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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.

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Guide To Machine Learning Data Governance

Data ScienceOct 27

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.

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Data ScienceOct 27

Few-Shot Learning: Methods & Applications

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.

Data ScienceOct 22

22 AutoML Case Studies: Applications and Results

Though there is a lot of buzz around autoML, we haven’t found a good compilation of case studies. So we built our comprehensive list of automated machine learning case studies so you can see how autoML could be used in your function/industry.

Data ScienceOct 9

50+ 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 50+ datasets to train and evaluate machine learning and AI models.

Data ScienceSep 27

45 Statistics, Facts & Forecasts on Machine Learning

Machine learning is the study of computer algorithms that learn through data. Machine learning is regarded as a subset of artificial intelligence. Surveys and market research are the best way to understand the overall view of the machine learning market, as numbers can reveal key metrics, from the market’s importance to its challenges.

Data ScienceSep 27

TinyML(EdgeAI): Machine Learning at the Edge

Applications of edge analytics transforming industries and the edge computing market is expected to reach ~$350 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.

Data ScienceSep 24

Top No-Code ML Platforms: ChatGPT Alternatives

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.

Data ScienceSep 17

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.

Data ScienceSep 12

BI Governance: 6 Implementation Best Practices

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.

Data ScienceJul 24

Federated Learning: 5 Use Cases & Real Life Examples

McKinsey highlights inaccuracy, cybersecurity threats, and intellectual property infringement as the most significant risks of generative AI adoption.Federated learning addresses these challenges by enhancing accuracy, strengthening security, and protecting IP, all while keeping data private.

Data ScienceJul 22

Top 5 RLHF Platforms: Guide & Features Comparison

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.