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Top 25 AI Tools for Software Development in 2025

Marktechpost

From enhancing software development processes to managing vast databases, AI has permeated every aspect of software development. As businesses strive to stay competitive, adopting AI tools can streamline workflows, minimize errors, and unlock innovative possibilities.

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Transforming IT operations and application modernization with artificial intelligence

IBM Journey to AI blog

Existing sales and service engineers can use language-based generative AI to augment their skills and easily find contextual or industrial knowledge to help them deliver better customer experiences or solve problems faster. For instance, organizations can use AI tools to audit compliance documentation according to relevant audit standards.

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DeepMind AI Supercharges YouTube Shorts Exposure by Auto-Generating Descriptions for Millions of Videos

Marktechpost

” This generated text is stored as metadata, enabling more efficient video classification and facilitating search engine accessibility. The impact of Flamingo has already been felt, as hundreds of thousands of newly uploaded Shorts videos have benefited from AI-generated descriptions.

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning Blog

You can deploy this solution with just a few clicks using Amazon SageMaker JumpStart , a fully managed platform that offers state-of-the-art foundation models for various use cases such as content writing, code generation, question answering, copywriting, summarization, classification, and information retrieval.

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Microsoft Phi 2 for Classification

Mlearning.ai

Modifying Microsoft Phi 2 LLM for Sequence Classification Task. Transformer-Decoder models have shown to be just as good as Transformer-Encoder models for classification tasks (checkout winning solutions in the kaggle competition: predict the LLM where most winning solutions finetuned Llama/Mistral/Zephyr models for classification).

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How to Practice Data-Centric AI and Have AI Improve its Own Dataset

ODSC - Open Data Science

For more complex issues like label errors, you can again simply filter out all the auto-detected bad data. For instance, when fine-tuning various LLM models on a text classification task (politeness prediction), this auto-filtering improves LLM performance without any change in the modeling code! You can find more details here.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. The platform provides a comprehensive set of annotation tools, including object detection, segmentation, and classification.