Tag: Management
5 Winning Data Engineering Strategies for 2022

5 Winning Data Engineering Strategies for 2022

Data-driven, data-first–what every organization likes to be called, but only a few fit the description. Today, organizations cannot implement modern data-centric initiatives without formulating robust data engineering strategies.  Data engineering is the process...

Dashboard Design Thinking

Dashboard Design Thinking

In this article I’m going to discuss the design thinking process and how it relates to creating dashboards. Using the design thinking process enables you to create user-centric dashboards that empower your stakeholders to make effective decisions.  After reading...

A Go-To Playbook for Multiple ML Retraining Strategies

A Go-To Playbook for Multiple ML Retraining Strategies

Building a successful machine learning model is no mean feat. It involves an arduous model-building phase and what comes next requires another rigor of maintaining the model output quality. A machine learning model once trained cannot live up to the changing data...

5 Tips to Successfully Implement ML in the Enterprise

5 Tips to Successfully Implement ML in the Enterprise

Nearly one-third of enterprises admit they don't use AI-based solutions. Tackling the data, tech and deployment-related challenges in a large company can be overwhelming, but not impossible. Filipa Castro, Data Scientist at Continental shared some good practices with...

Machine learning Projects: Lab to Live Journey

Machine learning Projects: Lab to Live Journey

AI is everywhere and its rate of adoption has significantly increased since the digital acceleration seen through the usher of the covid period. But the aesthetics get worn out soon as the aspiring AI/ML projects do not live up to the potential and end up...

A dogma of bias and variance

A dogma of bias and variance

If you are planning to choose machine learning for your business problem assuming its predictions are always correct, then there is something you should know. Machine learning algorithms are probabilistic by nature and are not perfectly accurate. Then what should we...

Using NLP to analyze customer feedback

Using NLP to analyze customer feedback

The role of customer experience is vital and known for a long time. According to the data gathered by CGS, 30% of customers are willing to pay more for excellent service. This can be delivered by listening to the voice of customers and constantly monitoring their...

Top 13 Data Science and Machine Learning Slack Communities

Top 13 Data Science and Machine Learning Slack Communities

Virtual communities make a difference, especially during a pandemic where people are unable to meet and interact in person. Slack is a real gold mine when it comes to finding inspiring peers who share similar data science joys and challenges. That’s why we have put...

A Quick Guide to Data Monetization Strategies

A Quick Guide to Data Monetization Strategies

Netflix’s success with its series “House of Cards” and AI-powered ways to reduce energy consumption at Google server farms by 40% have a shared origin. Both cases are perfect examples of companies monetizing their data. According to IDC data, there were 64.2...

How cloud computing benefits data science

How cloud computing benefits data science

Cloud computing is essentially the backbone of data science. But what exactly makes this technology so critical for data scientists and the AI solutions they build? Businesses are moving to the cloud  According to the Flexera (formerly RightScale) State of Cloud...