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30 Datasets for ML & AI Models in 2025
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.
Reverse Proxies: Overview, Benefits and Risks in 2025
Proxies help in masking your IP address and providing security against attacks on the internet. In this article, we will explore reverse proxies. What is a reverse proxy? A reverse proxy is a server that sits in front of web servers. It forwards clients’ requests, like web browsers, to those servers.
Top 9 Metrics for Software Testing Efficiency in 2025
The cost of software bugs in the production stage can be high; testing must be done to detect them. However, testing is not cheap; it is estimated that testing can take up to 25% of a project’s total cost. Given the high cost of testing, it becomes essential that your tests be efficient and effective.
Top 20 Test Automation Statistics QA Teams Must Know
Test automation can provide faster and more efficient tests than manual testing. Automating testing also offers benefits such as increased test coverage and accuracy. The interest in test automation has been increasing over the years. We present top statistics that show its benefits, trending practices, and rate of adoption by your competitors.
Top 7 Sentiment Analysis Challenges in 2025
Words are the most powerful tools to express our thoughts, opinions, intentions, desires, or preferences. However, the complexity of human languages constitutes a challenge for AI methods that work with natural languages, such as sentiment analysis. Explore sentiment analysis challenges and ways to improve sentiment analysis accuracy: Top 7 challenges in sentiment analysis 1.
5 Key Benefits of MLOps Practices for Businesses in 2025
MLOps is a method based on adapting DevOps practices to machine learning development processes. MLOps is useful in transitioning from running a couple of ML models manually to using ML models in the entire company operation. Overall, MLOps helps you improve delivery time, reduce defects, and make data science more productive.
Top 5 Data Collection Trends for Data-Driven Businesses
Data collection is becoming common practice for many businesses. Whether for implementing deep tech or conducting analytics, business leaders are continuously involved in gathering or using data to improve their operations. As people realize the power of harnessing data, the regulations and practices of gathering and using it change.
20 Test Automation Case Studies Demonstrating Business Impact
QA teams struggle with slow, manual testing—resulting in higher costs, longer development cycles, and customer dissatisfaction. Transitioning to automated QA testing is the top priority in the software testing environment. To help decision-makers assess the impact of test automation, we analyze 20 case studies highlighting real-world transformations.
Compare Top 5 MLOps vs DataOps Differences in 2025
Inspired by DevOps practices, MLOps and DataOps have emerged as critical methodologies for ensuring seamless machine learning and database operations. While both share roots in automation and operational efficiency, the debate around MLOps vs DataOps highlights their distinct roles in IT workflows.
Data Collection for Marketing: 8 Use Cases & Strategies
Marketing teams today face growing pressure to collect data more intelligently and responsibly. As privacy regulations tighten, AI becomes more embedded in campaigns, and customer acquisition costs rise, outdated data collection methods can quickly lead to compliance issues and underperforming strategies.