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It’s worth pointing out that Azure is a bit above the long term trendline, while AWS is still below (but accelerating up). GCP data is a bit more noisy as they don’t disclose GCP itself, but rather Google Cloud which includes GSuite.
It’s less expensive than it’s ever been in terms of actually getting a product to market, whether it’s leveraging platforms like Salesforce or GCP or AWS or Heroku. Just from a platform perspective, we can look at it: everybody knows from the AWS perspective how inexpensive it is to go to market.
Data scientist’s main responsibilities The three responsibility pillars of a data scientist encompass Data Acquisition and Engineering, Data Analysis and Modeling, and Communication and Collaboration. Data acquisition and engineering: Data Extraction : SaaS products generate a ton of user data. Tableau, Power BI).
Ali: This is why we did the acquisition of Mosaic. It will be like AWS, GCP, and Azure. Can they build a better model themselves for that with their data? Or if they took their data and put it in 1 of the large models, would that always beat what they’re doing? It’s hard and requires a lot of GPUs.
Bonus points : Experience with cloud platforms (AWS, Azure, GCP). Data scientist’s main responsibilities The three responsibility pillars of a data scientist encompass Data Acquisition and Engineering, Data Analysis and Modeling, and Communication and Collaboration. Experience with data visualization tools (e.g.,
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