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.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

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
Support The Machine Learning Podcast

Summary
Machine learning and AI applications hold the promise of drastically impacting every aspect of modern life. With that potential for profound change comes a responsibility for the creators of the technology to account for the ramifications of their work. In this episode Nicholas Cifuentes-Goodbody guides us through the minefields of social, technical, and ethical considerations that are necessary to ensure that this next generation of technical and economic systems are equitable and beneficial for the people that they impact.
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 Nicholas Cifuentes-Goodbody about the different elements of the machine learning workflow where ethics need to be considered

Interview

• Introduction
• How did you get involved in machine learning?
• To start with, who is responsible for addressing the ethical concerns around AI?
• What are the different ways that AI can have positive or negative outcomes from an ethical perspective? 
  • What is the role of practitioners/individual contributors in the identification and evaluation of ethical impacts of their work?
• What are some utilities that are helpful in identifying and addressing bias in training data?
• How can practitioners address challenges of equity and accessibility in the delivery of AI products?
• What are some of the options for reducing the energy consumption for training and serving AI?
• What are the most interesting, innovative, or unexpected ways that you have seen ML teams incorporate ethics into their work?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on ethical implications of ML?
• What are some of the resources that you recommend for people who want to invest in their knowledge and application of ethics in the realm of ML?

Contact Info

• WorldQuant University's Applied Data Science Lab
• 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

• UNESCO Recommendation on the Ethics of Artificial Intelligence
• European Union AI Act
• How machine learning helps advance access to human rights information
• Disinformation, Team Jorge
• China, AI, and Human Rights
• How China Is Using A.I. to Profile a Minority
• Weapons of Math Destruction
• Fairlearn
• AI Fairness 360
• Allen Institute for AI NYT
• Allen Institute for AI
• Transformers
• AI4ALL
• WorldQuant University
• How to Make Generative AI Greener
• Machine Learning Emissions Calculator
• Practicing Trustworthy Machine Learning
• Energy and Policy Considerations for Deep Learning
• Natural Language Processing
• Trolley Problem
• Protected Classes
• fairlearn (scikit-learn)
• BERT 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
Building machine learning systems and other intelligent applications are a complex undertaking. This often requires retrieving data from a warehouse engine, adding an extra barrier to every workflow. The RelationalAI engine was built as a co-processor for your data warehouse that adds a greater degree of flexibility in the representation and analysis of the underlying information, simplifying the work involved. In this episode CEO Molham Aref explains how RelationalAI is designed, the capabilities that it adds to your data clouds, and how you can start using it to build more sophisticated applications on your data.
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 Molham Aref about RelationalAI and the principles behind it for powering intelligent applications

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what RelationalAI is and the story behind it? 
  • On your site you call your product an "AI Co-processor". Can you explain what you mean by that phrase?
• What are the primary use cases that you address with the RelationalAI product? 
  • What are the types of solutions that teams might build to address those problems in the absence of something like the RelationalAI engine?
• Can you describe the system design of RelationalAI? 
  • How have the design and goals of the platform changed since you first started working on it?
• For someone who is using RelationalAI to address a business need, what does the onboarding and implementation workflow look like?
• What is your design philosophy for identifying the balance between automating the implementation of certain categories of application (e.g. NER) vs. providing building blocks and letting teams assemble them on their own?
• What are the data modeling paradigms that teams should be aware of to make the best use of the RKGS platform and Rel language?
• What are the aspects of customer education that you find yourself spending the most time on?
• What are some of the most under-utilized or misunderstood capabilities of the RelationalAI platform that you think deserve more attention?
• What are the most interesting, innovative, or unexpected ways that you have seen the RelationalAI product used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on RelationalAI?
• When is RelationalAI the wrong choice?
• What do you have planned for the future of RelationalAI?

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

• RelationalAI
• Snowflake
• AI Winter
• BigQuery
• Gradient Descent
• B-Tree
• Navigational Database
• Hadoop
• Teradata
• Worst Case Optimal Join
• Semantic Query Optimization
• Relational Algebra
• HyperGraph
• Linear Algebra
• Vector Database
• Pathway
  • Data Engineering Podcast Episode
• Pinecone
  • Data Engineering 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
Machine learning and generative AI systems have produced truly impressive capabilities. Unfortunately, many of these applications are not designed with the privacy of end-users in mind. TripleBlind is a platform focused on embedding privacy preserving techniques in the machine learning process to produce more user-friendly AI products. In this episode Gharib Gharibi explains how the current generation of applications can be susceptible to leaking user data and how to counteract those trends.
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 Gharib Gharibi about the challenges of bias and data privacy in generative AI models

Interview

• Introduction
• How did you get involved in machine learning?
• Generative AI has been gaining a lot of attention and speculation about its impact. What are some of the risks that these capabilities pose? 
  • What are the main contributing factors to their existing shortcomings?
  • What are some of the subtle ways that bias in the source data can manifest?
• In addition to inaccurate results, there is also a question of how user interactions might be re-purposed and potential impacts on data and personal privacy. What are the main sources of risk?
• With the massive attention that generative AI has created and the perspectives that are being shaped by it, how do you see that impacting the general perception of other implementations of AI/ML? 
  • How can ML practitioners improve and convey the trustworthiness of their models to end users?
  • What are the risks for the industry if generative models fall out of favor with the public?
• How does your work at Tripleblind help to encourage a conscientious approach to AI?
• What are the most interesting, innovative, or unexpected ways that you have seen data privacy addressed in AI applications?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on privacy in AI?
• When is TripleBlind the wrong choice?
• What do you have planned for the future of TripleBlind?

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

• TripleBlind
• ImageNet Geoffrey Hinton Paper
• BERT language model
• Generative AI
• GPT == Generative Pre-trained Transformer
• HIPAA Safe Harbor Rules
• Federated Learning
• Differential Privacy
• Homomorphic Encryption

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

Summary
Software development involves an interesting balance of creativity and repetition of patterns. Generative AI has accelerated the ability of developer tools to provide useful suggestions that speed up the work of engineers. Tabnine is one of the main platforms offering an AI powered assistant for software engineers. In this episode Eran Yahav shares the journey that he has taken in building this product and the ways that it enhances the ability of humans to get their work done, and when the humans have to adapt to the tool.
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 Eran Yahav about building an AI powered developer assistant at Tabnine

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what Tabnine is and the story behind it?
• What are the individual and organizational motivations for using AI to generate code? 
  • What are the real-world limitations of generative AI for creating software? (e.g. size/complexity of the outputs, naming conventions, etc.)
  • What are the elements of skepticism/oversight that developers need to exercise while using a system like Tabnine?
• What are some of the primary ways that developers interact with Tabnine during their development workflow? 
  • Are there any particular styles of software for which an AI is more appropriate/capable? (e.g. webapps vs. data pipelines vs. exploratory analysis, etc.)
• For natural languages there is a strong bias toward English in the current generation of LLMs. How does that translate into computer languages? (e.g. Python, Java, C++, etc.)
• Can you describe the structure and implementation of Tabnine? 
  • Do you rely primarily on a single core model, or do you have multiple models with subspecialization?
  • How have the design and goals of the product changed since you first started working on it?
• What are the biggest challenges in building a custom LLM for code? 
  • What are the opportunities for specialization of the model architecture given the highly structured nature of the problem domain?
• For users of Tabnine, how do you assess/monitor the accuracy of recommendations? 
  • What are the feedback and reinforcement mechanisms for the model(s)?
• What are the most interesting, innovative, or unexpected ways that you have seen Tabnine's LLM powered coding assistant used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI assisted development at Tabnine?
• When is an AI developer assistant the wrong choice?
• What do you have planned for the future of Tabnine?

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

• TabNine
• Technion University
• Program Synthesis
• Context Stuffing
• Elixir
• Dependency Injection
• COBOL
• Verilog
• MidJourney

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

Summary
Software systems power much of the modern world. For applications that impact the safety and well-being of people there is an extra set of precautions that need to be addressed before deploying to production. If machine learning and AI are part of that application then there is a greater need to validate the proper functionality of the models. In this episode Erez Kaminski shares the work that he is doing at Ketryx to make that validation easier to implement and incorporate into the ongoing maintenance of software and machine learning products.
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 Erez Kaminski about using machine learning in safety critical and highly regulated medical applications

Interview

• Introduction
• How did you get involved in machine learning?
• Can you start by describing some of the regulatory burdens placed on ML teams who are building solutions for medical applications? 
  • How do these requirements impact the development and validation processes of model design and development?
• What are some examples of the procedural and record-keeping aspects of the machine learning workflow that are required for FDA compliance? 
  • What are the opportunities for automating pieces of that overhead?
• Can you describe what you are doing at Ketryx to streamline the development/training/deployment of ML/AI applications for medical use cases? 
  • What are the ideas/assumptions that you had at the start of Ketryx that have been challenged/updated as you work with customers?
• What are the most interesting, innovative, or unexpected ways that you have seen ML used in medical applications?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Ketryx?
• When is Ketryx the wrong choice?
• What do you have planned for the future of Ketryx?

Contact Info

• Email
• 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

• Ketryx
• Wolfram Alpha
• Mathematica
• Tensorflow
• SBOM == Software Bill Of Materials
• Air-gapped Systems
• AlexNet
• Shapley Values
• SHAP
  • Podcast.__init__ Episode
• Bayesian Statistics
• Causal Modeling
• Prophet
• FDA Principles Of Software Validation

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 have gained a substantial amount of attention in the area of AI and machine learning. While they are impressive, there are many applications where they are not the best option. In this episode Piero Molino explains how declarative ML approaches allow you to make the best use of the available tools across use cases and data formats.
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 Piero Molino about the application of declarative ML in a world being dominated by large language models

Interview

• Introduction
• How did you get involved in machine learning?
• Can you start by summarizing your perspective on the effect that LLMs are having on the AI/ML industry? 
  • In a world where LLMs are being applied to a growing variety of use cases, what are the capabilities that they still lack?
  • How does declarative ML help to address those shortcomings?
• The majority of current hype is about commercial models (e.g. GPT-4). Can you summarize the current state of the ecosystem for open source LLMs? 
  • For teams who are investing in ML/AI capabilities, what are the sources of platform risk for LLMs?
  • What are the comparative benefits of using a declarative ML approach?
• What are the most interesting, innovative, or unexpected ways that you have seen LLMs used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on declarative ML in the age of LLMs?
• When is an LLM the wrong choice?
• What do you have planned for the future of declarative ML and Predibase?

Contact Info

• LinkedIn
• Website

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

Parting Question

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

Links

• Predibase
  • Podcast Episode
• Ludwig
  • Podcast.__init__ Episode
• Recommender Systems
• Information Retrieval
• Vector Database
• Transformer Model
• BERT
• Context Windows
• LLAMA

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