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Scalable Annotation Service?—?Marken

The Netflix TechBlog

For example, we have a service that stores a movie entity’s metadata or a service that stores metadata about images. In Pic 1 below, we have an example of an application which is used by editors to review their work. All data should be also available for offline analytics in Hive/Iceberg. Annotations can be versioned.

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Benchmark (YCSB) numbers for Redis, MongoDB, Couchbase2, Yugabyte and BangDB

High Scalability

An application example is a session store recording recent actions. We note that for MongoDB update latency is really very low (low is better) compared to other dbs, however the read latency is on the higher side. Application example: photo tagging; add a tag is an update, but most operations are to read tags. Conclusion.

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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Key Takeaways Critical performance indicators such as latency, CPU usage, memory utilization, hit rate, and number of connected clients/slaves/evictions must be monitored to maintain Redis’s high throughput and low latency capabilities. Similarly, an increased throughput signifies an intensive workload on a server and a larger latency.

Metrics 130
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Dynatrace automatically monitors OpenAI ChatGPT for companies that deliver reliable, cost-effective services powered by generative AI

Dynatrace

A typical design pattern is the use of a semantic search over a domain-specific knowledge base, like internal documentation, to provide the required context in the prompt. Our example dashboard below visualizes OpenAI token consumption. This includes OpenAI as well as Azure OpenAI services, such as GPT-3, Codex, DALL-E, or ChatGPT.

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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

For example, a Stanford University and UC Berkeley team noted in a research study that ChatGPT behavior deteriorates over time. Using the example of a chatbot, once the user submits a natural language prompt, RAG summarizes that prompt using semantic data. Consequently, AI model drift and hallucinations emerge as primary concerns.

Cache 204
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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Since its inception , Metaflow has been designed to provide a human-friendly API for building data and ML (and today AI) applications and deploying them in our production infrastructure frictionlessly. Example use case: Building model explainers Here’s a fascinating example of the usefulness of portable execution environments.

Systems 226
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MySQL on Azure Performance Benchmark – ScaleGrid vs. Azure Database

Scalegrid

While Microsoft offers their own Azure Database product, there are other alternatives available that may be able to help you improve your MySQL performance. In this blog post, we compare Azure Database for MySQL vs. ScaleGrid MySQL on Azure so you can see which provider offers the best throughput and latency performance.

Azure 299