Analytics Vidhya

JUNE 13, 2024

Introduction Google’s NotebookLM, an experimental AI-driven notebook, is designed to transform the way we interact with and utilize LLMs.

Experimentation 306

Experimentation Interactive Modeling Analytics 306

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

Analytics Vidhya

JULY 17, 2023

Google is unveiling its latest experimental offering from Google Labs: NotebookLM, previously known as Project Tailwind. This innovative notetaking software aims to revolutionize how we synthesize information by leveraging the power of language models.

Experimentation 269

Experimentation Software Modeling Analytics 269

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 350

IT Testing Experimentation Software 350

David Menninger's Analyst Perspectives

MAY 17, 2023

And recently, ChatGPT has raised awareness of AI and instigated research and experimentation into new ways in which AI can be applied. This perspective, the second in a series on generative AI, introduces some of the concepts behind ChatGPT, including large language models and transformers.

Modeling 147

Modeling Experimentation Data-driven Business Intelligence 147

Analytics Vidhya

NOVEMBER 11, 2024

Flax is an advanced neural network library built on top of JAX, aimed at giving researchers and developers a flexible, high-performance toolset for building complex machine learning models. This blog […] The post A Guide to Flax: Building Efficient Neural Networks with JAX appeared first on Analytics Vidhya.

Experimentation 222

Experimentation Machine Learning Modeling Analytics 222

CIO Business Intelligence

DECEMBER 19, 2024

Its been a year of intense experimentation. Now, the big question is: What will it take to move from experimentation to adoption? The key areas we see are having an enterprise AI strategy, a unified governance model and managing the technology costs associated with genAI to present a compelling business case to the executive team.

Experimentation 64

Experimentation Cost-Benefit Enterprise Strategy 64

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.,

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 338

Management Machine Learning Experimentation Metrics 338

CIO Business Intelligence

DECEMBER 10, 2024

Large language models (LLMs) just keep getting better. In just about two years since OpenAI jolted the news cycle with the introduction of ChatGPT, weve already seen the launch and subsequent upgrades of dozens of competing models. From Llama3.1 to Gemini to Claude3.5 From Llama3.1 to Gemini to Claude3.5

Modeling 115

Modeling Insurance Unstructured Data Experimentation 115

O'Reilly on Data

JANUARY 11, 2022

This trend started with the gigantic language model GPT-3. This may encourage the creation of more large-scale models; it might also drive a wedge between academic and industrial researchers. What does “reproducibility” mean if the model is so large that it’s impossible to reproduce experimental results?

Experimentation 303

Experimentation Modeling Strategy Machine Learning 303

O'Reilly on Data

SEPTEMBER 8, 2020

The programmer could then continue by filling in the actual code, possibly with extensive code completion (and yes, based on a model trained on all the code in GitHub or whatever). Most AI systems we’ve seen envision AI as an oracle: you give it the input, it pops out the answer.

Experimentation 303

Experimentation Interactive Modeling Software 303

O'Reilly on Data

JUNE 11, 2024

It is important to be careful when deploying an AI application, but it’s also important to realize that all AI is experimental. It would have been very difficult to develop the expertise to build and train a model, and much more effective to work with a company that already has that expertise. What are your specific use cases?

Modeling 265

Modeling Experimentation Interactive Data-driven 265

Cloudera

NOVEMBER 19, 2021

We recognise that experimentation is an important component of any enterprise machine learning practice. But, we also know that experimentation alone doesn’t yield business value. Organizations need to usher their ML models out of the lab (i.e., Organizations must think about an ML model in terms of its entire life cycle.

Machine Learning 127

Machine Learning Modeling Experimentation IT 127

KDnuggets

APRIL 14, 2023

AutoGPT is an experimental open-source pushing the capabilities of the GPT-4 language model. Just when we got our heads around ChatGPT, another one came along.

Experimentation 154

Experimentation Modeling 154

Analytics Vidhya

APRIL 14, 2023

Recently, researchers have shown that OpenAI’s generative AI model, GPT-4, has the capability to do scientific research all on its own! The model can design, […] The post GPT-4 Capable of Doing Autonomous Scientific Research appeared first on Analytics Vidhya.

Finance 319

Finance Modeling Analytics Experimentation 319

Rocket-Powered Data Science

JULY 6, 2021

MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. the monitoring of very important operational ML characteristics: data drift, concept drift, and model security).

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

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. ModelOps and MLOps fall under the umbrella of DataOps,with a specific focus on the automation of data science model development and deployment workflows.

Testing 312

Testing Machine Learning Consulting Data Quality 312

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

O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 315

Enterprise Deep Learning Data Governance Risk 315

O'Reilly on Data

APRIL 19, 2021

Relatively few respondents are using version control for data and models. Tools for versioning data and models are still immature, but they’re critical for making AI results reproducible and reliable. The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%).

Enterprise 358

Enterprise Risk Modeling Manufacturing 358

CIO Business Intelligence

DECEMBER 4, 2024

At AWS re:Invent Tuesday, JPMorgan Chase Global CIO Lori Beer detailed the evolving partnership between the financial services giant and AWS, which finds JPMorgan pushing the AWS SageMaker machine learning platform and AWS Bedrock generative AI platform beyond experimentation into production applications.

Experimentation 122

Experimentation Risk Marketing Enterprise 122

CIO Business Intelligence

NOVEMBER 27, 2024

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

Data Architecture 128

Data Architecture Cost-Benefit Machine Learning Experimentation 128

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.

Metrics 243

Metrics Experimentation Measurement Testing 243

Ontotext

JULY 24, 2023

I did some research because I wanted to create a basic framework on the intersection between large language models (LLM) and data management. LLM is by its very design a language model. The meaning of the data is the most important component – as the data models are on their way to becoming a commodity.

Modeling 98

Modeling Management Structured Data Data Architecture 98

O'Reilly on Data

SEPTEMBER 15, 2020

In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time. Outputs from trained AI models include numbers (continuous or discrete), categories or classes (e.g., spam or not-spam), probabilities, groups/segments, or a sequence (e.g.,

Advertising 299

Advertising Cost-Benefit ROI Machine Learning 299

O'Reilly on Data

MARCH 19, 2020

The chatbot was one of the first applications of AI in experimental and production usage. Does the growing engagement in neural networks, reinforcement learning, unsupervised learning, and the increased focus on putting models into production augur a shift in how practitioners in the space frame what they do? What’s in a name?

Deep Learning 280

Deep Learning Machine Learning Data-driven Modeling 280

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

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. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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

IBM Big Data Hub

MAY 17, 2024

Cloud maturity models are a useful tool for addressing these concerns, grounding organizational cloud strategy and proceeding confidently in cloud adoption with a plan. Cloud maturity models (or CMMs) are frameworks for evaluating an organization’s cloud adoption readiness on both a macro and individual service level.

Modeling 96

Modeling Cost-Benefit Optimization Digital Transformation 96

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 short, providers must demonstrate that their models are safe and effective.

Modeling 95

Modeling Testing Experimentation Risk Management 95

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.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

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 250

Management Metrics Machine Learning Modeling 250

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.

Data Science 121

Data Science Enterprise Experimentation Machine Learning 121

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 90

Machine Learning Modeling Metadata Recreation/Entertainment 90

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