This is part 1 of a 3 part series focused on looking at the current state of lakehouse formats. Don’t miss part 2 where we analyze community health and part 3 where we look at key trends.
This post explores Ericsson's extensive experience with AI, from optimizing network data and performance to addressing security challenges in telecom. Discover how Ericsson uses AI to predict vulnerabilities, fight fraud, and protect telecom infrastructure and privacy.
This analysis tackles these challenges and offers solutions, using advances in language models and interdisciplinary methods to push forward conversational AI.
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- Slow Inference Speed
- Difficulty Evaluating Quality
- Poisoning Attacks
- Backdoor Triggers
This post explores an efficient architecture combining Python and LangChain with OpenAI's LLM, Apache Kafka for data streaming, and Apache Flink for processing. Discover how it improves Salesforce CRM data by integrating public datasets from Google and LinkedIn and suggest ice-breaker conversations for sales reps.
This post deeply investigates boosting LLM outputs by weaving in contextually relevant data, aiming for better search and retrieval tasks. It highlights how this approach can elevate Salesforce CRM insights and sharpen AI response accuracy.
This tutorial explains how to make your version of Apache Flink to fit your needs. It talks about how to get started, keep your version up to date, and share your changes with others.
Read about graphs via large language models, comparing their benefits and drawbacks against vector databases. It discusses the application of knowledge graphs in sectors where relationships between entities are crucial for providing solutions to stakeholders.
Delve into the hybrid bulk data processing framework, designed for exceptional durability, observability, and scalability. It has effectively managed over 4,000 requests weekly for over five months. The discussion includes its support for over 15 entity types, blending offline and nearline entities for superior performance.
In the episode, Adel and Eric discuss why machine learning projects fail to reach production, introducing the BizML Framework to align business stakeholders with ML use cases, addressing the skills gap, exploring organizational use cases for operational improvement, and lessons from past ML hype cycles for generative AI.
In this episode you will learn:
• About Species X
• How to become a certified beer taster
• How Beau checks the quality of his beer
• Beau and Jon’s machine learning project
• About genetic algorithms
• How to get creativity out of LLMs
Adam Ferrari joins the show to talk about Starburst, data engineering, and what it takes to build a data lake.
Real-life experience and upright knowledge are the basis of all Infoshare speeches. We strive to fill both days of the conference and all five stages with inspiring content and expert data.
Join us and be part of the biggest tech festival in CEE!