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Machine Learning for Fraud Detection in Streaming Services

The Netflix TechBlog

Data analysis and machine learning techniques are great candidates to help secure large-scale streaming platforms. These models learn the distributions of benign samples and leverage that knowledge for identifying anomalous samples at the inference time. Detection of fraud and abuse at scale and in real-time is highly challenging.

C++ 312
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Dynatrace supports Amazon Linux 2023 as an AWS launch partner

Dynatrace

Saving your cloud operations and SRE teams hours of guesswork and manual tagging, the Davis AI engine analyzes billions of events in real time. To learn more about Dynatrace and to start your free trial, visit the Dynatrace website.

AWS 281
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Using JSONB in PostgreSQL: How to Effectively Store & Index JSON Data in PostgreSQL

Scalegrid

You can also check out our Working with JSON Data in PostgreSQL vs. MongoDB webinar in partnership with PostgresConf to learn more on the topic, and check out our SlideShare page to download the slides. It will result in a sequential scan: demo=# select * from books where data->'tags' ?

Storage 321
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Tech Transforms podcast: Navigating complex cloud environments and improving efficiency

Dynatrace

Tag us with #TechTransforms to be featured on our pages! Luckily, the AI models have come a long way in learning what happens every evening. Tune in to the full episode to learn more about the UK Home Office’s cloud journey and how Dimitris navigates this large-scale environment to deliver essential services efficiently.

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

High Scalability

This is guest post by Sachin Sinha who is passionate about data, analytics and machine learning at scale. Application example: photo tagging; add a tag is an update, but most operations are to read tags. Author & founder of BangDB. Workload B: Read mostly workload. This workload has a 95/5 reads/write mix.

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Answer-driven release validation with Dynatrace SaaS Cloud Automation

Dynatrace

Based on survey results gathered during the Dynatrace Perform 2020 conference, we learned that just 10% of organizations have implemented automatic quality gates in their release pipelines. In other words, 90% of organizations either rely on manual quality checks or, even more risky, rely only on hope! Seeing is believing.

Cloud 240
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IT Operations: A Use Case in the 2023 Gartner Critical Capabilities for Application Performance Monitoring and Observability

Dynatrace

Teams get bogged down manually tagging data, reacting to alert storms, switching between disconnected dashboards, tediously trying to restore and correlate observability data from an archive, or interpreting incomplete data because of sampling or lack of retention. Want to learn more?