“Listening to the data is important… but so is experience and intuition. After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model?” Steve Lohr

1. Google’s TensorFlow Object Detection API

At Google, we develop flexible state-of-the-art machine learning (ML) systems for computer vision that not only can be used to improve our products and services, but also spur progress in the research community. Creating accurate ML models capable of localizing and identifying multiple objects in a single image remains a core challenge in the field, and we invest a significant amount of time training and experimenting with these systems.

2.The age of analytics

Big data’s potential just keeps growing. Taking full advantage means companies must incorporate analytics into their strategic vision and use it to make better, faster decisions.

3.Data Science 360° Customer Views

One of the toughest mysteries lies is cracking customer behaviour. The very reason why data science fetched such popularity and patronage around the world as it could uncover customer behavior and improve their experience. This article helps you crack that by identifying data sources and implementing ML algorithms.

4.COVID-19 Impact on the Global Big Data Analytics Industry

This research measures the future impact of COVID-19 on the global Big Data Analytics (BDA) market. The embedded ecosystem has led to a hyper-connected world and the growth of the Internet of Things (IoT). Thanks to ubiquitous networks, IoT has connected all manner of endpoints and unveiled a treasure trove of data. This unprecedented volume of data has the potential to empower decision-makers as never before. Especially during the COVID-19 pandemic – including efforts to contain its spread and help businesses stay afloat – the need to extract, visualize, and execute this intelligence in near-real time is increasingly becoming a mission-critical objective.

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