AI Engineering Podcast

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

Summary
In this episode we're joined by Matt Zeiler, founder and CEO of Clarifai, as he dives into the technical aspects of retrieval augmented generation (RAG). From his journey into AI at the University of Toronto to founding one of the first deep learning AI companies, Matt shares his insights on the evolution of neural networks and generative models over the last 15 years. He explains how RAG addresses issues with large language models, including data staleness and hallucinations, by providing dynamic access to information through vector databases and embedding models. Throughout the conversation, Matt and host Tobias Macy discuss everything from architectural requirements to operational considerations, as well as the practical applications of RAG in industries like intelligence, healthcare, and finance. Tune in for a comprehensive look at RAG and its future trends in 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 Matt Zeiler, Founder & CEO of Clarifai, about the technical aspects of RAG, including the architectural requirements, edge cases, and evolutionary characteristics

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you describe what RAG (Retrieval Augmented Generation) is?
• What are the contexts in which you would want to use RAG?
• What are the alternatives to RAG?
• What are the architectural/technical components that are required for production grade RAG?
• Getting a quick proof-of-concept working for RAG is fairly straightforward. What are the failures modes/edge cases that start to surface as you scale the usage and complexity?
• The first step of building the corpus for RAG is to generate the embeddings. Can you talk through the planning and design process? (e.g. model selection for embeddings, storage capacity/latency, etc.)
• How does the modality of the input/output affect this and downstream decisions? (e.g. text vs. image vs. audio, etc.)
• What are the features of a vector store that are most critical for RAG?
• The set of available generative models is expanding and changing at breakneck speed. What are the foundational aspects that you look for in selecting which model(s) to use for the output?
• Vector databases have been gaining ground for search functionality, even without generative AI. What are some of the other ways that elements of RAG can be re-purposed?
• What are the most interesting, innovative, or unexpected ways that you have seen RAG used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on RAG?
• When is RAG the wrong choice?
• What are the main trends that you are following for RAG and its component elements going forward?

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

• Clarifai
• Geoff Hinton
• Yann Lecun
• Neural Networks
• Deep Learning
• Retrieval Augmented Generation
• Context Window
• Vector Database
• Prompt Engineering
• Mistral
• Llama 3
• Embedding Quantization
• Active Learning
• Google Gemini
• AI Model Attention
• Recurrent Network
• Convolutional Network
• Reranking Model
• Stop Words
• Massive Text Embedding Benchmark (MTEB)
• Retool State of AI Report
• pgvector
• Milvus
• Qdrant
• Pinecone
• OpenLLM Leaderboard
• Semantic Search
• Hashicorp

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

Summary
Artificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains his approach to building AI in a more human-like fashion and the emphasis on learning rather than statistical prediction.

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 Peter Voss about what is involved in making your AI applications more "human"

Interview

• Introduction
• How did you get involved in machine learning?
• Can you start by unpacking the idea of "human-like" AI?
• How does that contrast with the conception of "AGI"?
• The applications and limitations of GPT/LLM models have been dominating the popular conversation around AI. How do you see that impacting the overrall ecosystem of ML/AI applications and investment?
• The fundamental/foundational challenge of every AI use case is sourcing appropriate data. What are the strategies that you have found useful to acquire, evaluate, and prepare data at an appropriate scale to build high quality models? 
• What are the opportunities and limitations of causal modeling techniques for generalized AI models?
• As AI systems gain more sophistication there is a challenge with establishing and maintaining trust. What are the risks involved in deploying more human-level AI systems and monitoring their reliability?
• What are the practical/architectural methods necessary to build more cognitive AI systems?
• How would you characterize the ecosystem of tools/frameworks available for creating, evolving, and maintaining these applications?
• What are the most interesting, innovative, or unexpected ways that you have seen cognitive AI applied?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on desiging/developing cognitive AI systems?
• When is cognitive AI the wrong choice?
• What do you have planned for the future of cognitive AI applications at Aigo?

Contact Info

• LinkedIn
• Website

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

• Aigo.ai
• Artificial General Intelligence
• Cognitive AI
• Knowledge Graph
• Causal Modeling
• Bayesian Statistics
• Thinking Fast & Slow by Daniel Kahneman (affiliate link)
• Agent-Based Modeling
• Reinforcement Learning
• DARPA 3 Waves of AI presentation
• Why Don't We Have AGI Yet? whitepaper
• Concepts Is All You Need Whitepaper
• Hellen Keller
• Stephen Hawking

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

Summary
Generative AI promises to accelerate the productivity of human collaborators. Currently the primary way of working with these tools is through a conversational prompt, which is often cumbersome and unwieldy. In order to simplify the integration of AI capabilities into developer workflows Tsavo Knott helped create Pieces, a powerful collection of tools that complements the tools that developers already use. In this episode he explains the data collection and preparation process, the collection of model types and sizes that work together to power the experience, and how to incorporate it into your workflow to act as a second brain.


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 Tsavo Knott about Pieces, a personal AI toolkit to improve the efficiency of developers

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what Pieces is and the story behind it?
• The past few months have seen an endless series of personalized AI tools launched. What are the features and focus of Pieces that might encourage someone to use it over the alternatives?
• model selections
• architecture of Pieces application
• local vs. hybrid vs. online models
• model update/delivery process
• data preparation/serving for models in context of Pieces app
• application of AI to developer workflows
• types of workflows that people are building with pieces
• What are the most interesting, innovative, or unexpected ways that you have seen Pieces used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pieces?
• When is Pieces the wrong choice?
• What do you have planned for the future of Pieces?

Contact Info

• 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

• Pieces
• NPU == Neural Processing Unit
• Tensor Chip
• LoRA == Low Rank Adaptation
• Generative Adversarial Networks
• Mistral
• Emacs
• Vim
• NeoVim
• Dart
• Flutter
• Typescript
• Lua
• Retrieval Augmented Generation
• ONNX
• LSTM == Long Short-Term Memory
• LLama 2
• GitHub Copilot
• Tabnine
• Podcast Episode

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

Summary
Large Language Models (LLMs) have rapidly captured the attention of the world with their impressive capabilities. Unfortunately, they are often unpredictable and unreliable. This makes building a product based on their capabilities a unique challenge. Jignesh Patel is building DataChat to bring the capabilities of LLMs to organizational analytics, allowing anyone to have conversations with their business data. In this episode he shares the methods that he is using to build a product on top of this constantly shifting set of technologies.
Announcements

• Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
• Your host is Tobias Macey and today I'm interviewing Jignesh Patel about working with LLMs; understanding how they work and how to build your own

Interview

• Introduction
• How did you get involved in machine learning?
• Can you start by sharing some of the ways that you are working with LLMs currently?
• What are the business challenges involved in building a product on top of an LLM model that you don't own or control? 
  • In the current age of business, your data is often your strategic advantage. How do you avoid losing control of, or leaking that data while interfacing with a hosted LLM API?
• What are the technical difficulties related to using an LLM as a core element of a product when they are largely a black box? 
  • What are some strategies for gaining visibility into the inner workings or decision making rules for these models?
• What are the factors, whether technical or organizational, that might motivate you to build your own LLM for a business or product? 
  • Can you unpack what it means to "build your own" when it comes to an LLM?
• In your work at DataChat, how has the progression of sophistication in LLM technology impacted your own product strategy?
• What are the most interesting, innovative, or unexpected ways that you have seen LLMs/DataChat used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working with LLMs?
• When is an LLM the wrong choice?
• What do you have planned for the future of DataChat?

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

• DataChat
• CMU == Carnegie Mellon University
• SVM == Support Vector Machine
• Generative AI
• Genomics
• Proteomics
• Parquet
• OpenAI Codex
• LLama
• Mistral
• Google Vertex
• Langchain
• Retrieval Augmented Generation
• Prompt Engineering
• Ensemble Learning
• XGBoost
• Catboost
• Linear Regression
• COGS == Cost Of Goods Sold
• Bruce Schneier - AI And Trust

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 is a powerful set of technologies, holding the potential to dramatically transform businesses across industries. Unfortunately, the implementation of ML projects often fail to achieve their intended goals. This failure is due to a lack of collaboration and investment across technological and organizational boundaries. To help improve the success rate of machine learning projects Eric Siegel developed the six step bizML framework, outlining the process to ensure that everyone understands the whole process of ML deployment. In this episode he shares the principles and promise of that framework and his motivation for encapsulating it in his book "The AI Playbook".
Announcements

• Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
• Your host is Tobias Macey and today I'm interviewing Eric Siegel about how the bizML approach can help improve the success rate of your ML projects

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what bizML is and the story behind it? 
  • What are the key aspects of this approach that are different from the "industry standard" lifecycle of an ML project?
• What are the elements of your personal experience as an ML consultant that helped you develop the tenets of bizML?
• Who are the personas that need to be involved in an ML project to increase the likelihood of success? 
  • Who do you find to be best suited to "own" or "lead" the process?
• What are the organizational patterns that might hinder the work of delivering on the goals of an ML initiative?
• What are some of the misconceptions about the work involved in/capabilities of an ML model that you commonly encounter?
• What is your main goal in writing your book "The AI Playbook"?
• What are the most interesting, innovative, or unexpected ways that you have seen the bizML process in action?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on ML projects and developing the bizML framework?
• When is bizML the wrong choice?
• What are the future developments in organizational and technical approaches to ML that will improve the success rate of AI projects?

Contact Info

• 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

• The AI Playbook: Mastering the Rare Art of Machine Learning Deployment by Eric Siegel
• Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel
• Columbia University
• Machine Learning Week Conference
• Generative AI World
• Machine Learning Leadership and Practice Course
• Rexer Analytics
• KD Nuggets
• CRISP-DM
• Random Forest
• Gradient Descent

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

Summary
One of the most time consuming aspects of building a machine learning model is feature engineering. Generative AI offers the possibility of accelerating the discovery and creation of feature pipelines. In this episode Colin Priest explains how FeatureByte is applying generative AI models to the challenge of building and maintaining machine learning pipelines.
Announcements

• Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
• Your host is Tobias Macey and today I'm interviewing Colin Priest about applying generative AI to the task of building and deploying AI pipelines

Interview

• Introduction
• How did you get involved in machine learning?
• Can you start by giving the 30,000 foot view of the steps involved in an AI pipeline? 
  • Understand the problem
  • Feature ideation
  • Feature engineering
  • Experiment
  • Optimize
  • Productionize
• What are the stages of that process that are prone to repetition? 
  • What are the ways that teams typically try to automate those steps?
• What are the features of generative AI models that can be brought to bear on the design stage of an AI pipeline? 
  • What are the validation/verification processes that engineers need to apply to the generated suggestions?
  • What are the opportunities/limitations for unit/integration style tests?
• What are the elements of developer experience that need to be addressed to make the gen AI capabilities an enhancement instead of a distraction? 
  • What are the interfaces through which the AI functionality can/should be exposed?
• What are the aspects of pipeline and model deployment that can benefit from generative AI functionality? 
  • What are the potential risk factors that need to be considered when evaluating the application of this functionality?
• What are the most interesting, innovative, or unexpected ways that you have seen generative AI used in the development and maintenance of AI pipelines?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on the application of generative AI to the ML workflow?
• When is generative AI the wrong choice?
• What do you have planned for the future of FeatureByte's AI copilot capabiliteis?

Contact Info

• 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

• FeatureByte
• Generative AI
• The Art of War
• OCR == Optical Character Recognition
• Genetic Algorithm
• Semantic Layer
• Prompt Engineering

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

Summary
Every business develops their own specific workflows to address their internal organizational needs. Not all of them are properly documented, or even visible. Workflow automation tools have tried to reduce the manual burden involved, but they are rigid and require substantial investment of time to discover and develop the routines. Boaz Hecht co-founded 8Flow to iteratively discover and automate pieces of workflows, bringing visibility and collaboration to the internal organizational processes that keep the business running.
Announcements

• Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
• Your host is Tobias Macey and today I'm interviewing Boaz Hecht about using AI to automate customer support at 8Flow

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what 8Flow is and the story behind it?
• How does 8Flow compare to RPA tools that companies are using today? 
  • What are the opportunities for augmenting or integrating with RPA frameworks?
• What are the key selling points for the solution that you are building? (does AI sell? Or is it about the realized savings?)
• What are the sources of signal that you are relying on to build model features?
• Given the heterogeneity in tools and processes across customers, what are the common focal points that let you address the widest possible range of functionality?
• Can you describe how 8Flow is implemented? 
  • How have the design and goals evolved since you first started working on it?
• What are the model categories that are most relevant for process automation in your product?
• How have you approached the design and implementation of your MLOps workflow? (model training, deployment, monitoring, versioning, etc.)
• What are the open questions around product focus and system design that you are still grappling with?
• Given the relative recency of ML/AI as a profession and the massive growth in attention and activity, how are you addressing the challenge of obtaining and maximizing human talent?
• What are the most interesting, innovative, or unexpected ways that you have seen 8Flow used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on 8Flow?
• When is 8Flow the wrong choice?
• What do you have planned for the future of 8Flow?

Contact Info

• LinkedIn
• Personal Website

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.
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Links

• 8Flow
• Robotic Process Automation

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