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.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

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. What’s the reality? We wanted to find out what people are actually doing, so in September we surveyed O’Reilly’s users.

Enterprise 344

Enterprise Testing Modeling Reporting 344

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.”. Proper AI product monitoring is essential to this outcome.

Management 319

Management Machine Learning Metrics Modeling 319

DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps is a term that refers to the set of practices and tools that organizations use to improve the quality and speed of data analytics and machine learning. This can help organizations to build trust in their data-related workflows, and to drive better outcomes from their data analytics and machine learning initiatives.

Machine Learning 130

Machine Learning Data-driven Optimization Data Analytics 130

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Meta-Orchestration .

Testing 304

Testing Machine Learning Consulting Data Science 304

datapine

NOVEMBER 27, 2019

Businesses of all sizes are no longer asking if they need increased access to business intelligence analytics but what is the best BI solution for their specific business. Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

AWS Big Data

DECEMBER 4, 2024

They’re taking data they’ve historically used for analytics or business reporting and putting it to work in machine learning (ML) models and AI-powered applications. For example, when a retail data analyst creates customer segmentation reports, those same datasets are now being used by AI teams to train recommendation engines.

Data Analytics 104

Data Analytics Analytics Data Lake Data Quality 104

Cloudera

DECEMBER 4, 2024

Today, Artificial Intelligence (AI) and Machine Learning (ML) are more crucial than ever for organizations to turn data into a competitive advantage. To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput.

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

DataKitchen

APRIL 26, 2021

In early April 2021, DataKItchen sat down with Jonathan Hodges, VP Data Management & Analytics, at Workiva ; Chuck Smith, VP of R&D Data Strategy at GlaxoSmithKline (GSK) ; and Chris Bergh, CEO and Head Chef at DataKitchen, to find out about their enterprise DataOps transformation journey, including key successes and lessons learned.

Measurement 246

Measurement Metrics Data-driven Dashboards 246

Cloudera

JANUARY 9, 2025

Thats why were moving from Cloudera Machine Learning to Cloudera AI. Why AI Matters More Than ML Machine learning (ML) is a crucial piece of the puzzle, but its just one piece. It means combining data engineering, model ops, governance, and collaboration in a single, streamlined environment.

Machine Learning 85

Machine Learning Enterprise Data-driven Modeling 85

Smart Data Collective

SEPTEMBER 24, 2021

Machine Learning is a branch of Artificial Intelligence that works by giving computers the ability to learn without being explicitly programmed. As technology advances, machine learning will have more opportunities to help businesses engage with their customers and improve the overall customer experience.

Machine Learning 140

Machine Learning Marketing Modeling Sales 140

DataRobot Blog

APRIL 21, 2022

Machine learning, and especially deep learning, has become increasingly more accurate in the past few years. This increase in accuracy is important to make AI applications good enough for production , but there has been an explosion in the size of these models. Why should you care?

Deep Learning 145

Deep Learning Machine Learning Metrics Measurement 145

Rocket-Powered Data Science

JULY 6, 2023

These AI applications are essentially deep machine learning models that are trained on hundreds of gigabytes of text and that can provide detailed, grammatically correct, and “mostly accurate” text responses to user inputs (questions, requests, or queries, which are called prompts). Guess what?

Statistics 152

Statistics Deep Learning Machine Learning Enterprise 152

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. What is RAPIDS. RAPIDS brings the power of GPU compute to standard Data Science operations, be it exploratory data analysis, feature engineering or model building.

Machine Learning 137

Machine Learning Data Science Data Lake Modeling 137

Rocket-Powered Data Science

JULY 19, 2023

The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage. What could be faster and easier than on-prem enterprise data sources? A similarly high percentage of tabular data usage among data scientists was mentioned here.

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

CIO Business Intelligence

SEPTEMBER 29, 2023

If any technology has captured the collective imagination in 2023, it’s generative AI — and businesses are beginning to ramp up hiring for what in some cases are very nascent gen AI skills, turning at times to contract workers to fill gaps, pursue pilots, and round out in-house AI project teams.

Deep Learning 143

Deep Learning Machine Learning Consulting Modeling 143

CIO Business Intelligence

APRIL 15, 2022

And 20% of IT leaders say machine learning/artificial intelligence will drive the most IT investment. Insights gained from analytics and actions driven by machine learning algorithms can give organizations a competitive advantage, but mistakes can be costly in terms of reputation, revenue, or even lives.

Analytics 145

Analytics Machine Learning Insurance Testing 145

datapine

MARCH 25, 2022

1) What Is Business Intelligence And Analytics? If someone puts you on the spot, could you tell him/her what the difference between business intelligence and analytics is? But let’s see in more detail what experts say and how can we connect and differentiate the both. What Do The Experts Say? Table of Contents.

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

Data Science 101

JANUARY 22, 2020

I have covered my experience and what topics are on the exam. Those blog posts were for the old exam which focused on the legacy Azure Machine Learning Studio interface and general data science knowledge. Microsoft has posted a new skills document, and they are planning to add new training for DP 100.

Data Science 115

Data Science Machine Learning Optimization Modeling 115

Cloudera

JANUARY 7, 2025

As enterprises navigate complex data-driven transformations, hybrid and multi-cloud models offer unmatched flexibility and resilience. Adopting hybrid and multi-cloud models provides enterprises with flexibility, cost optimization, and a way to avoid vendor lock-in. The terms hybrid and multi-cloud are often used interchangeably.

Cost-Benefit 82

Cost-Benefit Optimization Data-driven Strategy 82

Occam's Razor

MARCH 30, 2017

What the heck is Artificial Intelligence? Machine Learning | Marketing. Machine Learning | Analytics. It is actually smarter than what you see above. I mean, just imagine how hard it is to do what you see above, and everything I do is actually so much easier! AI | Now | Global Maxima.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

DataKitchen

NOVEMBER 20, 2021

What makes an effective DataOps Engineer? You might ask what that means. A DataOps Engineer shepherds process flows across complex corporate structures. Organizations have changed significantly over the last number of years and even more dramatically over the previous 12 months, with the sharp increase in remote work.

Testing 246

Testing Machine Learning Data Warehouse Analytics 246

Cloudera

FEBRUARY 11, 2022

Advances in the development and application of Machine Learning (ML) and Deep Learning (DL) algorithms, require greater care to ensure that the ethics embedded in previous rule-based systems are not lost. This blog post hopes to provide this foundational understanding. What is Machine Learning.

Machine Learning 97

Machine Learning Sales Data Science Deep Learning 97

Data Science 101

NOVEMBER 29, 2019

Luckily, Amazon has come through with a flurry of machine learning announcements. Amazon Athena and Aurora add support for ML in SQL Queries You can now invoke Machine Learning models right from your SQL Queries. Now the AutoML will provide details on all model run iterations. We will have to wait and see.

Data Science 115

Data Science Machine Learning Deep Learning Modeling 115

Domino Data Lab

FEBRUARY 12, 2020

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. and you’re wondering what it is, this post is for you. this post on the Ray project blog ?. for reinforcement learning (RL), ? Introduction. Ray: Scaling Python Applications.

Experimentation 137

Experimentation Modeling Data Science Machine Learning 137

datapine

JANUARY 31, 2022

Business intelligence can also be referred to as “descriptive analytics”, as it only shows past and current state: it doesn’t say what to do, but what is or was. What Are The Benefits of Business Intelligence? In order to do this, they first defined what data was the most relevant for the company. The power of knowledge.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Cloudera

NOVEMBER 17, 2021

Every AMP includes all the dependencies, industry best practices, prebuilt models, and a business-ready AI application — All deployable with a couple clicks, allowing Data Science teams to start a new project with a working example that they can then customize to their own needs in a fraction of the time.

Machine Learning 124

Machine Learning Visualization Data Science Dashboards 124

CIO Business Intelligence

JUNE 12, 2024

As part of this work, the foundation’s volunteers learned about the necessity of collecting reliable data to provide efficient healthcare activity. Some of the models are traditional machine learning (ML), and some, LaRovere says, are gen AI, including the new multi-modal advances. It’s not aggregated,” she says.

Modeling 140

Modeling Testing Cost-Benefit Metadata 140

DataKitchen

OCTOBER 28, 2021

What Is AI Bias? 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.

Testing 130

Testing IT Manufacturing Machine Learning 130

Domino Data Lab

AUGUST 9, 2021

For a model-driven enterprise, having access to the appropriate tools can mean the difference between operating at a loss with a string of late projects lingering ahead of you or exceeding productivity and profitability forecasts. What Are Modeling Tools? Importance of Modeling Tools. Types of Modeling Tools.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

Cloudera

JANUARY 20, 2021

to make a classification model based off of training data stored in both Cloudera’s Operational Database (powered by Apache HBase) and Apache HDFS. Afterwards, this model is then scored and served through a simple Web Application. Machine learning is now being used to solve many real-time problems.

Machine Learning 115

Machine Learning Data Science Modeling Big Data 115

erwin

DECEMBER 9, 2024

When it comes to using AI and machine learning across your organization, there are many good reasons to provide your data and analytics community with an intelligent data foundation. For instance, Large Language Models (LLMs) are known to ultimately perform better when data is structured. Lets give a for instance.

Metadata 52

Metadata Data Quality Sales Modeling 52

CIO Business Intelligence

SEPTEMBER 19, 2024

Goldcast, a software developer focused on video marketing, has experimented with a dozen open-source AI models to assist with various tasks, says Lauren Creedon, head of product at the company. The goal at Goldcast is to link all these AI models and turn them into agents that do their assigned tasks without human prompts, she says.

IT 138

IT Cost-Benefit Consulting Modeling 138

Domino Data Lab

MAY 15, 2019

It focuses on his ML product management insights and lessons learned. Machine Learning Projects are Hard: Shifting from a Deterministic Process to a Probabilistic One. Over the years, I have listened to data scientists and machine learning (ML) researchers relay various pain points and challenges that impede their work.

Machine Learning 111

Machine Learning Management Experimentation Data Science 111

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. We covered different ways of measuring model efficiency and showed ways to visualize this and select models based on it.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

Dataiku

MARCH 25, 2024

But instead, a machine seamlessly identifies the scene and its location, provides a detailed description, and even suggests nearby attractions. 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.

Visualization 132

Visualization Modeling Experimentation Testing 132