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Top 25 AI Tools for Software Development in 2025

Marktechpost

AI-powered tools have become indispensable for automating tasks, boosting productivity, and improving decision-making. TabNine TabNine is an AI-powered code auto-completion tool developed by Codota, designed to enhance coding efficiency across a variety of Integrated Development Environments (IDEs).

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Machine Learning with MATLAB and Amazon SageMaker

Flipboard

MATLAB   is a popular programming tool for a wide range of applications, such as data processing, parallel computing, automation, simulation, machine learning, and artificial intelligence. Our objective is to demonstrate the combined power of MATLAB and Amazon SageMaker using this fault classification example.

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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

This requires not only well-designed features and ML architecture, but also data preparation and ML pipelines that can automate the retraining process. To solve this problem, we make the ML solution auto-deployable with a few configuration changes. AutoGluon is a toolkit for automated machine learning (AutoML).

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Top MLOps Tools Guide: Weights & Biases, Comet and More

Unite.AI

By establishing standardized workflows, automating repetitive tasks, and implementing robust monitoring and governance mechanisms, MLOps enables organizations to accelerate model development, improve deployment reliability, and maximize the value derived from ML initiatives.

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Smart Factories: Artificial Intelligence and Automation for Reduced OPEX in Manufacturing

DataRobot Blog

As a result of these technological advancements, the manufacturing industry has set its sights on artificial intelligence and automation to enhance services through efficiency gains and lowering operational expenses. These initiatives utilize interconnected devices and automated machines that create a hyperbolic increase in data volumes.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc., This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. and Pandas or Apache Spark DataFrames.

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Top Low-Code and No-Code Platforms for Data Science in 2023

ODSC - Open Data Science

Low-Code PyCaret: Let’s start off with a low-code open-source machine learning library in Python. H2O AutoML: A powerful tool for automating much of the more tedious and time-consuming aspects of machine learning, H2O AutoML provides the user(s) with a set of algorithms and tools to automate the entirety of the machine learning workflow.