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. However, the concept is quite abstract. Can’t we just fold it into existing DevOps best practices? Why: Data Makes It Different.

IT 364

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Analytics Vidhya

AUGUST 29, 2024

Introduction Hallucination in large language models (LLMs) refers to the generation of information that is factually incorrect, misleading, or fabricated. What […] The post Test – Blogathon appeared first on Analytics Vidhya.

Testing 219

Testing Modeling Analytics IT 219

Analytics Vidhya

SEPTEMBER 28, 2022

First, you build software, test it for possible faults, and finally deploy it for the end user’s accessibility. The post Automate Model Deployment with GitHub Actions and AWS appeared first on Analytics Vidhya. The same can be applied to […].

Modeling 337

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O'Reilly on Data

JANUARY 9, 2024

Specifically, could ChatGPT N (for large N) quit the game of generating code in a high-level language like Python, and produce executable machine code directly, like compilers do today? It’s not really an academic question. That will be a big change for professional programmers—though writing code is a small part of what programmers actually do.

Modeling 345

Modeling Software Testing Optimization 345

Analytics Vidhya

DECEMBER 10, 2024

Anthropic’s latest update introduces this cool capability to their AI model, Claude. Its in beta testing, but its already shaking up how AI can interact with software. Theyre […] The post Anthropic Computer Use: AI Assitant Taking Over Your Computer appeared first on Analytics Vidhya.

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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. It has helped to write a book.

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Analytics Vidhya

JUNE 29, 2022

Source: Wikipedia In this article, we shall provide some background on how multilingual multi-speaker models work and test an Indic TTS model that supports 9 languages and 17 speakers (Hindi, Malayalam, Manipuri, Bengali, Rajasthani, Tamil, Telugu, Gujarati, Kannada). It seems a bit counter-intuitive […].

Modeling 269

Modeling Testing Data Science Publishing 269

CIO Business Intelligence

NOVEMBER 13, 2024

One is going through the big areas where we have operational services and look at every process to be optimized using artificial intelligence and large language models. But a substantial 23% of respondents say the AI has underperformed expectations as models can prove to be unreliable and projects fail to scale.

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O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

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Analytics Vidhya

OCTOBER 26, 2021

The modus operandi of this algorithm is that the training examples are being stored and when the test […]. The post kNN Algorithm – An Instance-based ML Model to Predict Heart Disease appeared first on Analytics Vidhya. It is a way of solving tasks of approximating real or discrete-valued target functions.

Modeling 291

Modeling Machine Learning Testing Data Science 291

O'Reilly on Data

MARCH 25, 2025

Weve seen this across dozens of companies, and the teams that break out of this trap all adopt some version of Evaluation-Driven Development (EDD), where testing, monitoring, and evaluation drive every decision from the start. The truth is, we’re in the earliest days of understanding how to build robust LLM applications. The way out?

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Testing Data-driven Software Measurement 168

Analytics Vidhya

MARCH 24, 2023

People across various walks of life have been experimenting diversely with the AI to test its knowledge and gauge its capabilities. In one such attempt, LinkedIn Co-Founder, Reid Hoffman, has published an entire book with the help of GPT-4. Review & Summary appeared first on Analytics Vidhya.

IT 315

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Analytics Vidhya

MAY 9, 2024

Introduction Cross-validation is a machine learning technique that evaluates a model’s performance on a new dataset. It involves dividing a training dataset into multiple subsets and testing it on a new set. This prevents overfitting by encouraging the model to learn underlying trends associated with the data.

Machine Learning 319

Machine Learning Testing Modeling Analytics 319

CIO Business Intelligence

FEBRUARY 26, 2025

AI agents are powered by gen AI models but, unlike chatbots, they can handle more complex tasks, work autonomously, and be combined with other AI agents into agentic systems capable of tackling entire workflows, replacing employees or addressing high-level business goals. Not all of that is gen AI, though.

IT 141

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Analytics Vidhya

FEBRUARY 25, 2023

Introduction Often while working on predictive modeling, it is a common observation that most of the time model has good accuracy for the training data and lesser accuracy for the test data.

Predictive Modeling 313

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CIO Business Intelligence

NOVEMBER 22, 2024

Guan, along with AI leaders from S&P Global and Corning, discussed the gargantuan challenges involved in moving gen AI models from proof of concept to production, as well as the foundation needed to make gen AI models truly valuable for the business. MIT event, moderated by Lan Guan, CAIO at Accenture.

Unstructured Data 136

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O'Reilly on Data

NOVEMBER 12, 2024

Models using computers Anthropic’s computer use API is now available in beta. I did a simple experiment: I pointed it at two of my recent posts, “ Think Better ” and “ Henry Ford Does AI.” But what blew me away was the podcast it generated: an eight-minute discussion between two synthetic people who sounded interested and engaged.

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CIO Business Intelligence

NOVEMBER 19, 2024

From obscurity to ubiquity, the rise of large language models (LLMs) is a testament to rapid technological advancement. Just a few short years ago, models like GPT-1 (2018) and GPT-2 (2019) barely registered a blip on anyone’s tech radar. Agents will play different roles as part of a complex workflow, automating tasks more efficiently.

Testing 135

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CIO Business Intelligence

OCTOBER 17, 2024

This agentic approach to creation and validation is especially useful for people who are already taking a test-driven development approach to writing software,” Davis says. With existing, human-written tests you just loop through generated code, feeding the errors back in, until you get to a success state.”

Software 141

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Analytics Vidhya

DECEMBER 1, 2023

This powerhouse of a language model not only possesses advanced linguistic capabilities but also boasts a groundbreaking vision component. Introduction In the ever-evolving landscape of AI, OpenAI introduces its most remarkable creation yet – ChatGPT-4.

Testing 367

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Analytics Vidhya

FEBRUARY 3, 2024

The tech giant is testing a feature that leverages large language models (LLM) to provide detailed search results, catering to individual preferences and enhancing the overall user experience. Google Maps is set to revolutionize the way users explore and discover new places through its latest experiment with generative AI.

Testing 319

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CIO Business Intelligence

MARCH 25, 2025

Using the companys data in LLMs, AI agents, or other generative AI models creates more risk. Build up: Databases that have grown in size, complexity, and usage build up the need to rearchitect the model and architecture to support that growth over time.

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DataKitchen

APRIL 13, 2021

Testing and Data Observability. DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Testing and Data Observability.

Testing 300

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CIO Business Intelligence

DECEMBER 30, 2024

Small language models and edge computing Most of the attention this year and last has been on the big language models specifically on ChatGPT in its various permutations, as well as competitors like Anthropics Claude and Metas Llama models. Reasoning also helps us use AI as more of a decision support system, he adds.

ROI 141

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O'Reilly on Data

OCTOBER 12, 2021

Kevlin Henney and I were riffing on some ideas about GitHub Copilot , the tool for automatically generating code base on GPT-3’s language model, trained on the body of code that’s in GitHub. We know how to test whether or not code is correct (at least up to a certain limit). First, we wondered about code quality.

Testing 363

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Analytics Vidhya

DECEMBER 20, 2023

Introduction Google has become the center of attention since the announcement of its new Generative AI family of models called the Gemini. As Google has stated, Google’s Gemini family of Large Language Models outperforms the existing State of The Art(SoTA) GPT model from OpenAI in more than 30+ benchmark tests.

Testing 306

Testing Modeling Analytics IT 306

O'Reilly on Data

APRIL 25, 2023

There’s a lot of excitement about how the GPT models and their successors will change programming. Many of the prompts are about testing: ChatGPT is instructed to generate tests for each function that it generates. At least in theory, test driven development (TDD) is widely practiced among professional programmers.

Testing 364

Testing Software Strategy Enterprise 364

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. The next evolution of AI has arrived, and its agentic. The technology is relatively new, but all the major players are already on board. So its not just about the use case, but about having the guardrails.

Software 143

Software Risk Enterprise Cost-Benefit 143

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 339

Enterprise Testing Modeling Reporting 339

CIO Business Intelligence

APRIL 8, 2025

In many cases, companies should opt for closed, proprietary AI models that arent connected to the internet, ensuring that critical data remains secure within the enterprise. Yet failing to successfully address risk with an effective risk management program is courting disaster.

Risk Management 131

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CIO Business Intelligence

NOVEMBER 27, 2024

Data quality for AI needs to cover bias detection, infringement prevention, skew detection in data for model features, and noise detection. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects. It can end up, at best, wasting a lot of time and effort.

Enterprise 141

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O'Reilly on Data

JUNE 18, 2024

While RAG is conceptually simple—look up relevant documents and construct a prompt that tells the model to build its response from them—in practice, it’s more complex. Including all those results in a RAG query would be impossible with most language models, and impractical with the few that allow large context windows.

Testing 312

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O'Reilly on Data

SEPTEMBER 19, 2023

Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. While data scientists were no longer handling Hadoop-sized workloads, they were trying to build predictive models on a different kind of “large” dataset: so-called “unstructured data.” Cloud computing? And Hadoop rolled in.

Machine Learning 336

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CIO Business Intelligence

NOVEMBER 28, 2024

Uber no longer offers just rides and deliveries: It’s created a new division hiring out gig workers to help enterprises with some of their AI model development work. Data labeling in particular is a growing market, as companies rely on humans to check out data used to train AI models.

Manufacturing 121

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O'Reilly on Data

MARCH 14, 2023

It’s easy to ask it questions, but we all know that these large language models frequently generate false answers. ChatGPT gave me a bunch of Python code that implemented the Miller-Rabin primality test, and said that my number was divisible by 29. It was a brute-force primality test that tried each integer (both odd and even!)

Testing 312

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O'Reilly on Data

FEBRUARY 16, 2023

That’s what beta tests are for. Large language models like ChatGPT and Google’s LaMDA aren’t designed to give correct results. The ability of these models to “make up” stuff is interesting, and as I’ve suggested elsewhere , might give us a glimpse of artificial imagination.

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