Orchestrate all the Things George Anadiotis

From better together to full native integration, Neo4j is creating an ecosystem around all major cloud platforms to provide graph-powered features for Generative AI and beyond. 


As Neo4j just announced its partneship with Microsoft, we met with Chief Product Officer Sudhir Hasbe to talk about:


What this partnership means for users and how it worksHow graph-powered generative AI aligns with cloud platform AI strategiesSimilarities and differences across themHow Neo4j's strategy is shaping up, and when Databricks and Snowflake integration are coming.

For additional analysis and a writeup of the conversation, you can read the article published on Orchestrate all the Things: https://linkeddataorchestration.com/2024/03/27/neo4j-partners-with-microsoft-unfolds-strategy-to-power-generative-ai-applications-with-cloud-platforms-and-graph-rag/

If we look at the current status quo in AI as a case of demand and supply, what can we do to close the gap between the exponentially growing demand on the side of AI models and the linearly growing supply on the side of AI hardware?


This formulation was the premise on which Yonatan Geifman co-founded Deci in 2019. 


Today, with the generative AI explosion in full bloom, demand is growing faster than ever, and Deci is a part of this by contributing a number of open source models.


Join us as we explore:


How AI models are different than traditional software and what open source means in AIChoosing between GPT-4, Claude 3 and open source LLMsCustomizing LLMs and fine-tuning vs. RAGEvaluating LLMsMarket outlook

Article published on Orchestrate All the Things: https://linkeddataorchestration.com/2024/03/06/evaluating-and-building-applications-on-open-source-large-language-models/

There’s more to AI chips than NVIDIA: AMD, Intel, chiplets, upstarts, analog AI, optical computing, and AI chips designed by AI.


The interest and investment in AI is skyrocketing, and generative AI is fueling it. Over one-third of CxOs have reportedly already embraced GenAI in their operations, with nearly half preparing to invest in it.


What’s powering it all - AI chips - used to receive less attention. Up to the moment OpenAI’s Sam Altman claimed he wants to raise up to $7 trillion for a “wildly-ambitious” tech project to boost the world’s chip capacity. Geopolitics and sensationalism aside, however, keeping an eye on AI chips means being aware of today’s blockers and tomorrow’s opportunities.


According to a recent study by IMARC, the global AI chip market is expected to reach $89.6 Billion by 2029. The demand for AI chips has increased substantially over time. Growth in AI technology, rising demand for AI chips in consumer electronics and AI chip innovation all contribute to this forecast.


Few people have more insights to share on AI hardware than Tony Pialis, CEO and co-founder of Alphawave. In an extensive conversation, Pialis shared his insider’s perspective on the AI chip landscape, the transformative rise of chiplets, specialized hardware for training and inference, emerging directions like analog and optical computing, and much more.


Article published on Orchestrate All the Things: https://linkeddataorchestration.com/2024/02/13/the-future-of-ai-chips-leaders-dark-horses-and-rising-stars/

For many organizations today, data management comes down to handing over their data to one of the "Big 5" data vendors: Amazon, Microsoft Azure and Google, plus Snowflake and Databricks. 


But analysts David Vellante and George Gilbert believe that the needs of modern data applications coupled with the evolution of open storage management may lead to the emergence of a "sixth data platform".


The sixth data platform hypothesis is that open data formats may enable interoperability, leading the transition away from vertically integrated vendor-controlled platforms towards independent management of data storage and permissions.


It's an interesting scenario, and one that would benefit users by forcing vendors to compete for every workload based on the business value delivered, irrespective of lock-in. But how close are we to realizing this?


To answer this question, we have to examine open data formats and their interoperability potential across clouds and formats, as well as on the semantics and governance layer.


We caught up with Peter Corless and Alex Merced to talk about all of that.


Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2024/01/11/data-management-in-2024-open-data-formats-and-a-common-language-for-a-sixth-data-platform/

What is a skills-based economy and how is LinkedIn moving from vision to implementation?


As LinkedIn Director of Engineering Sofus Macskássy shares, there's AI, taxonomy, and ontology involved in building the Skills Graph that powers the transition.


We discuss the process of extracting skills from text, building a skills graph, and leveraging it for various product lines within LinkedIn.


We cover aspects related to explicit and implicit skill provenance, credibility, depth and interoperability.


Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2023/12/13/how-linkedin-is-moving-towards-a-skills-based-economy-with-the-skills-graph

Amazon Neptune, the managed graph database service by AWS, makes analytics faster and more agile while introducing a vision aiming to simplify graph databases.


It's not every day that you hear product leads questioning the utility of their own products. Brad Beebe, the general manager of Amazon Neptune, was all serious when he said that most customers don't actually want a graph database. However, that statement needs contextualization.


If Bebee had meant that in the literal sense, the team himself and Amazon Neptune Principal Product Manager Denise Gosnell lead would not have bothered developing and releasing a brand new analytics engine for their customers. We caught up with Bebee and Gosnell to discuss Amazon Neptune new features and the broader vision. 


We cover where Amazon Neptune fits in the AWS vision of data management, and how the new analytics engine provides a single service for graph workloads, high performance for graph analytic queries and graph algorithms, and vector store and search capabilities for Generative AI applications. We also share insights on the One Graph vision, the road from serverless to One Graph via HPC, as well as vectors and Graph AI.


Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2023/11/29/amazon-neptune-introduces-a-new-analytics-engine-and-the-one-graph-vision/


00:00:00 Introduction


00:01:44 Amazon Neptune & AWS vision of data management


00:05:35 The Importance of Graph Databases  


00:08:55 Amazon Neptune Use Cases


00:13:13 Introduction to Amazon Neptune Analytics  


00:15:20 Key Features of Neptune Analytics  


00:17:40 Use Cases for Neptune Analytics 


00:21:10 Preparing Data for Generative AI Applications


00:23:37 Neptune Analytics Use Cases and Deployment


00:26:43 Pricing and Roadmap Q&A


00:48:46 Conclusion