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One of the fastest-growing areas of technology is machine learning, but even seasoned professionals occasionally stumble over new terms and jargon. It is simple to get overwhelmed by the plethora of technical terms as research speeds up and new architectures, loss functions, and optimisation techniques appear. This blog article is your carefully chosen reference to […] The post 50+ Must-Know Machine Learning Terms You (Probably) Haven’t Heard Of appeared first on Analytics Vidhya.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 5 Routine Tasks That ChatGPT Can Handle for Data Scientists A practical walkthrough of how ChatGPT handles cleaning, exploration, visualization, modeling and more.
The Critical Distinction Your Organization Can't Afford to Miss As an independent AI and Business Intelligence consultant who has worked with organizations across healthcare, hospitality, agriculture, and marketing, I've observed a clear pattern. Companies that focus on developing data fluency achieve significantly better results with analytics, digital transformation, and AI adoption.
Two vendor names keep popping up when CIOs describe how they launched successful AI projects. The two companies, Databricks and Snowflake, started from different market positions and technical perspectives, with Databricks focused more on unstructured data processing and real-time analytics, while Snowflake has concentrated on abstracting and simplifying data warehousing in the cloud.
Speaker: Dylan Secrest, Founder of Alamo Innovation and Construction Digital Transformation Consultant
Construction payment workflows are notoriously complex when you consider juggling multiple stakeholders, compliance requirements, and evolving project scopes. Delays in approvals or misaligned data between budgets, lien waivers, and pay applications can grind progress to a halt. The good news? It doesn't have to be this way! Join expert Dylan Secrest to discover how leading contractors are turning payment chaos into clarity using digital workflows, integrated systems, and automation strategies.
Enterprise teams have moved past the hype of large language models. The focus now is execution. How to apply these tools in complex environments like compliance, finance, and risk without creating new vulnerabilities. In these areas, accuracy and accountability aren’t nice-to-haves; they’re essential. Policymakers and researchers are already laying the groundwork.
Organizations must often deal with a vast array of data formats and sources in their data analytics workloads. This range of data types, such as structured relational data, semi-structured formats like JSON and XML and even binary formats like Protobuf and Avro, has presented new challenges for companies looking to extract valuable insights. Protocol Buffers (protobuf) has gained significant traction in industries that require efficient data serialization and transmission, particularly in stream
Organizations must often deal with a vast array of data formats and sources in their data analytics workloads. This range of data types, such as structured relational data, semi-structured formats like JSON and XML and even binary formats like Protobuf and Avro, has presented new challenges for companies looking to extract valuable insights. Protocol Buffers (protobuf) has gained significant traction in industries that require efficient data serialization and transmission, particularly in stream
The Harsh Reality of Data Governance 💥 80% of data governance initiatives fail. Not because of tools. Not because of frameworks. But because the business isn’t involved, and no one agrees on what data truly matters. That’s where Critical Data Elements (CDEs) change everything. They force a conversation between IT and business about what matters.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 10 Python Libraries Every MLOps Engineer Should Know Learn about 10 essential Python libraries that support core MLOps tasks like versioning, deployment, and monitoring.
CIOs for the most part have had to ditch multiyear roadmaps, knowing that the rapid pace of change makes it near impossible to plan just three years out — let alone five years in the future, as once was the standard. Even with many CIOs now going with 18- or 24-month roadmaps, a lot of unknowns remain. “Roadmaps are just a plan, where we’re taking an educated guess of what the future might bring.
Enterprises today are in dire need of AI adoption and data management, thanks to increased competitiveness and unprecedented demand for automation. As companies prepare to meet this demand with a structured approach towards data modernization , their success relies on a foundational alignment between data strategies, AI adoption initiatives, and the overarching goals and objectives of the business.
ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0 like assets, backfills, and event-driven scheduling make orchestrating ETL/ELT pipelines easier than ever!
Our customers tell us that scientists are increasingly spending more time managing data-related challenges than focusing on science. The primary reason for this challenge is that scientific data comes in many types and is siloed across systems, groups, and stages, and scientists struggle to efficiently discover, access, share, and analyze datasets across silos.
Data's at the heart of every AI achievement I was recently browsing through some fascinating AI statistics on Exploding Topics , and one pattern became crystal clear: every impressive AI achievement traces back to one critical ingredient—data. Let me walk you through the numbers that illustrate this fundamental truth. Take Netflix’s stunning $1 billion in annual revenue from automated recommendations.
Discover a comprehensive collection of cheat sheets covering Docker commands, mathematics, Python, machine learning, data science, data visualization, CLI commands, and more.
Have you ever considered building tools powered by LLMs? These powerful predictive models can generate emails, write code, and answer complex questions, but they also come with risks. Without safeguards, LLMs can produce incorrect, biased, or even harmful outputs. That’s where guardrails come in. Guardrails ensure LLM security and responsible AI deployment by controlling outputs […] The post Can You Trust Your LLM?
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
The Governance Imperative: Beyond Simple Project Approval In today's rapidly evolving business landscape, boards face unprecedented pressure to make informed decisions about technology investments—particularly AI initiatives. These decisions carry far-reaching implications for organizational risk, competitive advantage, and stakeholder trust. According to a recent PwC survey , only about half of board directors feel they receive adequate information about AI-related risks, with directors not
Picture this: You’re a financial analyst starting your Monday morning with a steaming cup of coffee, ready to review your investment portfolio. But instead of manually scouring dozens of news websites, financial reports, and industry analyses, you simply ask your AI assistant: “What global events happened over the weekend that might impact my technology stock holdings?
If data is so important, why is it treated as an afterthought? Have you noticed how data is always deemed “critical,” yet we’re not always so sure who is accountable for it? You invest in platforms, hire data scientists, implement new controls, yet areas of your organisation continue to view data as an afterthought. If data is indeed the company’s crown jewel, why do many business leaders find it difficult to secure cross-functional buy-in—or even basic engagement—when it matters most?
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Debugging and Tracing LLMs Like a Pro Debugging LLMs is important because their workflows are complex and involve multiple parts like chains, prompts, APIs, tools, retrievers, and more.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
Editor’s Note: This article was originally authored by our colleague and BARC Fellow, Douglas Laney, and was first published on Forbes.com. We are republishing it with full permission, as we believe its insights are highly relevant to the topics we cover and valuable for our community. The independent “Data Company” or “DataCo” is a separate corporate entity whose sole objective is to manage and extract greater value from its parent company’s data.
A transformative agentic AI approach can be applied across various sectors, each with domain-specific opportunities. Below, we focus on four key industries — banking, insurance, healthcare and retail — highlighting how agentic AI solutions drive innovation and efficiency in each. These use cases illustrate the tangible benefits of the framework in action.
ISG Software Research recently published the 2025 Buyers Guide for Real-Time Data , providing an assessment of 43 software providers offering products used by analytics and data professionals to facilitate the use of real-time data. The Real-Time Data Buyers Guide research includes five reports which are focused on overall Real-Time Data, Application Integration, Messaging and Event Processing, Streaming Data and Streaming Analytics.
In 2025, several AI coding assistants have been released, which can be accessed directly from the terminal. Codex CLI, Gemini CLI, and Claude Code are some of the popular names that embed large language models into command-line workflows. These programming tools that can generate and fix code via natural language prompts are truly incredible. We […] The post Codex CLI vs Gemini CLI vs Claude Code appeared first on Analytics Vidhya.
AI adoption is reshaping sales and marketing. But is it delivering real results? We surveyed 1,000+ GTM professionals to find out. The data is clear: AI users report 47% higher productivity and an average of 12 hours saved per week. But leaders say mainstream AI tools still fall short on accuracy and business impact. Download the full report today to see how AI is being used — and where go-to-market professionals think there are gaps and opportunities.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Getting The Most From The LangChain Ecosystem Learn how to use the LangChain ecosystem to build, test, deploy, monitor, and visualize complex agentic workflows.
Cookies help us display personalized product recommendations and ensure you have great shopping experience. Accept X By using this site, you agree to the Privacy Policy and Terms of Use. Accept Analytics Analytics Show More Turning Data Into Decisions: How Analytics Improves Transportation Strategy 3 Min Read How Data Analytics Improves Lead Management and Sales Results 9 Min Read How Data Analytics Reduces Truck Accidents and Speeds Up Claims 7 Min Read Interior Designers Boost Profits with Pre
At a recent conference, I witnessed something that’s become far too common in data leadership circles: genuine surprise that chief data officers consistently cite culture — not technology — as their greatest challenge.
Reading Time: 3 minutes If you’re not familiar with the Gartner Critical Capabilities reports, in my opinion, they provide a rigorous comparison of vendors across a set of (critical) capabilities, and they are meant to be read along with the Gartner® Magic Quadrant™ reports. The post My Take on the 2024 Gartner® Critical Capabilities for Data Integration Tools Report appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.
Speaker: Claire Grosjean, Global Finance & Operations Executive
Finance teams are drowning in data—but is it actually helping them spend smarter? Without the right approach, excess spending, inefficiencies, and missed opportunities continue to drain profitability. While analytics offers powerful insights, financial intelligence requires more than just numbers—it takes the right blend of automation, strategy, and human expertise.
OpenAI has released two open-source language models, gpt-oss-120b and gpt-oss-20b. These are OpenAI’s first openly licensed LLMs since GPT-2. Aiming to create the best state-of-the-art reasoning and tool-use models available. The models were launched to considerable fanfare in the AI community. By open-sourcing gpt-oss, OpenAI allows people to freely use and adapt within the bounds […] The post OpenAI Returns to Open Source: A Complete Guide to gpt-oss-120b and gpt-oss-20b appeared
This article introduces and discusses four key reasons why data visualization is essential in data storytelling: simplifying complex information, discovering hidden patterns, fostering engagement and impact, and supporting informed decisions.
The compute paradigm shift is here The infrastructure that powers AI today won’t sustain tomorrow’s demands. CIOs must rethink how to scale smarter—not just bigger—or risk falling behind. Discover how Arm is redefining computing infrastructure for the AI era. As AI continues to reshape industries—from healthcare to financial services to national defense—we’re entering a new compute paradigm, where legacy models no longer scale economically or sustainably.
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