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.
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.
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.
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.
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.
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 researches are the best way to understand the overall view of the machine learning market because numbers can reveal metrics from the importance of the market to its challenges.
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.
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.
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.
Inverse Reinforcement Learning: Use Cases & Examples
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.
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.