Ciência de Dados
A ciência de dados capacita as organizações a extrair insights acionáveis de dados por meio de análise estatística, aprendizado de máquina e modelagem preditiva. Exploramos ferramentas, técnicas, aplicações práticas e melhores práticas para apoiar a tomada de decisões orientada por dados e os esforços de transformação digital.
Teste de Referência de Banco de Dados de Grafos: Neo4j vs FalkorDB vs Memgraph
We benchmarked Neo4j, FalkorDB, and Memgraph on a synthetic graph derived from 120,000 Amazon product reviews (381K nodes, 804K edges).
Aprendizado Federado: 7 Casos de Uso & Exemplos
According to recent McKinsey analyses, the most pressing risks of AI adoption include model hallucinations, data provenance and authenticity, regulatory non-compliance, and AI supply chain vulnerabilities. Federated learning (FL) has emerged as a foundational technique for organizations seeking to mitigate these risks.
57 Conjuntos de Dados para Modelos de ML e IA
Data is required to leverage or build generative AI or conversational AI solutions. You can use existing datasets available on the market or hire a data collection service. We identified 57 datasets to train and evaluate machine learning and AI models.
Principais Plataformas de ML sem Código: Alternativas ao ChatGPT
We benchmarked 4 no-code machine learning platforms across key metrics: data processing (handling missing values, outliers), model setup and ease of use, accuracy metrics output, availability of visualizations, and any major limitations or notes observed during testing. No-code machine learning tools benchmark Note: Scores represent average performance across kNN and Logistic Regression where applicable.