AIMultipleAIMultiple
No results found.

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.

Explore Data Science

Inverse Reinforcement Learning: Use Cases & Examples

Data ScienceJul 7

Inverse reinforcement learning is an approach in machine learning where machines infer the goals or reward structures that guide an expert’s behavior by observing their actions rather than receiving explicit instructions. Discover what inverse reinforcement learning is, how it works, and the top industry use cases with examples.

Read More
Data ScienceJul 30

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 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.

Data ScienceJul 2

Toloka AI Review & Its Top Alternatives for RLHF

Toloka AI is a popular name in the Reinforcement Learning from Human Feedback (RLHF) and AI data services spaces. If your business is considering an RLHF or AI data partner like Toloka AI, our research can provide valuable guidance.

Data ScienceMay 28

Applying RLHF: Techniques, use cases, and challenges

Training AI systems to align with human values can be a challenge in machine learning. To mitigate this, developers are advancing AI through reinforcement learning (RL), allowing systems to learn from their actions. A notable trend in RL is Reinforcement Learning from Human Feedback (RLHF), which combines human insights with algorithms for efficient AI training.

Data ScienceMay 27

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 ScienceJun 13

Guide To Machine Learning Data Governance

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.

Data ScienceMar 22

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 22

30 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. Explore different types of existing datasets: custom human-generated, custom machine-generated, natural language processing, open, public government, image, audio, and healthcare datasets to train your machine-learning models.

Data ScienceAug 19

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 ScienceApr 4

Web Scraping for Machine Learning: From HTML to ML

~54.7 billion people around the world have been recorded to use the internet, creating 1.7MB of data every second. Crawling this exponentially growing volume of data could provide many opportunities for breakthroughs in data science.