Summary
In this episode of the AI Engineering Podcast Ron Green, co-founder and CTO of KungFu AI, talks about the evolving landscape of AI systems and the challenges of harnessing generative AI engines. Ron shares his insights on the limitations of large language models (LLMs) as standalone solutions and emphasizes the need for human oversight, multi-agent systems, and robust data management to support AI initiatives. He discusses the potential of domain-specific AI solutions, RAG approaches, and mixture of experts to enhance AI capabilities while addressing risks. The conversation also explores the evolving AI ecosystem, including tooling and frameworks, strategic planning, and the importance of interpretability and control in AI systems. Ron expresses optimism about the future of AI, predicting significant advancements in the next 20 years and the integration of AI capabilities into everyday software applications.


Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• Seamless data integration into AI applications often falls short, leading many to adopt RAG methods, which come with high costs, complexity, and limited scalability. Cognee offers a better solution with its open-source semantic memory engine that automates data ingestion and storage, creating dynamic knowledge graphs from your data. Cognee enables AI agents to understand the meaning of your data, resulting in accurate responses at a lower cost. Take full control of your data in LLM apps without unnecessary overhead. Visit aiengineeringpodcast.com/cognee to learn more and elevate your AI apps and agents. 
• Your host is Tobias Macey and today I'm interviewing Ron Green about the wheels that we need for harnessing the power of the generative AI engine

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what you see as the main shortcomings of LLMs as a stand-alone solution (to anything)?
• The most established vehicle for harnessing LLM capabilities is the RAG pattern. What are the main limitations of that as a "product" solution?
• The idea of multi-agent or mixture-of-experts systems is a more sophisticated approach that is gaining some attention. What do you see as the pro/con conversation around that pattern?
• Beyond the system patterns that are being developed there is also a rapidly shifting ecosystem of frameworks, tools, and point solutions that plugin to various points of the AI lifecycle. How does that volatility hinder the adoption of generative AI in different contexts?
  • In addition to the tooling, the models themselves are rapidly changing. How much does that influence the ways that organizations are thinking about whether and when to test the waters of AI?
• Continuing on the metaphor of LLMs and engines and the need for vehicles, where are we on the timeline in relation to the model T Ford?
  • What are the vehicle categories that we still need to design and develop? (e.g. sedans, mini-vans, freight trucks, etc.)
• The current transformer architecture is starting to reach scaling limits that lead to diminishing returns. Given your perspective as an industry veteran, what are your thoughts on the future trajectory of AI model architectures?
  • What is the ongoing role of regression style ML in the landscape of generative AI?
• What are the most interesting, innovative, or unexpected ways that you have seen LLMs used to power a "vehicle"?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working in this phase of AI?
• When is generative AI/LLMs the wrong choice?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

• Kungfu.ai
• Llama open generative AI models
• ChatGPT
• Copilot
• Cursor
• RAG == Retrieval Augmented Generation
  • Podcast Episode
• Mixture of Experts
• Deep Learning
• Random Forest
• Supervised Learning
• Active Learning)
• Yann LeCunn
• RLHF == Reinforcement Learning from Human Feedback
• Model T Ford
• Mamba selective state space
• Liquid Network
• Chain of thought
• OpenAI o1
• Marvin Minsky
• Von Neumann Architecture
• Attention Is All You Need
• Multilayer Perceptron
• Dot Product
• Diffusion Model
• Gaussian Noise
• AlphaFold 3
• Anthropic
• Sparse Autoencoder

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
In this episode of the AI Engineering Podcast Jim Olsen, CTO of ModelOp, talks about the governance of generative AI models and applications. Jim shares his extensive experience in software engineering and machine learning, highlighting the importance of governance in high-risk applications like healthcare. He explains that governance is more about the use cases of AI models rather than the models themselves, emphasizing the need for proper inventory and monitoring to ensure compliance and mitigate risks. The conversation covers challenges organizations face in implementing AI governance policies, the importance of technical controls for data governance, and the need for ongoing monitoring and baselines to detect issues like PII disclosure and model drift. Jim also discusses the balance between innovation and regulation, particularly with evolving regulations like those in the EU, and provides valuable perspectives on the current state of AI governance and the need for robust model lifecycle management.


Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• Your host is Tobias Macey and today I'm interviewing Jim Olsen about governance of your generative AI models and applications

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what governance means in the context of generative AI models? (e.g. governing the models, their applications, their outputs, etc.)
• Governance is typically a hybrid endeavor of technical and organizational policy creation and enforcement. From the organizational perspective, what are some of the difficulties that teams are facing in understanding what those policies need to encompass?
  • How much familiarity with the capabilities and limitations of the models is necessary to engage productively with policy debates?
• The regulatory landscape around AI is still very nascent. Can you give an overview of the current state of legal burden related to AI?
  • What are some of the regulations that you consider necessary but as-of-yet absent?
• Data governance as a practice typically relates to controls over who can access what information and how it can be used. The controls for those policies are generally available in the data warehouse, business intelligence, etc. What are the different dimensions of technical controls that are needed in the application of generative AI systems?
  • How much of the controls that are present for governance of analytical systems are applicable to the generative AI arena?
• What are the elements of risk that change when considering internal vs. consumer facing applications of generative AI?
  • How do the modalities of the AI models impact the types of risk that are involved? (e.g. language vs. vision vs. audio)
• What are some of the technical aspects of the AI tools ecosystem that are in greatest need of investment to ease the burden of risk and validation of model use?
• What are the most interesting, innovative, or unexpected ways that you have seen AI governance implemented?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI governance?
• What are the technical, social, and organizational trends of AI risk and governance that you are monitoring?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

• ModelOp
• Foundation Models
• GDPR
• EU AI Regulation
• Llama 2
• AWS Bedrock
• Shadow IT
• RAG == Retrieval Augmented Generation
  • Podcast Episode
• Nvidia NEMO
• LangChain
• Shapley Values
• Gibberish Detection

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
In this episode of the AI Engineering Podcast, Vasilije Markovich talks about enhancing Large Language Models (LLMs) with memory to improve their accuracy. He discusses the concept of memory in LLMs, which involves managing context windows to enhance reasoning without the high costs of traditional training methods. He explains the challenges of forgetting in LLMs due to context window limitations and introduces the idea of hierarchical memory, where immediate retrieval and long-term information storage are balanced to improve application performance. Vasilije also shares his work on Cognee, a tool he's developing to manage semantic memory in AI systems, and discusses its potential applications beyond its core use case. He emphasizes the importance of combining cognitive science principles with data engineering to push the boundaries of AI capabilities and shares his vision for the future of AI systems, highlighting the role of personalization and the ongoing development of Cognee to support evolving AI architectures.


Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• Your host is Tobias Macey and today I'm interviewing Vasilije Markovic about adding memory to LLMs to improve their accuracy

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what "memory" is in the context of LLM systems?
• What are the symptoms of "forgetting" that manifest when interacting with LLMs?
  • How do these issues manifest between single-turn vs. multi-turn interactions?
• How does the lack of hierarchical and evolving memory limit the capabilities of LLM systems?
• What are the technical/architectural requirements to add memory to an LLM system/application?
• How does Cognee help to address the shortcomings of current LLM/RAG architectures?
• Can you describe how Cognee is implemented?
  • Recognizing that it has only existed for a short time, how have the design and scope of Cognee evolved since you first started working on it?
• What are the data structures that are most useful for managing the memory structures?
• For someone who wants to incorporate Cognee into their LLM architecture, what is involved in integrating it into their applications?
  • How does it change the way that you think about the overall requirements for an LLM application?
• For systems that interact with multiple LLMs, how does Cognee manage context across those systems? (e.g. different agents for different use cases)
• There are other systems that are being built to manage user personalization in LLm applications, how do the goals of Cognee relate to those use cases? (e.g. Mem0 - https://github.com/mem0ai/mem0)
• What are the unknowns that you are still navigating with Cognee?
• What are the most interesting, innovative, or unexpected ways that you have seen Cognee used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Cognee?
• When is Cognee the wrong choice?
• What do you have planned for the future of Cognee?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

• Cognee
• Montenegro
• Catastrophic Forgetting
• Multi-Turn Interaction
• RAG == Retrieval Augmented Generation
  • Podcast Episode
• GraphRAG
  • Podcast Episode
• Long-term memory
• Short-term memory
• Langchain
• LlamaIndex
• Haystack
• dlt
  • Data Engineering Podcast Episode
• Pinecone
  • Podcast Episode
• Agentic RAG
• Airflow
• DAG == Directed Acyclic Graph
• FalkorDB
• Neo4J
• Pydantic
• AWS ECS
• AWS SNS
• AWS SQS
• AWS Lambda
• LLM As Judge
• Mem0
• QDrant
• LanceDB
• DuckDB

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
In this episode of the AI Engineering Podcast, Tanner Burson, VP of Engineering at Prismatic, talks about the evolving impact of generative AI on software developers. Tanner shares his insights from engineering leadership and data engineering initiatives, discussing how AI is blurring the lines of developer roles and the strategic value of AI in software development. He explores the current landscape of AI tools, such as GitHub's Copilot, and their influence on productivity and workflow, while also touching on the challenges and opportunities presented by AI in code generation, review, and tooling. Tanner emphasizes the need for human oversight to maintain code quality and security, and offers his thoughts on the future of AI in development, the importance of balancing innovation with practicality, and the evolving role of engineers in an AI-driven landscape.


Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• Your host is Tobias Macey and today I'm interviewing Tanner Burson about the impact of generative AI on software developers

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what types of roles and work you consider encompassed by the term "developers" for the purpose of this conversation?
• How does your work at Prismatic give you visibility and insight into the effects of AI on developers and their work?
• There have been many competing narratives about AI and how much of the software development process it is capable of encompassing. What is your top-level view on what the long-term impact on the job prospects of software developers will be as a result of generative AI?
• There are many obvious examples of utilities powered by generative AI that are focused on software development. What do you see as the categories or specific tools that are most impactful to the development cycle?
• In what ways do you find familiarity with/understanding of LLM internals useful when applying them to development processes?
• As an engineering leader, how are you evaluating and guiding your team on the use of AI powered tools?
  • What are some of the risks that you are guarding against as a result of AI in the development process?
• What are the most interesting, innovative, or unexpected ways that you have seen AI used in the development process?
• What are the most interesting, unexpected, or challenging lessons that you have learned while using AI for software development?
• When is AI the wrong choice for a developer?
• What are your projections for the near to medium term impact on the developer experience as a result of generative AI?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

• Prismatic
• Google AI Development announcement
• Tabnine
  • Podcast Episode
• GitHub Copilot
• Plandex
• OpenAI API
• Amazon Q
• Ollama
• Huggingface Transformers
• Anthropic
• Langchain
• Llamaindex
• Haystack
• Llama 3.2
• Qwen2.5-Coder

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
Machine learning workflows have long been complex and difficult to operationalize. They are often characterized by a period of research, resulting in an artifact that gets passed to another engineer or team to prepare for running in production. The MLOps category of tools have tried to build a new set of utilities to reduce that friction, but have instead introduced a new barrier at the team and organizational level. Donny Greenberg took the lessons that he learned on the PyTorch team at Meta and created Runhouse. In this episode he explains how, by reducing the number of opinions in the framework, he has also reduced the complexity of moving from development to production for ML systems.


Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• Your host is Tobias Macey and today I'm interviewing Donny Greenberg about Runhouse and the current state of ML infrastructure

Interview

• Introduction
• How did you get involved in machine learning?
• What are the core elements of infrastructure for ML and AI?
  • How has that changed over the past ~5 years?
  • For the past few years the MLOps and data engineering stacks were built and managed separately. How does the current generation of tools and product requirements influence the present and future approach to those domains?
• There are numerous projects that aim to bridge the complexity gap in running Python and ML code from your laptop up to distributed compute on clouds (e.g. Ray, Metaflow, Dask, Modin, etc.). How do you view the decision process for teams trying to understand which tool(s) to use for managing their ML/AI developer experience?
• Can you describe what Runhouse is and the story behind it?
  • What are the core problems that you are working to solve?
  • What are the main personas that you are focusing on? (e.g. data scientists, DevOps, data engineers, etc.)
  • How does Runhouse factor into collaboration across skill sets and teams?
• Can you describe how Runhouse is implemented?
  • How has the focus on developer experience informed the way that you think about the features and interfaces that you include in Runhouse?
• How do you think about the role of Runhouse in the integration with the AI/ML and data ecosystem?
• What does the workflow look like for someone building with Runhouse?
• What is involved in managing the coordination of compute and data locality to reduce networking costs and latencies?
• What are the most interesting, innovative, or unexpected ways that you have seen Runhouse used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Runhouse?
• When is Runhouse the wrong choice?
• What do you have planned for the future of Runhouse?
• What is your vision for the future of infrastructure and developer experience in ML/AI?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

• Runhouse
  • GitHub
• PyTorch
  • Podcast.__init__ Episode
• Kubernetes
• Bin Packing
• Linear Regression
• Gradient Boosted Decision Tree
• Deep Learning
• Transformer Architecture)
• Slurm
• Sagemaker
• Vertex AI
• Metaflow
  • Podcast.__init__ Episode
• MLFlow
• Dask
  • Data Engineering Podcast Episode
• Ray
  • Podcast.__init__ Episode
• Spark
• Databricks
• Snowflake
• ArgoCD
• PyTorch Distributed
• Horovod
• Llama.cpp
• Prefect
  • Data Engineering Podcast Episode
• Airflow
• OOM == Out of Memory
• Weights and Biases
• KNative
• BERT language model

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
With the growth of vector data as a core element of any AI application comes the need to keep those vectors up to date. When you go beyond prototypes and into production you will need a way to continue experimenting with new embedding models, chunking strategies, etc. You will also need a way to keep the embeddings up to date as your data changes. The team at Timescale created the pgai Vectorizer toolchain to let you manage that work in your Postgres database. In this episode Avthar Sewrathan explains how it works and how you can start using it today.


Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• Your host is Tobias Macey and today I'm interviewing Avthar Sewrathan about the pgai extension for Postgres and how to run your AI workflows in your database

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what pgai Vectorizer is and the story behind it?
• What are the benefits of using the database engine to execute AI workflows?
  • What types of operations does pgai Vectorizer enable?
  • What are some common generative AI patterns that can't be done with pgai?
• AI applications require a large and complex set of dependencies. How does that work with pgai Vectorizer and the Python runtime in Postgres?
  • What are some of the other challenges or system pressures that are introduced by running these AI workflows in the database context?
• Can you describe how the pgai extension is implemented?
• With the rapid pace of change in the AI ecosystem, how has that informed the set of features that make sense in pgai Vectorizer and won't require rebuilding in 6 months?
• Can you describe the workflow of using pgai Vectorizer to build and maintain a set of embeddings in their database?
  • How can pgai Vectorizer help with the situation of migrating to a new embedding model and having to reindex all of the content?
• How do you think about the developer experience for people who are working with pgai Vectorizer, as compared to using e.g. LangChain, LlamaIndex, etc.?
• What are the most interesting, innovative, or unexpected ways that you have seen pgai Vectorizer used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on pgai Vectorizer?
• When is pgai Vectorizer the wrong choice?
• What do you have planned for the future of pgai Vectorizer?

Contact Info

• LinkedIn
• Website

Parting Question

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

• Timescale
• pgai
• Transformer architecture for deep learning
• Neural Networks
• pgvector
• pgvectorscale
• Modal
• RAG == Retrieval Augmented Generation
• Semantic Search
• Ollama
• GraphRAG
• agensgraph
• LangChain
• LlamaIndex
• Haystack
• IVFFlat
• HNSW
• DiskANN
• Repl.it Agent
• BM25
• TSVector
• ParadeDB

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
In this episode Philip Kiely from BaseTen talks about the intricacies of running open models in production. Philip shares his journey into AI and ML engineering, highlighting the importance of understanding product-level requirements and selecting the right model for deployment. The conversation covers the operational aspects of deploying AI models, including model evaluation, compound AI, and model serving frameworks such as TensorFlow Serving and AWS SageMaker. Philip also discusses the challenges of model quantization, rapid model evolution, and monitoring and observability in AI systems, offering valuable insights into the future trends in AI, including local inference and the competition between open source and proprietary models.


Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• Your host is Tobias Macey and today I'm interviewing Philip Kiely about running open models in production

Interview

• Introduction
• How did you get involved in machine learning?
• Can you start by giving an overview of the major decisions to be made when planning the deployment of a generative AI model?
• How does the model selected in the beginning of the process influence the downstream choices?
• In terms of application architecture, the major patterns that I've seen are RAG, fine-tuning, multi-agent, or large model. What are the most common methods that you see? (and any that I failed to mention)
  • How have the rapid succession of model generations impacted the ways that teams think about their overall application? (capabilities, features, architecture, etc.)
• In terms of model serving, I know that Baseten created Truss. What are some of the other notable options that teams are building with?
  • What is the role of the serving framework in the context of the application?
• There are also a large number of inference engines that have been released. What are the major players in that arena?
  • What are the features and capabilities that they are each basing their competitive advantage on?
• For someone who is new to AI Engineering, what are some heuristics that you would recommend when choosing an inference engine?
• Once a model (or set of models) is in production and serving traffic it's necessary to have visibility into how it is performing. What are the key metrics that are necessary to monitor for generative AI systems?
  • In the event that one (or more) metrics are trending negatively, what are the levers that teams can pull to improve them?
• When running models constructed with e.g. linear regression or deep learning there was a common issue with "concept drift". How does that manifest in the context of large language models, particularly when coupled with performance optimization?
• What are the most interesting, innovative, or unexpected ways that you have seen teams manage the serving of open gen AI models?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working with generative AI model serving?
• When is Baseten the wrong choice?
• What are the future trends and technology investments that you are focused on in the space of AI model serving?

Contact Info

• LinkedIn
• Twitter

Parting Question

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

• Baseten
  • Podcast Episode
• Copyleft
• Llama Models
• Nomic
• Olmo
• Allen Institute for AI
• Playground 2
• The Peace Dividend Of The SaaS Wars
• Vercel
• Netlify
• RAG == Retrieval Augmented Generation
  • Podcast Episode
• Compound AI
• Langchain
• Outlines Structured output for AI systems
• Truss
• Chains
• Llamaindex
• Ray
• MLFlow
• Cog (Replicate) containers for ML
• BentoML
• Django
• WSGI
• uWSGI
• Gunicorn
• Zapier
• vLLM
• TensorRT-LLM
• TensorRT
• Quantization
• LoRA Low Rank Adaptation of Large Language Models
• Pruning
• Distillation
• Grafana
• Speculative Decoding
• Groq
• Runpod
• Lambda Labs

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
In this episode of the AI Engineering podcast, Philip Rathle, CTO of Neo4J, talks about the intersection of knowledge graphs and AI retrieval systems, specifically Retrieval Augmented Generation (RAG). He delves into GraphRAG, a novel approach that combines knowledge graphs with vector-based similarity search to enhance generative AI models. Philip explains how GraphRAG works by integrating a graph database for structured data storage, providing more accurate and explainable AI responses, and addressing limitations of traditional retrieval systems. The conversation covers technical aspects such as data modeling, entity extraction, and ontology use cases, as well as the infrastructure and workflow required to support GraphRAG, setting the stage for innovative applications across various industries.

Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• Your host is Tobias Macey and today I'm interviewing Philip Rathle about the application of knowledge graphs in AI retrieval systems

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what GraphRAG is?
  • What are the capabilities that graph structures offer beyond vector/similarity-based retrieval methods of prompting?
• What are some examples of the ways that semantic limitations of nearest-neighbor vector retrieval fail to provide relevant results?
• What are the technical requirements to implement graph-augmented retrieval?
  • What are the concrete ways in which the embedding and retrieval steps of a typical RAG pipeline need to be modified to account for the addition of the graph?
• Many tutorials for building vector-based knowledge repositories skip over considerations around data modeling. For building a graph-based knowledge repository there obviously needs to be a bit more work put in. What are the key design choices that need to be made for implementing the graph for an AI application?
  • How does the selection of the ontology/taxonomy impact the performance and capabilities of the resulting application?
• Building a fully functional knowledge graph can be a significant undertaking on its own. How can LLMs and AI models help with the construction and maintenance of that knowledge repository?
  • What are some of the validation methods that should be brought to bear to ensure that the resulting graph properly represents the knowledge domain that you are trying to model?
• Vector embedding and retrieval are a core building block for a majority of AI application frameworks. How much support do you see for GraphRAG in the ecosystem?
  • For the case where someone is using a framework that does not explicitly implement GraphRAG techniques, what are some of the implementation strategies that you have seen be most effective for adding that functionality?
• What are some of the ways that the combination of vector search and knowledge graphs are useful independent of their combination with language models?
• What are the most interesting, innovative, or unexpected ways that you have seen GraphRAG used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on GraphRAG applications?
• When is GraphRAG the wrong choice?
• What are the opportunities for improvement in the design and implementation of graph-based retrieval systems?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

• Neo4J
• GraphRAG Manifesto
• RAG == Retrieval Augmented Generation
  • Podcast Episode
• VLDB == Very Large DataBases
• Knowledge Graph
• Nearest Neighbor Search
• PageRank
• Things Not Strings) Google Knowledge Graph Paper
• pgvector
• Pinecone
  • Data Engineering Podcast Episode
• Tables To Labels
• NLP == Natural Language Processing
• Ontology
• LangChain
• LlamaIndex
• RLHF == Reinforcement Learning with Human Feedback
• Senzing
• NeoConverse
• Cypher query language
• GQL query standard
• AWS Bedrock
• Vertex AI
• Sequoia Training Data - Klarna episode
• Ouroboros

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
In this episode of the AI Engineering podcast Praveen Gujar, Director of Product at LinkedIn, talks about the applications of generative AI in digital advertising. He highlights the key areas of digital advertising, including audience targeting, content creation, and ROI measurement, and delves into how generative AI is revolutionizing these aspects. Praveen shares successful case studies of generative AI in digital advertising, including campaigns by Heinz, the Barbie movie, and Maggi, and discusses the potential pitfalls and risks associated with AI-powered tools. He concludes with insights into the future of generative AI in digital advertising, highlighting the importance of cultural transformation and the synergy between human creativity and AI.
Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• Your host is Tobias Macey and today I'm interviewing Praveen Gujar about the applications of generative AI in digital advertising

Interview

• Introduction
• How did you get involved in machine learning?
• Can you start by defining "digital advertising" for the scope of this conversation?
  • What are the key elements/characteristics/goals of digital avertising?
• In the world before generative AI, what did a typical end-to-end advertising campaign workflow look like?
  • What are the stages of that workflow where generative AI are proving to be most useful?
    • How do the current limitations of generative AI (e.g. hallucinations, non-determinism) impact the ways in which they can be used?
• What are the technological and organizational systems that need to be implemented to effectively apply generative AI in public-facing applications that are so closely tied to brand/company image?
  • What are the elements of user education/expectation setting that are necessary when working with marketing/advertising personnel to help avoid damage to the brands?
• What are some examples of applications for generative AI in digital advertising that have gone well?
  • Any that have gone wrong?
• What are the most interesting, innovative, or unexpected ways that you have seen generative AI used in digital advertising?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on digital advertising applications of generative AI?
• When is generative AI the wrong choice?
• What are your future predictions for the use of generative AI in dgital advertising?

Contact Info

• Website
• LinkedIn

Parting Question

• From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

• Generative AI
• LLM == Large Language Model
• Dall-E)
• RLHF == Reinforcement Learning fHuman Feedback

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary
In this episode of the AI Engineering podcast, host Tobias Macy interviews Tammer Saleh, founder of SuperOrbital, about the potentials and pitfalls of using Kubernetes for machine learning workloads. The conversation delves into the specific needs of machine learning workflows, such as model tracking, versioning, and the use of Jupyter Notebooks, and how Kubernetes can support these tasks. Tammer emphasizes the importance of a unified API for different teams and the flexibility Kubernetes provides in handling various workloads. Finally, Tammer offers advice for teams considering Kubernetes for their machine learning workloads and discusses the future of Kubernetes in the ML ecosystem, including areas for improvement and innovation.
Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• Your host is Tobias Macey and today I'm interviewing Tammer Saleh about the potentials and pitfalls of using Kubernetes for your ML workloads.

Interview

• Introduction
• How did you get involved in Kubernetes?
• For someone who is unfamiliar with Kubernetes, how would you summarize it?
• For the context of this conversation, can you describe the different phases of ML that we're talking about?
• Kubernetes was originally designed to handle scaling and distribution of stateless processes. ML is an inherently stateful problem domain. What challenges does that add for K8s environments?
• What are the elements of an ML workflow that lend themselves well to a Kubernetes environment?
• How much Kubernetes knowledge does an ML/data engineer need to know to get their work done?
• What are the sharp edges of Kubernetes in the context of ML projects?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working with Kubernetes?
• When is Kubernetes the wrong choice for ML?
• What are the aspects of Kubernetes (core or the ecosystem) that you are keeping an eye on which will help improve its utility for ML workloads?

Contact Info

• Email
• LinkedIn

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for ML workloads today?

Links

• SuperOrbital
• CloudFoundry
• Heroku
• 12 Factor Model
• Kubernetes
• Docker Compose
• Core K8s Class
• Jupyter Notebook
• Crossplane
• Ochre Jelly
• CNCF (Cloud Native Computing Foundation) Landscape
• Stateful Set
• RAG == Retrieval Augmented Generation
  • Podcast Episode
• Kubeflow
• Flyte
  • Data Engineering Podcast Episode
• Pachyderm
  • Data Engineering Podcast Episode
• CoreWeave
• Kubectl ("koob-cuddle")
• Helm
• CRD == Custom Resource Definition
• Horovod
  • Podcast.__init__ Episode
• Temporal
• Slurm
• Ray
• Dask
• Infiniband

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA