The hottest Cost Optimization Substack posts right now

And their main takeaways
Category
Top Technology Topics
VuTrinh. 139 implied HN points 21 May 24
  1. Working on pet projects is fun, but it's important to have clear learning goals to actually gain knowledge from them.
  2. When using tools like Spark or Airflow, always ask what problem they solve to understand their value better.
  3. To make your projects more effective, think like a user and check if they get what they need from your data systems.
Detection at Scale 119 implied HN points 08 Apr 24
  1. Security teams can optimize SIEM costs and improve data management by filtering logs effectively before they are ingested into the system. Filtering can enhance security data lake efficiency, reducing unnecessary costs and improving overall data quality.
  2. Starting with clear intentions and asking key questions about data value, cost constraints, and threat visibility can help in creating a comprehensive and cost-efficient log filtering program.
  3. Filtering at various stages - source, in transit, and within the SIEM itself - allows security teams to reduce storage costs, optimize performance, improve data quality, and enhance the relevance of collected logs.
system bashing 117 implied HN points 18 Jul 23
  1. In a tech company, engineering involves balancing cloud costs and user interface to optimize costs and enhance user experience.
  2. Reducing costs significantly is crucial for a company's profitability regardless of other measures like discounts or marketing strategies.
  3. Engineering decisions impact user experience constraints and cloud costs, requiring a balance between the two for system efficiency.
Recommender systems 33 implied HN points 06 Jan 24
  1. Training an early ranker to mimic the final ranker can improve top-line metrics and reduce costs
  2. Knowledge distillation involves training a student model, the early ranker, to learn from a teacher model, the final ranker
  3. Implementing knowledge distillation through shared or auxiliary tasks can increase alignment between the early and final rankers
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CodeLink’s Substack 0 implied HN points 11 May 23
  1. Deploying machine learning models on GPU cores can be costly due to server prices and lack of scalability.
  2. Using Kubernetes and KEDA for autoscaling GPU nodes can significantly reduce costs and improve scalability.
  3. Implementing cost-optimized ML on production can be achieved by using K8s and autoscaling GPU nodes, resulting in substantial cost savings.