As more AI-enhanced applications seep into our daily lives and expand their reach to larger swaths of populations around the world, we must clearly understand the vulnerabilities trained machine leaning models can exhibit based on the data used during development. Such issues can negatively impact select groups of people, so addressing the ethical decisions made by AI--possibly unknowingly--is important to the long-term fairness and success of this new technology.
Organizations trying to move forward with analytics and data science initiatives -- while floating in an ocean of data -- must enhance their overall approach and culture to embrace a foundation on DataOps and MLOps. Leveraging these operational frameworks are necessary to enable the data to generate real business value.
Within the broad universe of IT in the business world, the approaches for deploying solutions by traditional software engineers and trendy, new data scientists couldn't be more different. However, appreciating these differences are incredibly important because great business value can be gained by integrating both worlds of development into driving more efficiency and effectiveness into an organization.
As your organization matures its data science portfolio and capabilities, establishing a modern data stack is vital to enabling such growth. Here, we overview various in-data warehouse machine learning services, and discuss each of their benefits and requirements.
Many new roles have appeared in the data world ever since the rise of the Data Scientist took the spotlight several years ago. Now, there is a new core player ready to take center stage, and we may see in five years, nearly every organization will have an Analytics Engineering team.
Our latest poll shows major change in where AI, Data Science, Machine Learning are being applied, with decline in interest in traditional fields like CRM/Consumer Analytics, and growth in applications to Computer Vision, COVID, Agriculture, and Education.
Go from zero to hero in under six months. Data science has a very high barrier of entry. It is a very competitive field that everybody from different educational backgrounds are looking to get into. Here is useful advice on how to proceed.
Today, we need much more than just numbers about our organization to understand, gain insights, and take relevant actions. While visualizations of the data are important, making an emotional connection with the stories behind the data is key. If you want to sell a story, send a missile to the heart.
Edge Analytics isn’t just coding and tools. The different environment outside the datacenter or cloud means a purpose built platform is the best way to deliver consistent results. We discuss 5 different considerations for an edge platform to support your training and deployment.
Many career opportunities exist in the ever-expanding domain of data. Finding your place -- and finding your salary -- is largely up to your dedication, focus, and drive to learn. If you are an aspiring Data Scientist or have already started your professional journey, there are multiple strategies for maximizing your earning potential.
What’s the key to a smooth data migration experience? It comes down to this primary issue: whether or not you can rapidly determine your dataset composition.
What will the demand for Data Scientists be in 10 years? Will Data Scientists be extinct?
How to Land a Data Analytics Job in 6 Months
Data Scientists Will be Extinct in 10 Years
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