Rocket-Powered Data Science

FEBRUARY 15, 2023

While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities. So, if you have 1 trillion data points (g.,

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Strategy Experimentation Uncertainty Machine Learning 290

CIO Business Intelligence

JULY 11, 2023

In recent years, we have witnessed a tidal wave of progress and excitement around large language models (LLMs) such as ChatGPT and GPT-4. In this blog post, we will delve deeper into each of these principles and provide concrete examples to illustrate their importance.

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Modeling Testing Experimentation Risk Management 66

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

DataRobot Blog

MARCH 16, 2023

release, we’ve made it easy for you to rapidly prepare data, engineer new features and subsequently automate model deployment and monitoring into your Snowflake data landscape, all with limited data movement. We’ve tightened the loop between ML data prep , experimentation and testing all the way through to putting models into production.

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Modeling Experimentation Machine Learning Data-driven 52

DataRobot Blog

MARCH 8, 2023

Leveraging DataRobot’s JDBC connectors, enterprise teams can work together to train ML models on their data residing in SAP HANA Cloud and SAP Data Warehouse Cloud, as well as have an option to enrich it with data from external data sources.

Enterprise 105

Enterprise Experimentation Machine Learning Modeling 105

DataRobot Blog

SEPTEMBER 30, 2022

How do you track the integrity of a machine learning model in production? Model Observability can help. By tracking service, drift, prediction data, training data, and custom metrics, you can keep your models and predictions relevant in a fast-changing world. Model Observability Features.

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DataRobot Blog

MARCH 16, 2023

Why model-driven AI falls short of delivering value Teams that just focus model performance using model-centric and data-centric ML risk missing the big picture business context. We are also thrilled to share the innovations and capabilities that we have developed at DataRobot to meet and exceed those requirements.

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Experimentation Data-driven Modeling Metadata 98

Domino Data Lab

NOVEMBER 14, 2019

Model interpretability continues to spark public discourse among industry. We have covered model interpretability previously, including a proposed definition of machine learning (ML) interpretability. Yet there are tradeoffs to consider when selecting a model. Errors like these may occur when the model is being constructed.

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Modeling Machine Learning Data Science Experimentation 71

Cloudera

JULY 27, 2021

Here in the virtual Fast Forward Lab at Cloudera , we do a lot of experimentation to support our applied machine learning research, and Cloudera Machine Learning product development. Only through hands-on experimentation can we discern truly useful new algorithmic capabilities from hype.

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Dataiku

MARCH 25, 2024

This scenario is not science fiction but a glimpse into the capabilities of Multimodal Large Language Models (M-LLMs), where the convergence of various modalities extends the landscape of AI. But instead, a machine seamlessly identifies the scene and its location, provides a detailed description, and even suggests nearby attractions.

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Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

Domino Data Lab

MAY 15, 2019

Unfortunately, a common challenge that many industry people face includes battling “ the model myth ,” or the perception that because their work includes code and data, their work “should” be treated like software engineering. These steps also reflect the experimental nature of ML product management.

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Machine Learning Management Experimentation Uncertainty 111

IBM Big Data Hub

MARCH 27, 2024

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Figure 1 illustrates the framework for Scope 3 emission estimation employing a large language model.

Experimentation 75

Experimentation Modeling Deep Learning Data-driven 75

The Unofficial Google Data Science Blog

JULY 22, 2020

Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.

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Cloudera

APRIL 13, 2021

Unfortunately, most organizations run into trouble when it comes to bridging the gap that exists between experimentation and full-scale ML production. Proper science takes experimentation and observation, as well as a willingness to accept the failures alongside the successes. Step 7: Maintain the integrity of your models.

Machine Learning 98

Machine Learning Enterprise Experimentation Modeling 98

Cloudera

OCTOBER 4, 2023

On top of these core critical capabilities, we also need the following: Petabyte and larger scalability — particularly valuable in predictive analytics use cases where high granularity and deep histories are essential to training AI models to greater precision. appeared first on Cloudera Blog.

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Data Warehouse Experimentation Cost-Benefit Dashboards 87

IBM Big Data Hub

MAY 20, 2024

A classical computer is how you’re reading this blog. As algorithm discovery and development matures and we expand our focus to real-world applications, commercial entities, too, are shifting from experimental proof-of-concepts toward utility-scale prototypes that will be integrated into their workflows.

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IBM Big Data Hub

DECEMBER 4, 2023

And the abundance of data available for training models has opened up vast possibilities for experimentation and learning. Evaluating the performance of generative AI models ensures that they meet desired standards and can provide reliable outputs.

Experimentation 64

Experimentation Modeling Metrics Machine Learning 64

Domino Data Lab

OCTOBER 14, 2020

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. For example, our models can show farmers how to increase their production while using less fertilizer.

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IBM Big Data Hub

DECEMBER 15, 2023

When AI algorithms, pre-trained models, and data sets are available for public use and experimentation, creative AI applications emerge as a community of volunteer enthusiasts builds upon existing work and accelerates the development of practical AI solutions.

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Deep Learning Experimentation Machine Learning Risk 66

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. Datatron — Automates deployment and monitoring of AI models.

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IBM Big Data Hub

MAY 25, 2023

The Center of Excellence (CoE) already has more than 1,000 consultants with specialized generative AI expertise that are engaging with a global set of clients to drive productivity in IT operations and core business processes like HR or marketing, elevate their customer experiences and create new business models.

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Consulting Experimentation Machine Learning Modeling 101

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.

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Modeling Experimentation Knowledge Discovery Testing 61

Cloudera

APRIL 9, 2021

This is part 4 in this blog series. 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 second blog dealt with creating and managing Data Enrichment pipelines. Here are the key stages: .

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

DataRobot Blog

NOVEMBER 15, 2022

A packed keynote session showed how repeatable workflows and flexible technology get more models into production. Our in-booth theater attracted a crowd in Singapore with practical workshops, including Using AI & Time Series Models to Improve Demand Forecasting and a technical demonstration of the DataRobot AI Cloud platform.

Big Data 52

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Cloudera

DECEMBER 13, 2022

Workflows become so cumbersome that projects never make it past pilot and most importantly, data scientists’ ML models rarely emerge from experimentation to operation. . To learn more, read our technical brief: Securely Train and Deploy Machine Learning Models in Production with Cloudera Data Platform.

Machine Learning 78

Machine Learning Experimentation Data Lake Data Processing 78

IBM Big Data Hub

DECEMBER 5, 2023

With the rise of highly personalized online shopping, direct-to-consumer models, and delivery services, generative AI can help retailers further unlock a host of benefits that can improve customer care, talent transformation and the performance of their applications. The impact of these investments will become evident in the coming years.

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Unstructured Data Cost-Benefit Experimentation Machine Learning 91

Cloudera

MAY 31, 2023

Riding the wave of the generative AI revolution, third party large language model (LLM) services like ChatGPT and Bard have swiftly emerged as the talk of the town, converting AI skeptics to evangelists and transforming the way we interact with technology. Adding Enterprise Context to LLMs Contextual Limitation is not unique to enterprises.

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IBM Big Data Hub

MAY 9, 2023

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation to become business critical for many organizations. Success in delivering scalable enterprise AI necessitates the use of tools and processes that are specifically made for building, deploying, monitoring and retraining AI models.

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DataRobot

JANUARY 20, 2022

. — Collaborating via Snowflake Data Cloud and DataRobot AI Cloud Platform will enable multiple organizations to build a community movement where experimentation, innovation, and creativity flourish. As ICSs mature digitally, there is a need to ensure that all processes, datasets, and models are transparent and are free from bias. –

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Data-driven Experimentation Modeling Predictive Modeling 145

IBM Big Data Hub

OCTOBER 4, 2022

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation. Multiple unsupported tools for building and deploying models. Consistent principles guiding the design, development, deployment and monitoring of models are critical in driving responsible, trustworthy AI.

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Metadata Risk Risk Management Experimentation 98

DataKitchen

DECEMBER 1, 2022

We want them to be able to focus on the things that move the needle, like getting models in production, understanding their customers better, or making decisions faster. At DataKitchen, we’re trying to give people the tools and best practices to help them succeed with data and keep their job enjoyable and rewarding.

Experimentation 130

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IBM Big Data Hub

MARCH 4, 2024

Figure 2: ROI potential by transforming into an AI+ enterprise Organizations with high data maturity that embed an AI+ transformation model into the enterprise fabric and culture can generate up to 2.6 Consider the following: Do you need a public foundation model? This culture encourages experimentation and expertise growth.

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Enterprise ROI Risk Strategy 99

DataKitchen

JANUARY 20, 2021

The center of excellence (COE) model leverages the DataOps team to solve real-world challenges. A COE typically has a full-time staff that focuses on delivering value for customers in an experimentation-driven, iterative, result-oriented, customer-focused way. DataOps Center of Excellence.

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Metrics Experimentation Measurement Testing 243

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

IBM Big Data Hub

FEBRUARY 1, 2024

Today, more than 130 central banks are actively exploring CBDCs and publishing periodic reports on the functional and non-functional requirements of CBDC platforms, including the evolving architectural considerations and the outcomes of their various CBDC experimentations.

Experimentation 93

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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. Models trained in DataRobot can also be easily deployed to Azure Machine Learning, allowing users to host models easier in a secure way.

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