Datalicious Lakshmikanth Rajamani
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- Technology
The data podcast for people, who love to play with the data. This podcast is hosted by Lakshmikanth Rajamani, he discuss about how successful companies in the planet are consuming Data Science, Big Data and AI in their workplace and delivering extraordinary results.
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Are you a Data Scientist or ML Engineer?
The minimal difference that helps you understand what Data Scientist and an ML engineer would do in their day-to-day work. Data Science is the generic term, that fuses data and science in a right proportion. Data Science is the art of dealing with problem-related to contextual data which may be structured or unstructured. Fortune companies started consuming actionable insights from their data lake and so big data comes into the picture and the opportunities to handle those data are challenging. Data Scientist deals with data engineering, statistical modeling, and convey their findings through beautiful charts. Wherein ML engineers are responsible for delivering the outcomes of the model predictions and the engineering behind them. They write services to encapsulate the models, provide security for them, take care of deployment and if required optimize their performance and so. That's how data scientists and ML engineers differ in their work in nature.
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Social Data and it’s importance!
Social Data is an essential piece of your business marketing strategy. Social channels help you connect with your customers, increase awareness about your brand and boost your leads and sales. Let’s get some insights about social Data and its users adoption.
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Meeting with Binary Data - Introduction
Meeting with binary data is a new series from Datalicious. Where we discuss about the evolution of computer science and its applications.
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Ascendence of Data
It gives you some insights about the data governance and it’s game changing part in modern enterprises.
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Importance of Data Analytics and Salesforce Einstein.
When couple of folks from AI and Data Science start discussing about the importance of data analytics and success requirements of ML engine.