Analytics Vidhya

MARCH 17, 2022

To Data Science Enthusiasts, We are happy to bring you another webinar into ‘The DataHour’ series. The webinar is based on building and operationalizing your ML Model using Tableau Business Science. The post Webinar: Build & Operationalize ML Model Using Tableau Business Science appeared first on Analytics Vidhya.

Modeling 350

Modeling Data Science Analytics Machine Learning 350

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. million on inference, grounding, and data integration for just proof-of-concept AI projects.

Modeling 116

Modeling Insurance Unstructured Data Experimentation 116

Analytics Vidhya

JANUARY 28, 2022

This article was published as a part of the Data Science Blogathon. Introduction A Machine Learning solution to an unambiguously defined business problem is developed by a Data Scientist ot ML Engineer. The post Deploying ML Models Using Kubernetes appeared first on Analytics Vidhya.

Modeling 346

Modeling Machine Learning Metrics Testing 346

O'Reilly on Data

JANUARY 17, 2023

Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? Building Models. A common task for a data scientist is to build a predictive model. You know the drill: pull some data, carve it up into features, feed it into one of scikit-learn’s various algorithms.

Machine Learning 363

Machine Learning Predictive Modeling Software Modeling 363

CIO Business Intelligence

NOVEMBER 27, 2024

From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.

Data Architecture 128

Data Architecture Cost-Benefit Machine Learning Experimentation 128

O'Reilly on Data

FEBRUARY 19, 2021

The data that powers ML applications is as important as code, making version control difficult; outputs are probabilistic rather than deterministic, making testing difficult; training a model is processor intensive and time consuming, making rapid build/deploy cycles difficult. A Wave of Cloud-Native, Distributed Data Frameworks.

Data Lake 362

Data Lake Machine Learning Data Warehouse Modeling 362

CIO Business Intelligence

NOVEMBER 5, 2024

The move relaxes Meta’s acceptable use policy restricting what others can do with the large language models it develops, and brings Llama ever so slightly closer to the generally accepted definition of open-source AI. As long as Meta keeps the training data confidential, CIOs need not be concerned about data privacy and security.

Modeling 126

Modeling Enterprise Technology Publishing 126

CIO Business Intelligence

OCTOBER 28, 2024

Whisper is not the only AI model that generates such errors. In a separate study, researchers found that AI models used to help programmers were also prone to hallucinations. This phenomenon, known as hallucination, has been documented across various AI models. With over 4.2

Risk 120

Risk Reporting Machine Learning Consulting 120

CIO Business Intelligence

JANUARY 2, 2025

Nearly nine out of 10 senior decision-makers said they have gen AI pilot fatigue and are shifting their investments to projects that will improve business performance, according to a recent survey from NTT DATA. You would build the POC, but the efficacy of the solution didnt necessarily pan out with the original hypothesis, he adds.

Experimentation 144

Experimentation Digital Transformation Technology Finance 144

Analytics Vidhya

NOVEMBER 23, 2022

This article was published as a part of the Data Science Blogathon. Introduction Web apps are the apps through which you can showcase your solution or approach to the public at a mass level. Creating the model is not enough until it’s in use by people. When it comes to delivering the solution then, everyone […].

Data Science 342

Data Science Publishing Modeling Analytics 342

Analytics Vidhya

OCTOBER 21, 2021

This article was published as a part of the Data Science Blogathon Overview Hadoop is widely used in the industry to examine large data volumes. Table of […]. The post A Comprehensive Guide to Apache Spark RDD and PySpark appeared first on Analytics Vidhya.

Data Science 349

Data Science Publishing Modeling Analytics 349

CIO Business Intelligence

OCTOBER 30, 2024

Whether it’s a financial services firm looking to build a personalized virtual assistant or an insurance company in need of ML models capable of identifying potential fraud, artificial intelligence (AI) is primed to transform nearly every industry. Building a strong, modern, foundation But what goes into a modern data architecture?

Enterprise 139

Enterprise Data Architecture Unstructured Data Insurance 139

CIO Business Intelligence

DECEMBER 12, 2024

Business leaders may be confident that their organizations data is ready for AI, but IT workers tell a much different story, with most spending hours each day massaging the data into shape. Theres a perspective that well just throw a bunch of data at the AI, and itll solve all of our problems, he says.

IT 134

IT Data Quality Experimentation Machine Learning 134

CIO Business Intelligence

JANUARY 15, 2025

According to research from NTT DATA , 90% of organisations acknowledge that outdated infrastructure severely curtails their capacity to integrate cutting-edge technologies, including GenAI, negatively impacts their business agility, and limits their ability to innovate. [1] The foundation of the solution is also important.

Cost-Benefit 125

Cost-Benefit Data-driven Enterprise Risk 125

CIO Business Intelligence

MARCH 25, 2025

These areas are considerable issues, but what about data, security, culture, and addressing areas where past shortcuts are fast becoming todays liabilities? Types of data debt include dark data, duplicate records, and data that hasnt been integrated with master data sources.

Risk 140

Risk Cost-Benefit Data-driven Digital Transformation 140

Analytics Vidhya

OCTOBER 27, 2021

This article was published as a part of the Data Science Blogathon Overview of Flask As a Data Science Enthusiast, Machine Learning Engineer, or data science practitioner it is not up to create a machine learning model for the specific problem but presenting your solution to the audience, to a client so they can give […].

Data Science 328

Data Science Machine Learning Publishing Modeling 328

Analytics Vidhya

FEBRUARY 1, 2024

Introduction In a groundbreaking move, Alibaba Cloud has introduced a serverless version of its Platform for AI-Elastic Algorithm Service (PAI-EAS) at the AI & Big Data Summit in Singapore.

Big Data 317

Big Data Enterprise Modeling Analytics 317

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. So we augment with open source, he says.

Cost-Benefit 115

Cost-Benefit Modeling Marketing Sales 115

CIO Business Intelligence

NOVEMBER 1, 2024

When considering how to work AI into your existing business practices and what solution to use, you must determine whether your goal is to develop, deploy, or consume AI technology. Developing AI When most people think about artificial intelligence, they likely imagine a coder hunched over their workstation developing AI models.

Recreation/Entertainment 120

Recreation/Entertainment Machine Learning Modeling Data Science 120

CIO Business Intelligence

DECEMBER 5, 2024

In today’s data-driven world, large enterprises are aware of the immense opportunities that data and analytics present. Yet, the true value of these initiatives is in their potential to revolutionize how data is managed and utilized across the enterprise. They had an AI model in place intended to improve fraud detection.

Enterprise 106

Enterprise Insurance Unstructured Data Business Intelligence 106

CIO Business Intelligence

JANUARY 8, 2025

The reasons include higher than expected costs, but also performance and latency issues; security, data privacy, and compliance concerns; and regional digital sovereignty regulations that affect where data can be located, transported, and processed. So we carefully manage our data lifecycle to minimize transfers between clouds.

Data Processing 140

Data Processing Optimization Modeling Enterprise 140

DataKitchen

APRIL 13, 2021

This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Companies that implement DataOps find that they are able to reduce cycle times from weeks (or months) to days, virtually eliminate data errors, increase collaboration, and dramatically improve productivity.

Testing 300

Testing Machine Learning Consulting Data Science 300

CIO Business Intelligence

NOVEMBER 19, 2024

The next phase of this transformation requires an intelligent data infrastructure that can bring AI closer to enterprise data. The challenges of integrating data with AI workflows When I speak with our customers, the challenges they talk about involve integrating their data and their enterprise AI workflows.

Management 116

Management Unstructured Data Deep Learning Metadata 116

O'Reilly on Data

AUGUST 18, 2020

So you need to redesign your company’s data infrastructure. Do you buy a solution from a big integration company like IBM, Cloudera, or Amazon? This article, which examines this shift in more depth, is an opinionated result of countless conversations with data scientists about their needs in modern data science workflows.

Data Science 364

Data Science Machine Learning Data Warehouse Deep Learning 364

CIO Business Intelligence

APRIL 24, 2025

The first wave of generative artificial intelligence (GenAI) solutions has already achieved considerable success in companies, particularly in the area of coding assistants and in increasing the efficiency of existing SaaS products. However, these applications only show a small glimpse of what is possible with large language models (LLMs).

Enterprise 118

Enterprise Sales Cost-Benefit B2B 118

CIO Business Intelligence

FEBRUARY 4, 2025

Instead of seeing digital as a new paradigm for our business, we over-indexed on digitizing legacy models and processes and modernizing our existing organization. This only fortified traditional models instead of breaking down the walls that separate people and work inside our organizations. And its testing us all over again.

Digital Transformation 144

Digital Transformation Sales Optimization Enterprise 144

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

Manufacturing Data-driven Marketing Testing 121

Analytics Vidhya

MARCH 21, 2025

Large language models answer questions using the knowledge they learned during training. Retrieval-Augmented Generation (RAG) helps by letting LLMs pull in external data, but even RAG needs help with complex questions. Adaptive RAG offers a solution. This fixed knowledge base limits them.

Modeling 140

Modeling Analytics IT 140

datapine

NOVEMBER 27, 2019

Data exploded and became big. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. 1) Data Quality Management (DQM). We all gained access to the cloud.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

CIO Business Intelligence

FEBRUARY 6, 2025

As someone deeply involved in shaping data strategy, governance and analytics for organizations, Im constantly working on everything from defining data vision to building high-performing data teams. My work centers around enabling businesses to leverage data for better decision-making and driving impactful change.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115