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For those of you, who are wondering what is “Analytics Vidhya”, “Analytics” can be defined as the science of extracting insights from raw data. The spectrum of analytics starts from capturing data and evolves into using insights / trends from this data to make informed decisions. “Vidhya” on the other hand is a Sanskrit noun meaning “Knowledge” or “Clarity on a subject”. Knowledge, which has been gained through reading literature or through self practice / experimentation.
Analytics Vidhya provides a community based knowledge portal for Analytics and Data Science professionals. The aim of the platform is to become a complete portal serving all knowledge and career needs of Data Science Professionals. Source
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| Language | English |
| Country | India |
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Recent Articles
Search ArticlesOpenAI Decisions API: What it is and how to use it?
Imagine building an AI agent that has to decide whether a customer complaint needs human intervention, which department should handle a query, or whether a submitted delivery photo is valid. Getting these decisions right matters. A false positive can trigger an unnecessary escalation, while a false negative can let a genuine issue slip through.
LLM Batching Explained: Static vs Dynamic vs Continuous Batching
Imagine four people booking a ride to the same destination. If each person takes a separate taxi, four vehicles make the same trip. But if they travel together in a bus, everyone reaches the destination in a single trip. The bus carries more people at once and makes better use of the resources. Batching in AI works in a similar way. Instead of processing one request at a time, a server groups multiple requests together so the GPU can process them more efficiently.
How to Design Human-in-the-Loop Checkpoints for Autonomous AI Agents
Reading list Autonomous AI agents can read requests, retrieve data, reason through options, and trigger actions in seconds. That speed is useful, but it also creates risk when the next step affects money, customer records, or external systems. Human-in-the-loop checkpoints add control at the moment an agent’s recommendation is about to become a real-world action.
EmbeddingGemma 2: Text, Code, Images, Video and Audio in One Vector Space
Reading list EmbeddingGemma 2 launched on October 6, 2026 under Apache 2.0. It is a sub-1B model built on Gemma 4 that maps text, code, images, video and audio into one 768-dimensional space. This article covers the architecture, the benchmarks, and runnable scripts to provide measured results.
Nano Banana 2.1 Review
Have an idea for a lesson illustration, a project chart, or a comic for your next presentation? Nano Banana 2.1 lets you explore these formats with text prompts, making it easier to try different visual styles without switching between tools. In this article, we test Nano Banana 2.1 across four practical tasks: a clay illustration, a bar chart, a linocut photo transformation, and a debugging comic, looking at what worked, what fell short, and which details needed closer attention.
10 Jev Projects on GitHub You Should Check Out
Reading list Agents make dozens of small decisions before producing useful output: which source to trust, which tool to call, what context to keep and when to stop. Jev brings those hidden choices into the open by turning them into typed decisions with probabilities, instead of relying on free-form text alone. That makes it useful across browser agents, code reviewers, context-pruning tools, search workflows, routers and mobile automation projects.
JEV vs LLM as a Judge: The AI Evaluation Comparison
Reading list An LLM judge writes its answer as text. JEV gives a short, ready-to-use answer directly. Many teams now use LLM-as-a-Judge to check AI answers, especially when exact-match tests fail for long or open-ended responses. But every judgement adds cost, delay, and possible bias, making this hard to scale. Jev, a small decision model from TypeSafe AI, takes a leaner route: it returns a short choice with confidence instead of full written reasoning.
Building an Enterprise AI Customer Support Platform with Claude Fable 5.1 and Claude Code
Reading list Most AI coding demos stop at task managers, weather apps, or simple chatbots. For this project, we take on something more demanding: building an enterprise customer-support platform that can investigate complaints, retrieve relevant policies, recommend resolutions, and keep risky actions behind human approval. This gives us a practical way to test Claude Fable 5.1 as an agentic engineering tool rather than simply a code generator.
Claude Code Custom Commands & Skills: Automate Your Workflow
Every dev team has repeatable setup tasks: spinning up a new project, running a deployment checklist, generating a file structure, or typing the same instructions into Claude again and again. Claude Code helps reduce that friction with slash commands, including built-in commands and custom commands you can define for your own workflows. These commands turn repeated instructions into quick triggers, making day-to-day development smoother and more consistent.
OpenAI Dot: How to Access & Automate Work with OpenAI’s Agent
Even the future has a waiting room! That is a slightly awkward introduction to OpenAI Dot, but a useful one. In this article I’ll be discussing what this autonomous 24/7 agent (which consumes no usage) can do, what it is, how you can access it (along with its setup), and a live analytics exercise I ran after on Dot. By the time you finish this article, you’d be using autonomous AI agents like never before.