Primarily SQL-based data transformations
Posted: Sun Feb 09, 2025 10:29 am
While the road ahead presents challenges, the solutions being developed today are paving the way for data centers that meet modern technological demands and also reduce their environmental impact.
The ideal partner should have technical expertise across multiple disciplines outside AI. In the context of AI adoption, cloud, security, data, customer experience, etc., are not independent – in fact, these disciplines are highly interconnected. As such, for a partner to deliver on the promises of AI, they must have proven experience in multiple areas.
Lastly, an ideal partner should understand the importance of indonesia rcs data agility. The pace of technological change is making the future murkier and more difficult to predict. Rather than trying to anticipate some future state, businesses should instead work with their partner to make their data ecosystems and human capital agile enough to adapt rapidly and continuously.
Cloud-based SaaS with sharing, versioning, and data governance
Scalable cloud warehouses
ML and AI insights through integration with ML platforms
At a first glance, this looks sufficient. However, the “modern” data stack is permanently split into two towers along the AI/ML demarcation line.
For example, on the side of the data warehouse, the language is SQL, but the lingua franca of ML is Python – good luck finding engineers who can excel at both. Likewise, data warehouses need CPU clusters, while ML models need GPU clusters. Finally, there’s the issue of handling unstructured data like conference call videos and product photos. While cramming these multimodal objects into the database tables is already questionable, tossing them over the fence to ML models is even more cumbersome.
The ideal partner should have technical expertise across multiple disciplines outside AI. In the context of AI adoption, cloud, security, data, customer experience, etc., are not independent – in fact, these disciplines are highly interconnected. As such, for a partner to deliver on the promises of AI, they must have proven experience in multiple areas.
Lastly, an ideal partner should understand the importance of indonesia rcs data agility. The pace of technological change is making the future murkier and more difficult to predict. Rather than trying to anticipate some future state, businesses should instead work with their partner to make their data ecosystems and human capital agile enough to adapt rapidly and continuously.
Cloud-based SaaS with sharing, versioning, and data governance
Scalable cloud warehouses
ML and AI insights through integration with ML platforms
At a first glance, this looks sufficient. However, the “modern” data stack is permanently split into two towers along the AI/ML demarcation line.
For example, on the side of the data warehouse, the language is SQL, but the lingua franca of ML is Python – good luck finding engineers who can excel at both. Likewise, data warehouses need CPU clusters, while ML models need GPU clusters. Finally, there’s the issue of handling unstructured data like conference call videos and product photos. While cramming these multimodal objects into the database tables is already questionable, tossing them over the fence to ML models is even more cumbersome.