Data Science
Data science empowers organizations to extract actionable insights from data through statistical analysis, machine learning, and predictive modeling. We explore tools, techniques, real-world applications, and best practices to support data-driven decision-making and digital transformation efforts.
57 Datasets for ML & AI Models
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.
Federated Learning: 7 Use Cases & Examples
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.
TinyML(EdgeAI): Machine Learning at the Edge
Applications of edge analytics transforming industries and the edge computing market is expected to reach ~$350 billion by 2027. However, the current approach to edge analytics involves machine learning models trained on the cloud. This introduces latency to the system and is prone to privacy issues.
Top No-Code ML Platforms: ChatGPT Alternatives in 2026
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.
Meta Learning: 7 Techniques & Use Cases in 2026
Training and fine-tuning a typical machine learning (ML) model can take weeks and cost thousands of dollars. Meta learning helps cut this down by leveraging prior learning experiences to accelerate training, reduce costs, and improve generalization. Explore the key meta-learning techniques and use cases in fields such as healthcare and online learning.
Guide To Machine Learning Data Governance in 2026
In this article, we explain machine learning data governance. We explain its key principles, benefits, use cases, best practices, and our future expectations of data governance.
Few-Shot Learning: Methods & Applications in 2026
Imagine a healthcare startup building an AI system to detect rare diseases. The challenge? There isn’t enough labeled data to train a traditional machine learning model. That’s where few-shot learning (FSL) comes in. From diagnosing complex medical conditions to enhancing natural language processing, few-shot learning is redefining how AI learns from limited examples.
Machine Learning in Data Integration: 8 Use Cases & Challenges
Integrating and analyzing data from disparate sources effectively has become paramount. Data integration often presents challenges, ranging from managing AI data quality to ensuring security. As organizations grapple with these obstacles, Artificial Intelligence (AI) and Machine Learning (ML) are emerging as transformative technologies, offering innovative solutions to simplify and enhance data integration processes.
BI Governance: 6 Implementation Best Practices in 2026
The global business intelligence market is projected to be $33.3B by 2025, with more business units adopting BI tools. The importance of business intelligence is increasing. Data-driven decision making, for instance, is five times faster via data access and data analytics.
Top 5 RLHF Platforms: Guide & Features Comparison ['26]
As AI adoption grows, with 65% of organizations now regularly using generative AI, selecting the right tools for optimizing AI models has become more crucial than ever. Reinforcement learning from human feedback (RLHF) platforms have emerged as key players in this process.