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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.