O'Reilly on Data

NOVEMBER 28, 2023

And everyone has opinions about how these language models and art generation programs are going to change the nature of work, usher in the singularity, or perhaps even doom the human race. 16% of respondents working with AI are using open source models. A few have even tried out Bard or Claude, or run LLaMA 1 on their laptop.

Enterprise 342

Enterprise Testing Modeling Reporting 342

O'Reilly on Data

MARCH 7, 2019

Language understanding benefits from every part of the fast-improving ABC of software: AI (freely available deep learning libraries like PyText and language models like BERT ), big data (Hadoop, Spark, and Spark NLP ), and cloud (GPU's on demand and NLP-as-a-service from all the major cloud providers). They don’t have a subject.

Deep Learning 279

Deep Learning Testing Machine Learning Modeling 279

Domino Data Lab

JANUARY 11, 2021

Even for experienced developers and data scientists, the process of developing a model could involve stringing together many steps from many packages, in ways that might not be as elegant or efficient as one might like. the experience is still rooted in the same goal: simple efficiency for the whole model development lifecycle.

Modeling 145

Modeling Metrics Data Science Testing 145

CIO Business Intelligence

DECEMBER 11, 2024

To solve the problem, the company turned to gen AI and decided to use both commercial and open source models. With security, many commercial providers use their customers data to train their models, says Ringdahl. Thats one of the catches of proprietary commercial models, he says. Its possible to opt-out, but there are caveats.

Cost-Benefit 115

Cost-Benefit Modeling Marketing Sales 115

O'Reilly on Data

JUNE 15, 2023

The world changed on November 30, 2022 as surely as it did on August 12, 1908 when the first Model T left the Ford assembly line. The creators of generative AI systems and Large Language Models already have tools for monitoring, modifying, and optimizing them.

Metrics 359

Metrics Reporting Measurement Finance 359

DataKitchen

JULY 12, 2023

The Syntax, Semantics, and Pragmatics Gap in Data Quality Validate Testing Data Teams often have too many things on their ‘to-do’ list. Syntax-Based Profiling and Testing : By profiling the columns of data in a table, you can look at values in a column to understand and craft rules about what is allowed for a column.

Data Quality 130

Data Quality Testing Manufacturing Finance 130

O'Reilly on Data

MARCH 23, 2023

It’s important to understand that ChatGPT is not actually a language model. It’s a convenient user interface built around one specific language model, GPT-3.5, is one of a class of language models that are sometimes called “large language models” (LLMs)—though that term isn’t very helpful. with specialized training.

IT 348

IT Modeling Testing Risk 348

CIO Business Intelligence

MARCH 19, 2025

Meanwhile, in December, OpenAIs new O3 model, an agentic model not yet available to the public, scored 72% on the same test. Were developing our own AI models customized to improve code understanding on rare platforms, he adds. SS&C uses Metas Llama as well as other models, says Halpin. Devin scored nearly 14%.

Software 143

Software Risk Enterprise Cost-Benefit 143

O'Reilly on Data

JUNE 14, 2024

Generative AI models are trained on large repositories of information and media. They are then able to take in prompts and produce outputs based on the statistical weights of the pretrained models of those corpora. In essence, the latest O’Reilly Answers release is an assembly line of LLM workers.

Metadata 298

Metadata Publishing Data-driven Modeling 298

DataKitchen

SEPTEMBER 20, 2021

The best way to ensure error-free execution of data production is through automated testing and monitoring. The DataKitchen Platform enables data teams to integrate testing and observability into data pipeline orchestrations. Automated tests work 24×7 to ensure that the results of each processing stage are accurate and correct.

Testing 246

Testing Metrics Measurement Dashboards 246

AWS Big Data

NOVEMBER 27, 2024

After the data is in Amazon Redshift, dbt models are used to transform the raw data into key metrics such as ticket trends, seller performance, and event popularity. Create dbt models in dbt Cloud. Deploy dbt models to Amazon Redshift. Choose Test Connection. Choose Next if the test succeeded.

Data Warehouse 94

Data Warehouse Analytics Testing Modeling 94

Rocket-Powered Data Science

JUNE 17, 2022

The dominant references everywhere to Observability was just the start of awesome brain food offered at Splunk’s.conf22 event. Reference ) The latest updates to the Splunk platform address the complexities of multi-cloud and hybrid environments, enabling cybersecurity and network big data functions (e.g., is here, now!

Machine Learning 238

Machine Learning Recreation/Entertainment Risk Business Objectives 238

O'Reilly on Data

JANUARY 17, 2023

” I, thankfully, learned this early in my career, at a time when I could still refer to myself as a software developer. Building Models. A common task for a data scientist is to build a predictive model. You might say that the outcome of this exercise is a performant predictive model. That’s sort of true.

Machine Learning 363

Machine Learning Predictive Modeling Software Modeling 363

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. have a large body of tools to choose from: IDEs, CI/CD tools, automated testing tools, and so on. We have great tools for working with code: creating it, managing it, testing it, and deploying it.

Machine Learning 279

Machine Learning Software Metadata Testing 279

CIO Business Intelligence

JANUARY 14, 2025

By articulating fitness functions automated tests tied to specific quality attributes like reliability, security or performance teams can visualize and measure system qualities that align with business goals. Experimentation: The innovation zone Progressive cities designate innovation districts where new ideas can be tested safely.

Enterprise 119

Enterprise Technology Metrics Measurement 119

O'Reilly on Data

SEPTEMBER 17, 2024

Chain-of-thought prompts often include some examples of problems, procedures, and solutions that are done correctly, giving the AI a model to emulate. Is every reference correct and—even more important—does it exist? Checking the AI is a strenuous test of your own knowledge. Is the AI’s output too vague or general to be useful?

Modeling 303

Modeling Reporting Sales Testing 303

David Menninger's Analyst Perspectives

SEPTEMBER 24, 2024

AI governance should address a number of issues, including data privacy, bias in data and models, drift in model accuracy, hallucinations and toxicity. Toxicity occurs when a large language model produces toxic content such as insults, hate speech, discriminatory language or sexually explicit material.

Testing 173

Testing Modeling Enterprise Risk 173

DataKitchen

NOVEMBER 20, 2021

A DataOps Engineer can make test data available on demand. We have automated testing and a system for exception reporting, where tests identify issues that need to be addressed. We often refer to data operations and analytics as a factory. It then autogenerates QC tests based on those rules.

Testing 246

Testing Machine Learning Data Warehouse Analytics 246

AWS Big Data

JANUARY 9, 2025

In this post, we use the term vanilla Parquet to refer to Parquet files stored directly in Amazon S3 and accessed through standard query engines like Apache Spark, without the additional features provided by table formats such as Iceberg. Also, the time travel feature can further mitigate any risks of lookahead bias.

Metadata 107

Metadata Snapshot Cost-Benefit Optimization 107

CIO Business Intelligence

DECEMBER 11, 2024

We are now deciphering rules from patterns in data, embedding business knowledge into ML models, and soon, AI agents will leverage this data to make decisions on behalf of companies. By modern, I refer to an engineering-driven methodology that fully capitalizes on automation and software engineering best practices.

Management 111

Management Data Governance Data Science Reporting 111

AWS Big Data

NOVEMBER 7, 2024

Custom context enhances the AI model’s understanding of your specific data model, business logic, and query patterns, allowing it to generate more relevant and accurate SQL recommendations. Your queries, data and database schemas are not used to train a generative AI foundational model (FM).

Metadata 97

Metadata Sales Data Warehouse Optimization 97

AWS Big Data

NOVEMBER 1, 2023

Amazon Redshift ML allows data analysts, developers, and data scientists to train machine learning (ML) models using SQL. In previous posts, we demonstrated how you can use the automatic model training capability of Redshift ML to train classification and regression models.

Modeling 111

Modeling Data Warehouse Machine Learning Recreation/Entertainment 111

AWS Big Data

NOVEMBER 26, 2024

For more examples and references to other posts, refer to the following GitHub repository. On a data platform, a data catalog stores table metadata and typically contains the data model and physical storage location of the datasets. This post is one of multiple posts about XTable on AWS. create_hudi_s3.py

Metadata 95

Metadata Data Lake Snapshot Data Warehouse 95

CIO Business Intelligence

APRIL 9, 2025

AI can reference previous grants, suggest improvements, and help researchers complete applications in a shorter period of time, she says. Right now, we support 55 large language models, says Gonick. Prasoles team ran a pilot gen AI admissions project, but testing immediately identified a problem.

Interactive 108

Interactive Technology Statistics Consulting 108

AWS Big Data

NOVEMBER 19, 2024

The process starts by creating a vector based on the question (embedding) by invoking the embedding model. Pre-filtered documents that relate to the user query are included in the prompt of the large language model (LLM) that summarizes the answer. Refer to Service Quotas for more details.

Management 94

Management Metadata Manufacturing Testing 94

Domino Data Lab

MARCH 16, 2021

We are at an interesting time in our industry when it comes to validating models – a crossroads of sorts when you think about it. There is an opportunity for practitioners and leaders to make a real difference by championing proper model validation. Three models were created. Image source: [link]. Image source: [link].

Modeling 122

Modeling Deep Learning Testing Optimization 122

AWS Big Data

DECEMBER 17, 2024

In internal tests, AI-driven scaling and optimizations showcased up to 10 times price-performance improvements for variable workloads. Lakehouse allows you to use preferred analytics engines and AI models of your choice with consistent governance across all your data.

Data Lake 82

Data Lake Data Warehouse Data-driven Optimization 82

CIO Business Intelligence

NOVEMBER 7, 2024

It covers essential topics like artificial intelligence, our use of data models, our approach to technical debt, and the modernization of legacy systems. Phase two focused on developing use cases, creating a backlog, exploring domains for resource allocation, and identifying the right subject matter experts for testing and experimentation.

Insurance 120

Insurance Experimentation Testing Technology 120

AWS Big Data

NOVEMBER 19, 2024

These organizations often maintain multiple AWS accounts for development, testing, and production stages, leading to increased complexity and cost. Additionally, customers adopting a federated deployment model find it challenging to provide isolated environments for different teams or departments, and at the same time optimize cost.

Metadata 106

Metadata Cost-Benefit Metrics Optimization 106

DataKitchen

AUGUST 19, 2021

Self-service data science teams may require their own segmentation models for building reports, views, and PowerPoints. For each domain, one would want to know that a build was completed, that tests were applied and passed, and that data flowing through the system is correct. The third set of domains are cached data sets (e.g.,

Data Warehouse 246

Data Warehouse Data Lake Manufacturing Testing 246

Smart Data Collective

JUNE 15, 2021

However, it is important to make sure that you understand the potential role of AI and what business model to build around it. However, even the most brilliant idea built around AI technology can fail without a proper business model. Without a good business model, you won’t understand customer needs and how to build your startup.

Modeling 116

Modeling Advertising Cost-Benefit Machine Learning 116

DataKitchen

SEPTEMBER 21, 2023

Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

DataKitchen

MAY 10, 2021

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. Since 2008, teams working for our founding team and our customers have delivered 100s of millions of data sets, dashboards, and models with almost no errors. Tie tests to alerts.

Testing 214

Testing Manufacturing Data Quality Statistics 214

DataKitchen

OCTOBER 28, 2021

Machine learning (ML) models are computer programs that draw inferences from data — usually lots of data. One way to think of ML models is that they instantiate an algorithm (a decision-making procedure often involving math) in software and then, at relatively low cost, deploy it on a large scale. What Is AI Bias?

Testing 130

Testing IT Manufacturing Machine Learning 130

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. This has serious implications for software testing, versioning, deployment, and other core development processes.

Management 363

Management Machine Learning Experimentation Metrics 363

AWS Big Data

OCTOBER 14, 2024

Large language model (LLM)-based generative AI is a new technology trend for comprehending a large corpora of information and assisting with complex tasks. Generative AI models can translate natural language questions into valid SQL queries, a capability known as text-to-SQL generation. Choose Manage model access.

Metadata 89

Metadata Data Lake Modeling Data Warehouse 89

IBM Big Data Hub

JANUARY 3, 2024

Most everyone has heard of large language models, or LLMs, since generative AI has entered our daily lexicon through its amazing text and image generating capabilities, and its promise as a revolution in how enterprises handle core business functions. Enter the world of Large Speech Models, or LSMs. But there’s more.

Modeling 99

Modeling Interactive Metrics Testing 99