O'Reilly on Data

JULY 28, 2020

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 363

Marketing Experimentation Metrics Testing 363

CIO Business Intelligence

NOVEMBER 13, 2024

The new version, ARIS 10 SR27, available now, includes AI Companion, which a release stated contains capabilities such as the ability for users to query information stored in models within the ARIS repository without the need for an exact match on keywords, and can translate text-based descriptions into structured BPM models.

Experimentation 113

Experimentation Testing Software Risk 113

CIO Business Intelligence

JULY 18, 2024

The early bills for generative AI experimentation are coming in, and many CIOs are finding them more hefty than they’d like — some with only themselves to blame. According to IDC’s “ Generative AI Pricing Models: A Strategic Buying Guide ,” the pricing landscape for generative AI is complicated by “interdependencies across the tech stack.”

Cost-Benefit 136

Cost-Benefit Experimentation Consulting Modeling 136

IBM Big Data Hub

JUNE 10, 2024

As organizations strive to harness the power of AI while controlling costs, leveraging anything as a service (XaaS) models emerges as a strategic approach. Embracing the power of XaaS XaaS encompasses a broad spectrum of cloud-based and on-premises service models that offer scalable and cost-effective solutions to businesses.

Experimentation 62

Experimentation Optimization Risk Management Modeling 62

O'Reilly on Data

OCTOBER 19, 2021

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 352

IT Testing Experimentation Software 352

CIO Business Intelligence

MAY 23, 2022

Many companies whose AI model training infrastructure is not proximal to their data lake incur steeper costs as the data sets grow larger and AI models become more complex. The cloud is great for experimentation when data sets are smaller and model complexity is light. Potential headaches of DIY on-prem infrastructure.

Experimentation 137

Experimentation Cost-Benefit Data Lake Data Science 137

Cloudera

OCTOBER 8, 2024

This service supports a range of optimized AI models, enabling seamless and scalable AI inference. By 2023, the focus shifted towards experimentation. Enterprise developers began exploring proof of concepts (POCs) for generative AI applications, leveraging API services and open models such as Llama 2 and Mistral.

Optimization 75

Optimization Experimentation Metrics Modeling 75

DataRobot Blog

DECEMBER 6, 2022

Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results. Taking a Multi-Tiered Approach to Model Risk Management. Learn how to leverage Google BigQuery large datasets for large scale Time Series forecasting models in the DataRobot AI platform.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

CIO Business Intelligence

MAY 9, 2024

Experimentation drives momentum: How do we maximize the value of a given technology? Via experimentation. This can be as simple as a Google Sheet or sharing examples at weekly all-hands meetings Many enterprises do “blameless postmortems” to encourage experimentation without fear of making mistakes and reprisal.

Enterprise 121

Enterprise Experimentation Sales Manufacturing 121

CIO Business Intelligence

MAY 21, 2024

With the generative AI gold rush in full swing, some IT leaders are finding generative AI’s first-wave darlings — large language models (LLMs) — may not be up to snuff for their more promising use cases. With this model, patients get results almost 80% faster than before. It’s fabulous.”

IT 131

IT Cost-Benefit Experimentation Forecasting 131

CIO Business Intelligence

OCTOBER 24, 2024

By 2026, hyperscalers will have spent more on AI-optimized servers than they will have spent on any other server until then, Lovelock predicts. In some cases, the AI add-ons will be subscription models, like Microsoft Copilot, and sometimes, they will be free, like Salesforce Einstein, he says. growth in device spending.

IT 133

IT Data-driven Recreation/Entertainment Experimentation 133

CIO Business Intelligence

AUGUST 20, 2024

Our mental models of what constitutes a high-performance team have evolved considerably over the past five years. Post-pandemic, high-performance teams excelled at remote and hybrid working models, were more empathetic to individual needs, and leveraged automation to reduce manual work.

IT 141

IT Digital Transformation Experimentation Risk 141

CIO Business Intelligence

JULY 24, 2024

This dynamic framework offers CIOs a powerful tool to continually optimize their technology portfolios, ensuring their organizations remain agile, efficient, and future-ready. Key strategies for exploration: Experimentation: Conduct small-scale experiments. Use agile methodologies to implement updates and optimizations quickly.

Technology 98

Technology Experimentation Cost-Benefit ROI 98

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 Data Science 105

AWS Big Data

SEPTEMBER 5, 2024

In the context of Retrieval-Augmented Generation (RAG), knowledge retrieval plays a crucial role, because the effectiveness of retrieval directly impacts the maximum potential of large language model (LLM) generation. document-only) ~ 20%(bi-encoder) higher NDCG@10, comparable to the TAS-B dense vector model.

Metrics 100

Metrics Testing Experimentation Modeling 100

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.

Slice and Dice 52

Slice and Dice Modeling Metrics Experimentation 52

CIO Business Intelligence

JANUARY 19, 2024

The exam covers everything from fundamental to advanced data science concepts such as big data best practices, business strategies for data, building cross-organizational support, machine learning, natural language processing, scholastic modeling, and more. and SAS Text Analytics, Time Series, Experimentation, and Optimization.

Data Science 126

Data Science Machine Learning Predictive Modeling Experimentation 126

AWS Big Data

NOVEMBER 19, 2024

Customers maintain multiple MWAA environments to separate development stages, optimize resources, manage versions, enhance security, ensure redundancy, customize settings, improve scalability, and facilitate experimentation. micro, remember to monitor its performance using the recommended metrics to maintain optimal operation.

Metadata 75

Metadata Cost-Benefit Metrics Optimization 75

CIO Business Intelligence

OCTOBER 5, 2023

Sandeep Davé knows the value of experimentation as well as anyone. CBRE has also used AI to optimize portfolios for several clients, and recently launched a self-service generative AI product that enables employees to interact with CBRE and external data in a conversational manner. Let’s start with the models.

Experimentation 130

Experimentation Strategy Machine Learning Interactive 130

Cloudera

MARCH 2, 2022

You can read previous blog posts on Impala’s performance and querying techniques here – “ New Multithreading Model for Apache Impala ”, “ Keeping Small Queries Fast – Short query optimizations in Apache Impala ” and “ Faster Performance for Selective Queries ”. . Analytical SQL workloads use aggregates and joins heavily.

Data Warehouse 89

Data Warehouse Optimization Analytics Sales 89

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 84

Machine Learning Modeling Metadata Recreation/Entertainment 84

Occam's Razor

AUGUST 12, 2013

than multi-channel attribution modeling. By the time you are done with this post you'll have complete knowledge of what's ugly and bad when it comes to attribution modeling. You'll know how to use the good model, even if it is far from perfect. Multi-Channel Attribution Models. Linear Attribution Model.

Modeling 162

Modeling Optimization Marketing Interactive 162

CIO Business Intelligence

JULY 31, 2024

This year, however, Salesforce has accelerated its agenda, integrating much of its recent work with large language models (LLMs) and machine learning into a low-code tool called Einstein 1 Studio. Einstein 1 Studio is a set of low-code tools to create, customize, and embed AI models in Salesforce workflows. What is Einstein 1 Studio?

Data Lake 119

Data Lake IT Sales Experimentation 119

Domino Data Lab

SEPTEMBER 10, 2020

Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for models, not to mention many cycles of training, tuning, and tweaking models so they’re performant. This means many projects get stuck in endless research and experimentation.

Modeling 101

Modeling Data Science IT Metrics 101

CIO Business Intelligence

OCTOBER 14, 2024

Focused on digitization and innovation and closely aligned with lines of business, some 40% of IT leaders surveyed in CIO.com’s State of the CIO Study 2024 characterize themselves as transformational, while a quarter (23%) consider themselves functional: still optimizing, modernizing, and securing existing technology infrastructure.

Digital Transformation 131

Digital Transformation Experimentation Cost-Benefit Risk 131

CIO Business Intelligence

AUGUST 14, 2024

It’s embedded in the applications we use every day and the security model overall is pretty airtight. Microsoft has also made investments beyond OpenAI, for example in Mistral and Meta’s LLAMA models, in its own small language models like Phi, and by partnering with providers like Cohere, Hugging Face, and Nvidia. That’s risky.”

Enterprise 136

Enterprise Cost-Benefit Experimentation Modeling 136

CIO Business Intelligence

APRIL 24, 2024

But to find ways it can help grow a company’s bottom line, CIOs have to do more to understand a company’s business model and identify opportunities where gen AI can change the playing field. We’re mostly still optimizing our sales and marketing processes with CRM tools,” he says. And hyper-personalization at scale.”

Sales 128

Sales Finance Experimentation Cost-Benefit 128

CIO Business Intelligence

APRIL 25, 2023

A transformer is a type of AI deep learning model that was first introduced by Google in a research paper in 2017. Five years later, transformer architecture has evolved to create powerful models such as ChatGPT. Meanwhile, however, many other labs have been developing their own generative AI models.

Experimentation 116

Experimentation Deep Learning Enterprise Modeling 116

CIO Business Intelligence

NOVEMBER 7, 2022

A developing playbook of best practices for data science teams covers the development process and technologies for building and testing machine learning models. CIOs and CDOs should lead ModelOps and oversee the lifecycle Leaders can review and address issues if the data science teams struggle to develop models.

Machine Learning 128

Machine Learning Data Science Experimentation IoT 128

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. Optimize later. Step 4: Iterate quickly.

Machine Learning 98

Machine Learning Enterprise Experimentation Modeling 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

Experimentation Data-driven Risk Optimization 130

Cloudera

OCTOBER 4, 2023

In telecommunications, fast-moving data is essential when we’re looking to optimize the network, improving quality, user satisfaction, and overall efficiency. This also achieves workload isolation, so we can run mission critical workloads independent from experimental and exploratory ones and nobody steps on anyone’s toes by accident.

Data Warehouse 88

Data Warehouse Experimentation Cost-Benefit Dashboards 88

CIO Business Intelligence

JULY 3, 2023

But the rise of large language models (LLMs) is starting to make true knowledge management (KM) a reality. These models can extract meaning from digital data at scale and speed beyond the capabilities of human analysts. Data exists in ever larger silos, but real knowledge still resides in employees.

Management 105

Management Experimentation Modeling Enterprise 105

CIO Business Intelligence

DECEMBER 6, 2023

Laying the data foundation The four pillars of DS Smith’s digital plan revolved around the management and optimization of data generated by industrial machines, energy providers, its supply chain, and customer experience. Energy optimization is another key aspect of DS Smith’s data and sustainability pipeline, the CIO says.

Manufacturing 97

Manufacturing Data Lake Digital Transformation Machine Learning 97

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. Observe, optimize, and scale enterprise data pipelines. . ModelOp — Governs, monitors, and orchestrates models across the enterprise. Meta-Orchestration .

Testing 304

Testing Machine Learning Consulting Data Quality 304

CIO Business Intelligence

JUNE 14, 2023

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. They can also transform the data, create data models, visualize data, and share assets by using Power BI.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

CIO Business Intelligence

FEBRUARY 22, 2024

With the aim to accelerate innovation and transform its digital infrastructures and services, Ferrovial created its Digital Hub to serve as a meeting point where research and experimentation with digital strategies could, for example, provide new sources of income and improve company operations.

IT 105

IT Digital Transformation IoT Cost-Benefit 105