more important as we start performing multiple different types of analytics, some critical to daily operations and some more exploratory and experimental in nature, and we don’t want to have resource demands collide. appeared first on Cloudera Blog.

Data Warehouse 84

Data Warehouse Experimentation Cost-Benefit Dashboards 84

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.

Big Data 52

Big Data Experimentation Data-driven Machine Learning 52

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. James Murdoch, Chandan Singh, Karl Kumber, and Reza Abbasi-Asi’s recent paper, “Definitions, methods, and applications in interpretable machine learning” Introduction.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

DataRobot Blog

FEBRUARY 28, 2023

Integrating different systems, data sources, and technologies within an ecosystem can be difficult and time-consuming, leading to inefficiencies, data silos, broken machine learning models, and locked ROI. Learn more about DataRobot hosted notebooks.

Data Processing 72

Data Processing Experimentation Machine Learning Modeling 72

DataRobot Blog

OCTOBER 18, 2022

As AI-driven use cases increase, the number of AI models deployed increases as well, leaving resource-strapped data science teams struggling to monitor and maintain this growing repository. “We These accelerators are specifically designed to help organizations accelerate from data to results. Monitoring with Machine Learning.

Big Data 96

Big Data Forecasting Experimentation Metrics 96

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. The post Data for Enterprise AI: at the very forefront of innovation appeared first on Cloudera Blog. But UOB didn’t stop there.

Enterprise 120

Enterprise Experimentation Uncertainty Deep Learning 120

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

The Unofficial Google Data Science Blog

APRIL 23, 2024

This blog post discusses such a comprehensive approach that is used at Youtube. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. And we can keep repeating this approach, relying on intuition and luck.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. 42% of data scientists are solo practitioners or on teams of five or fewer people. 42% of data scientists are solo practitioners or on teams of five or fewer people. Auto-scale compute.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

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

Domino Data Lab

JUNE 13, 2019

2018 , 2019 ], the rediscovery of the 50,000 lost MNIST test digits provides an opportunity to quantify the degradation of the official MNIST test set over a quarter-century of experimental research.” . “In the same spirit as [Recht et al., ” They also were able to.

Testing 83

Testing Data Science Experimentation Metadata 83

Domino Data Lab

APRIL 7, 2019

A good NLP library will, for example, correctly transform free text sentences into structured features (like cost per hour and is diabetic ), that easily feed into a machine learning (ML) or deep learning (DL) pipeline (like predict monthly cost and classify high risk patients ). Image Credit: Parsa Ghaffari on the Raylien Blog.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

Cloudera

APRIL 13, 2021

You’ve probably heard it more than once: Machine learning (ML) can take your digital transformation to another level. Unfortunately, most organizations run into trouble when it comes to bridging the gap that exists between experimentation and full-scale ML production. Still, at its core, ML is about science.

Machine Learning 97

Machine Learning Enterprise Experimentation Modeling 97

O'Reilly on Data

MAY 14, 2020

This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. 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

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.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

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. Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. .

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Testing Machine Learning Consulting Data Quality 300

Insight

MARCH 27, 2019

Discovering the proper method requires time-consuming experimentation. Even after many experiments, a machine learning (ML) engineer may still not discover the best option. DeepAugment: lightning fast autoML DeepAugment is designed as a fast and flexible autoML data augmentation solution. 2018 , blog ).

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

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

IBM Big Data Hub

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

Machine Learning 78

Machine Learning Manufacturing Cost-Benefit Deep Learning 78

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”.

Management 244

Management Metrics Machine Learning Modeling 244

Domino Data Lab

MAY 8, 2019

I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Machine learning model interpretability.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Cloudera

DECEMBER 13, 2022

Or better yet, “How do we empower people with enterprise data solutions that amplify positive outcomes in the security operations center?”. The world is awash in data. Operationalize ML with the Cloudera Data Platform. The post How Agencies Can Gain the Cyber Edge with Smart Data Solutions appeared first on Cloudera Blog.

Machine Learning 76

Machine Learning Experimentation Data Lake Data Processing 76

DataRobot Blog

JANUARY 12, 2023

By outsourcing the day-to-day management of the data science platform to the team who created the product, AI builders can see results quicker and meet market demands faster, and IT leaders can maintain rigorous security and data isolation requirements. Peace of Mind with Secure AI-Driven Data Science on Google Cloud.

Management 59

Management Data-driven ROI Cost-Benefit 59

The Unofficial Google Data Science Blog

JUNE 30, 2016

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects.

Statistics 44

Statistics Optimization Modeling Experimentation 44

DataRobot Blog

FEBRUARY 9, 2022

Machine learning operations (MLOps) solutions allow all models to be monitored from a central location, regardless of where they are hosted or deployed. Manual processes cannot keep up with the speed and scale of the machine learning lifecycle , as it evolves constantly. Implement MLOps Tools.

IT 52

IT Experimentation Machine Learning Data Processing 52

Domino Data Lab

FEBRUARY 12, 2020

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. this post on the Ray project blog ?. Ray is an open-source system for scaling Python applications from single machines to large clusters. for reinforcement learning (RL), ?

Experimentation 137

Experimentation Modeling Data Science Management 137

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. This blog post introduces the notions of representational uncertainty and interventional uncertainty to paint a fuller picture of what the practicing data scientist is up against. This blog post does not discuss objectives.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

There has been debate as to whether the term “data science” is necessary. Others argue that attaching the “science” is clear indication of a “wannabe” (think physics, chemistry, biology as opposed to computer science, social science and even creation science ). Some don’t see the point.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Domino Data Lab

AUGUST 26, 2019

This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. Introduction.

Testing 79

Testing Modeling Machine Learning Metrics 79

DataRobot Blog

MARCH 7, 2023

Organizations that want to prove the value of AI by developing, deploying, and managing machine learning models at scale can now do so quickly using the DataRobot AI Platform on Microsoft Azure. AI Platform Single-Tenant SaaS are fully managed by DataRobot and replace disparate machine learning tools, simplifying management.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

DataRobot

JUNE 2, 2021

They went on to say that investing in MLOps directly answers one of the biggest questions facing AI practitioners in the enterprise: how to move from experimentation to transformation. This blog post explores some of the most interesting findings in the report about the growing importance of MLOps.

Experimentation 52

Experimentation Machine Learning Reporting Modeling 52

DataRobot

JANUARY 20, 2022

As health and care delivery converges, analytical staff will be required to work across more boundaries with larger volumes of data than ever before. . Ultimately, this will free up and empower the analytical and data science health community resources to support the big clinical and operational change programmes required.

Data-driven 145

Data-driven Experimentation Modeling Predictive Modeling 145

datapine

JANUARY 31, 2022

It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214