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Navigating the AI Boom: My Compass in a World of Digital Brains

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As the host of Techsambad, I've had the privilege of exploring the cutting edge of technology with industry experts. In my recent podcast, I sat down with Mouli, a product manager at a stealth-mode consumer AI startup, to demystify the current AI landscape that seems to change by the minute. Here are my key takeaways from our enlightening conversation. The "Deluge of Models": Not All AIS Are Created Equal During our discussion, Mouli highlighted a crucial point that resonated with me: the sheer number of AI models available today is daunting. From Chatgpt, which has almost become a generic term like "Xerox," to offerings from Google (Gemini), Anthropic (Claude), Meta (Llama), and many others, it's a crowded field that can confuse even tech enthusiasts. What sets them apart? I learned that: Training Data & Parameters: Each model is trained on different datasets with varied parameters, leading to unique strengths and weaknesses. Some excel at coding, other...
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Why I think In Memory Data Management was made for cloud

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http://upload.wikimedia.org/wikipedia/commons/b/b5/Cloud_computing.svg







Recently, I came across a nice blog on why Amazon thinks big data was made for the cloud. It talks of how big data and cloud computing will work hand in hand to create a central platform for communities to share huge Data Set.  In Memory Data management such as HANA enables this symbiotic relationship between the cloud and big data by facilitating on the fly reorganization of data






  1. Separate Database for customers
  2. Shared Database, Separate Schemas for customers
  3. Shared Database, Shared Schema for customers

Of the three approaches, the shared schema approach has the lowest costs, because it serves largest number of tenants per database server. Also, the administrative and hardware/software costs are drastically reduced. But, it comes with one complexity: As the customer isolation is the minimal, stringent database management  is required to ensure that tenants can never access other tenants' data, even in the event of unexpected bugs or attacks. Dynamic reorganization of Data is one of the prime requirements.

In Memory Databases such as HANA leverages the positives of columnar databases so that
  • New Attributes can be added easily vis-à-vis a row based database architecture
  • Locking for changing the data layout in only required for a short period contrary to row based architecture where the entire database or table would be completely locked to process data definition operations

I feel that there is a  great applicability of In Memory Data Management in cloud. Do give in your comments/ feedback on this piece.

Comments

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