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 us on how to successfully.
Announcing DSSe at DataConnect Conference 2022
We are thrilled to announce that DSSelevate (DSSe), our initiative to help close the gender gap in tech, will be part of this year's DataConnect Conference in Columbus, Ohio, hosted by Women in Analytics (WIA) this coming June 2nd and 3rd!
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 under-performing.
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 do when no ML model is perfect?.
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 feedback.
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.
Interpretable Machine Learning and How to Build Trust in our Models
Machine learning models can undoubtedly help humans make more informed decisions, with an increasing number of use cases among different industries. By feeding the algorithms with large amounts of data, they are able to identify patterns and make predictions accordingly.
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.
How Natural Language Processing Supports Financial Services
Financial services are traditionally a data-heavy industry full of transactional data, user data and textual data.
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?
