…d be foolhardy for the vast majority of vendors to even try to meet their requirements. This is why introducing licensing shenanigans to solve the “AWS problem” is a non-starter, a solution looking for a problem. You’re not going to resolve your own business mistakes by reverse-engineering a licensing solution to what was essentially a business model problem. Do you honestly believe that Amazon was going to use RedisLabs’ management implementations when introducing its in-memory database services? The fact is, whether you’re Mongo, RedisLabs, Confluent, or anyone else, you’re free to sell your solutions as a service to customers using any IaaS platform, including Amazon’s or Google’s. In fact, Mongo has done precisely that, and by most accounts, pretty successfully at that.
…aking this possible is no small feat; it requires extensive engineering and infrastructure support. Every day more than 1 trillion events are written into a streaming ingestion pipeline, which is processed and written to a 100PB cloud-native data warehouse. And every day, our users run more than 150,000 jobs against this data, spanning everything from rep…
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…shifted the primary challenge of NMT to the problem of “fixed-length vector”: As shown in Figure 1, no matter how long or short the source sentence is, the neural network needs to compress the source sentence into a fixed-length vector, which will lead to increasing complexity and uncertainties during decoding especially when the source sentence is long .