rabbit food

A bunch of Elasticians working together

A bunch of Elasticians talking Elasticsearch and all of the IT tech surrounding it #elastic #elasticsearch #kibana #elk read less
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Episodios

Training a SOC Team with Andrue McElhaney
Hace 4 días
Training a SOC Team with Andrue McElhaney
Summary In this conversation, Andrue McElhaney, a Senior Solution Architect at Elastic, discusses his journey in technology and his passion for training. He emphasizes the importance of hands-on experience and practical learning in becoming an expert. He also shares insights on learning Elastic and transitioning to the platform, highlighting the value of starting small, leveraging the community, and finding consistency. Andrue concludes with a key takeaway: start with a project, build a team, and find your own path to success Takeaways Hands-on experience and practical learning are crucial in becoming an expert in technology.When learning Elastic, start small and leverage free cloud trials to get hands-on experience with the platform.Engage with the Elastic community through forums, meetups, and conferences to learn from others' experiences.Building consistency and finding a training model that works for your organization is key to successful transition to Elastic.Start with a project, build a team, and focus on continuous learning and improvement. Learning Elastic: Starting Small and Leveraging the CommunityKey Takeaways: Starting with a Project and Continuous Learning "Hands-on experience with your stuff... It's great for that muscle memory.""Start with the free cloud trials... See the functionality for yourself.""Automation and orchestration... Spin things up quickly and tear them down Titles 01:18 Andrue's Journey: From Video Games to Elastic Expert 10:16Training and Transitioning to Elastic 14:24The Role of DevOps in Technology Training and Deployment 21:56Utilizing the Elastic Community and Resources 26:23Andrue's Advice for Learning Elastic
How ML has changed in Security and what it is about to do
13-06-2024
How ML has changed in Security and what it is about to do
Summary The conversation covers topics related to creating chapters, endpoint security, machine learning, XDR, EDR, kernel, supply chain security, and zero trust in Windows environment. The discussion also delves into the evolution of security culture in the US military and the challenges faced in implementing Zero Trust. The conversation provides insights into the depth of detail required to defend against advanced adversaries and the importance of training and experience in cybersecurity.Keywordschapters, endpoint security, machine learning, XDR, EDR, kernel, supply chain security, zero trust, US military, cybersecurity cultureTakeaways The evolution of endpoint security and the challenges posed by advanced attacksThe role of machine learning in enhancing endpoint and extended detection and response (XDR) solutions The complexities and challenges of implementing Zero Trust in a Windows environment and the need to define a clear framework for protectionThe evolution of security culture in the US military and the emphasis on training and experience in cybersecurityThe depth of detail required to defend against advanced adversaries and the importance of understanding supply chain vulnerabilities The Significance of the Kernel in CybersecurityImpact of Supply Chain Security on Cybersecurity Posture "I always appreciate kind of the spy versus spy analogy because as soon as you come up with a good defense, attackers come up with another attack.""I feel like there's a change happening in the security world from audit D type logging type security to XDR.""The kernel is a key component for cybersecurity."
Aircraft Data in Elastic
13-06-2024
Aircraft Data in Elastic
Elastic plays a crucial role in the aviation industry, particularly in areas such as operational energy, fuel efficiency, supply chain management, manufacturing, and cybersecurity. By analyzing data from aircraft engines and sensors, Elastic can help optimize fuel consumption, identify regional fuel usage patterns, and suggest improvements in efficiency. It can also assist in monitoring and securing the fuel supply chain, ensuring the quality and safety of the fuel. In manufacturing, Elastic can analyze data from the production process, identify efficiencies, and enable predictive analysis. Additionally, Elastic can be used to track and manage logistics in air transport, such as tracking food supplies and ensuring their safe delivery. Takeaways Elastic is used in the aviation industry to optimize fuel consumption and improve operational energy efficiency.It can analyze data from aircraft engines and sensors to identify regional fuel usage patterns and suggest improvements.Elastic helps monitor and secure the fuel supply chain, ensuring the quality and safety of the fuel.In manufacturing, Elastic enables analysis of production data, identification of efficiencies, and predictive analysis.It can track and manage logistics in air transport, such as tracking food supplies and ensuring their safe delivery. Sound Bites "Operational energy: Analyzing fuel consumption and energy resources in the Air Force.""Fuel supply chain: Monitoring and securing the quality and safety of fuel.""Manufacturing efficiency: Analyzing production data and enabling predictive analysis." Chapters 00:00Fuel Efficiency and Operational Performance 09:34Unstructured Data and Elastic 22:52Cybersecurity in the Airplane World 28:18Logistics and Supply Chain Management Keywords elastic, aviation industry, operational energy, fuel efficiency, supply chain management, manufacturing, cybersecurity, fuel consumption, regional fuel usage, efficiency improvements, fuel supply chain, manufacturing process, predictive analysis, logistics, air transport, food supplies
LLMs need search
05-06-2024
LLMs need search
Summary LLMs and vector databases are powerful tools in information retrieval, but they still need a search engine to perform optimally. Vectors provide predictions based on the most likely context within the vector space, but without additional context, the interpretation can be difficult. LLMs understand language patterns and allow for semantic search without exact terms. Vector databases use coordinates to find content matches and determine relevance, but they lack the user's context. Elasticsearch as a vector database allows for additional context and combines multiple search modalities for better results. Keywords: LLMs, vector databases, search engine, information retrieval, context, semantic search, relevance, ElasticsearchTakeaways LLMs and vector databases need a search engine to perform optimallyVectors provide predictions based on the most likely context within the vector spaceLLMs allow for semantic search without exact termsVector databases lack the user's context, which affects relevanceElasticsearch as a vector database allows for additional context and combines multiple search modalities Understanding Context in Information RetrievalThe Power of Elasticsearch as a Vector Database "LLMs and vectors databases and vector search and retrieval augmented generation, all the above, still need a search engine to perform to their optimal accuracy and efficiency.""LLMs are trained on a large amount of content, so they understand the patterns of language usage.""With Elasticsearch as your vector database, you can vectorize your content using third-party models and then bring to bear your additional context that LLMs don't have any knowledge of." Chapters 00:00 The Role of Search Engines in Optimizing LLMs and Vector Databases 02:16 Limitations of Vector Databases and the Need for Additional Context 04:12 Elasticsearch: A Superior Vector Database with Multiple Search Modalities