Analytics Vidhya

NOVEMBER 23, 2020

Introduction If you want to know – How to use MACHINE LEARNING. ows) Visual Programming Software appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. The post Predicting Employee Attrition using Orange(.ows)

Visualization 334

Visualization Software Machine Learning Data Science 334

O'Reilly on Data

DECEMBER 12, 2019

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Random attacks can reveal all kinds of unexpected software and math bugs.

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

Analytics Vidhya

MARCH 15, 2024

Introduction Software development is on the brink of a transformative shift as artificial intelligence (AI) continues to push the boundaries of what was once deemed impossible. Enter Devin AI, an AI software engineer developed by the innovative minds at Cognition.

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Software Analytics Deep Learning Machine Learning 400

Analytics Vidhya

JUNE 12, 2023

From humble beginnings to influential […] The post The Journey of a Senior Data Scientist and Machine Learning Engineer at Spice Money appeared first on Analytics Vidhya. In this article, we explore Tajinder’s inspiring success story.

Machine Learning 306

Machine Learning Data Science Data-driven Analytics 306

Analytics Vidhya

AUGUST 22, 2024

(SAMPLE) How Generative AI is Revolutionizing Software Development? Introduction Welcome to the future of software development! Generative AI (GenAI) has evolved from a mere buzzword into a powerful force that is fundamentally changing how we create software. appeared first on Analytics Vidhya.

Software 306

Software Analytics Machine Learning Modeling 306

Analytics Vidhya

MAY 22, 2023

Introduction Join us in this interview as Sumeet shares his background, journey as a former Data Scientist to a software engineer, and learn the captivating aspects of his current job. He provides insights into the future of data science and software engineering and offers valuable advice for career transitioners.

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Software Data Science Analytics Deep Learning 65

CIO Business Intelligence

JANUARY 9, 2025

Generative artificial intelligence ( genAI ) and in particular large language models ( LLMs ) are changing the way companies develop and deliver software. The future will be characterized by more in-depth AI capabilities that are seamlessly woven into software products without being apparent to end users. An overview.

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Software Enterprise Key Performance Indicator Machine Learning 128

Analytics Vidhya

APRIL 24, 2023

Introduction We live in a world where social media platforms shape our interests, tailor our news feeds, and provide customized content, all thanks to machine learning! With machine learning (ML), a branch of artificial intelligence (AI), software programs can predict outcomes more accurately without being explicitly instructed.

Machine Learning 208

Machine Learning Software Analytics Technology 208

Analytics Vidhya

MARCH 8, 2024

The burgeoning AI industry not only promises to enhance existing technologies but also holds the potential to revolutionize previously unimaginable […] The post Transitioning from Software Developer to AI Engineer appeared first on Analytics Vidhya.

Software 291

Software Technology Analytics Machine Learning 291

David Menninger's Analyst Perspectives

OCTOBER 5, 2020

Ventana Research has been evaluating analytics and business intelligence (BI) software for a long time—almost 20 years. Our methodology for these assessments is referred to as a Value Index. We use weightings derived from our benchmark research about how you, as buyers of these technologies, value and evaluate vendors.

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Software Analytics Business Intelligence Technology 290

Analytics Vidhya

JUNE 1, 2021

The post How Open Source Beats Proprietary Software Helps for MLOps? ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction It’s necessary to keep your ML pipeline for compliance. appeared first on Analytics Vidhya.

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Software Data Science Publishing Analytics 305

Analytics Vidhya

APRIL 16, 2022

In a normal software engineering development cycle, you would now sit back and relax; however, in the machine learning development cycle, deployment to production is just about […]. Introduction Congratulations, you have deployed a model to production; it is an achievement for you and your team!

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Modeling Machine Learning Data Science Publishing 398

KDnuggets

DECEMBER 3, 2024

Check out these key concepts, tools, jargon, and tips for integrating ML models into existing software systems.

Machine Learning 128

Machine Learning Software Modeling 128

David Menninger's Analyst Perspectives

JULY 23, 2024

I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The AI Platforms: Ventana Research Buyers Guide is the distillation of a year of market and product research by Ventana Research.

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Software Marketing Machine Learning 275

Analytics Vidhya

JUNE 15, 2021

The post Applying Software Engineering Process for more effective Data Science Projects appeared first on Analytics Vidhya. ArticleVideo Book This article was published as a part of the Data Science Blogathon Hello World! An interesting title isn’t it? I thought the same.

Data Science 261

Data Science Software Publishing Analytics 261

Analytics Vidhya

MAY 25, 2023

Introduction If you are reading this blog, you might have been familiar with what Git is and how it has been an integral part of software development. Similarly, Data Version Control (DVC) is an open-source, Git-based version management for Machine Learning development that instills best practices across the teams.

Machine Learning 336

Machine Learning Software Management Analytics 336

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). Why AI software development is different. AI products are automated systems that collect and learn from data to make user-facing decisions.

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Management Machine Learning Experimentation Metrics 363

Analytics Vidhya

FEBRUARY 10, 2022

Introduction This article is part of an ongoing blog series on Machine Learning Operations(MLOps). We have seen differences in traditional software development in […]. This is the 2nd blog of the MLOps series. In the previous article, we have gone through the introduction of MLOps.

Modeling 335

Modeling Machine Learning Data Science Publishing 335

David Menninger's Analyst Perspectives

MAY 15, 2025

Founded as Software Development Laboratories in 1977, Oracle is a behemoth in the software industry, generating more than $50 billion in revenue in its fiscal year 2024. Originally focused solely on the relational database market, the software provider operated as Relational Systems, Inc.

Data Lake 130

Data Lake Data Warehouse Machine Learning Software 130

David Menninger's Analyst Perspectives

AUGUST 1, 2024

Having just completed our AI Platforms Buyers Guide assessment of 25 different software providers, I was surprised to see how few provided robust AI governance capabilities. As I’ve written previously , data governance has changed dramatically over the last decade, with nearly twice as many enterprises (71% v.

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Marketing Data Governance Machine Learning Software 289

Analytics Vidhya

JUNE 6, 2022

If we have to build any NLP-based software using Machine Learning or Deep Learning then we can use this pipeline. This article was published as a part of the Data Science Blogathon. Introduction Hello friends, In this article, we will discuss End to End NLP pipeline in an easy way.

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Deep Learning Machine Learning Data Science Publishing 343

Analytics Vidhya

NOVEMBER 5, 2023

From data access and manipulation to advanced machine learning techniques and seamless integration with Geographic Information System (GIS) software, Python is […] The post Top 50+ Geospatial Python Libraries appeared first on Analytics Vidhya.

Machine Learning 333

Machine Learning Software Management Analytics 333

O'Reilly on Data

JANUARY 17, 2023

What would you say is the job of a software developer? A layperson, an entry-level developer, or even someone who hires developers will tell you that job is to … well … write software. They’d say that the job involves writing some software, sure. But deep down it’s about the purpose of software. Pretty simple.

Machine Learning 363

Machine Learning Predictive Modeling Software Modeling 363

O'Reilly on Data

MAY 14, 2020

This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. Which stage is the product in currently? AI is no different. Tools like MLFlow are designed to help manage experimentation.

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Management Experimentation B2B Machine Learning 364

Analytics Vidhya

FEBRUARY 2, 2022

Table of contents Overview Traditional Software development Life Cycle Waterfall Model Agile Model DevOps Challenges in ML models Understanding MLOps Data Engineering Machine Learning DevOps Endnotes Overview: MLOps According to research by deeplearning.ai, only 2% of the companies using Machine Learning, Deep learning have […].

Machine Learning 271

Machine Learning Deep Learning Data Science Publishing 271

David Menninger's Analyst Perspectives

NOVEMBER 19, 2024

I previously explained that data observability software has become a critical component of data-driven decision-making. This has increased the focus on data observability software providers such as Bigeye and the role they play in ensuring that data meets quality and reliability requirements. 

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DataKitchen

APRIL 13, 2021

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. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. . Collaboration and Sharing.

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Testing Machine Learning Consulting Data Science 300

Analytics Vidhya

APRIL 17, 2023

It uses best practices of software engineering to build production-ready data science pipelines. The advantages of using Kedro are: Machine Learning Engineering: It borrows concepts from […] The post Walkthrough of Kedro Framework Using News Classification Task appeared first on Analytics Vidhya.

Data Science 291

Data Science Machine Learning Software Analytics 291

David Menninger's Analyst Perspectives

NOVEMBER 12, 2024

I recently attended Infor’s Velocity Summit , designed to showcase the latest versions of its CloudSuite ERP software. The company provides industry-specific enterprise software that enhances business performance and operational efficiency. This includes customer facing, financial, supply chain and workforce software.

Finance 130

Finance Prescriptive Analytics Cost-Benefit Manufacturing 130

O'Reilly on Data

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. All ML projects are software projects.

IT 364

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David Menninger's Analyst Perspectives

MAY 14, 2025

Our research illustrates a gap between awareness of the need for governance in AI initiatives and policies to govern AI and machine learning models. AI is increasingly incorporated into data quality software to automate and enhance data quality checks, supporting automation of data classification, metadata management and data lineage.

Data Governance 130

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David Menninger's Analyst Perspectives

JANUARY 15, 2025

I recently completed the latest edition of our Business Planning Buyers Guide, which reviews and assesses the offerings of 14 providers of this software. One of the points that I look at is whether and to what extent the software provider offers out-of-the-box external data useful for forecasting, planning, analysis and evaluation.

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Predictive Modeling Forecasting Predictive Analytics Statistics 130

O'Reilly on Data

MARCH 24, 2020

On the machine learning side, we are entering what Andrei Karpathy, director of AI at Tesla, dubs the Software 2.0 Before you even think about sophisticated modeling, state-of-the-art machine learning, and AI, you need to make sure your data is ready for analysis—this is the realm of data preparation.

Machine Learning 364

Machine Learning Statistics Data Quality Data Collection 364

David Menninger's Analyst Perspectives

APRIL 14, 2021

Organizations are becoming more and more data-driven and are looking for ways to accelerate the usage of artificial intelligence and machine learning (AI/ML). But there are many other steps in the process as well, such as coordinating the handoff between data scientists and IT or software engineers for deployment to production.

Machine Learning 264

Machine Learning Data-driven Software Modeling 264

O'Reilly on Data

MARCH 25, 2025

Most teams approach this like traditional software development but quickly discover it’s a fundamentally different beast. Check out the graph belowsee how excitement for traditional software builds steadily while GenAI starts with a flashy demo and then hits a wall of challenges? Whats worse: Inputs are rarely exactly the same.

Testing 174

Testing Data-driven Software Measurement 174

CIO Business Intelligence

OCTOBER 24, 2024

As many as 56% of IT workers 1 say the help desk ticket volume is up, according to a recent survey by software vendor Ivanti. The reasons include more software deployments, network reliability problems, security incidents/outages, and a rise in remote working. These technologies handle ticket classification, improving accuracy.

Machine Learning 110

Machine Learning Software Reporting Interactive 110

datapine

MAY 14, 2019

2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. Best for: This best data science book is especially effective for those looking to enter the data-driven machine learning and deep learning avenues of the field. 4) “Machine Learning Yearning” by Andrew Ng.

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Data Science Machine Learning Big Data Data-driven 350