In this episode, Rashmi Shetty, senior director of enterprise generative AI platform at Capital One, joins us to explore how the company is designing, deploying, and scaling multi-agent systems in a highly regulated environment. Rashmi walks us through Chat Concierge, a multi-agent chat experience for auto dealerships that handles intent disambiguation, tool invocation, and human handoffs to deliver safer, more personalized customer journeys. We discuss Capital One’s platform-centric approach to AI agents and how it separates design from runtime governance, embedding policies, guardrails, and cyber controls across agent threat boundaries. Rashmi shares how the team approaches the developer experience for agent builders, observability, and evals for stochastic, multi-agent workflows; and strategies for model specialization, including fine-tuning and distillation. We also cover standards and abstraction, closed-loop learning from production telemetry, and key lessons for enterprises building agentic systems.
The complete show notes for this episode can be found at https://twimlai.com/go/765.
Modern software development is evolving rapidly. New tools, processes, and AI-powered systems are reshaping how teams collaborate and how engineers find satisfaction in their craft. At the same time, developer experience has become a critical function for helping organizations balance agility, security, and scale while maintaining the creativity and flow that make top tier engineering possible.
Capital One is continuously transforming its developer culture, with a focus on faster development cycles, reducing operational overhead, and boosting productivity across the organization.
Catherine McGarvey is the SVP of Developer Experience at Capital One. She joins the podcast with Sean Falconer to talk about what developer enablement means at enterprise scale, measuring developer productivity, being agile in a regulated environment, AI in enterprise development, the future for developers, and much more.
Full Disclosure: This episode is sponsored by Capital One.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Developer Experience at Capital One with Catherine McGarvey appeared first on Software Engineering Daily.
Explore how Capital One is using tech to innovate the banking experience here.
Connect with Kathleen on LinkedIn and visit her blog.
Shoutout to user Theraot for answering the questions How to connect a signal with extra arguments in Godot 4, which won them a Lifeboat badge.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Prem Natarajan, the executive vice president, chief scientist and head of AI at Capital One, discusses how AI is enhancing financial services by transforming customer experiences and internal operations. He shares how advanced AI models and agentic workflows are accelerating the delivery of personalized services and improving efficiency — paving the way for enhanced security and streamlined financial processes.
Today, we're joined by Abhijit Bose, head of enterprise AI and ML platforms at Capital One to discuss the evolution of the company’s approach and insights on Generative AI and platform best practices. In this episode, we dig into the company’s platform-centric approach to AI, and how they’ve been evolving their existing MLOps and data platforms to support the new challenges and opportunities presented by generative AI workloads and AI agents. We explore their use of cloud-based infrastructure—in this case on AWS—to provide a foundation upon which they then layer open-source and proprietary services and tools. We cover their use of Llama 3 and open-weight models, their approach to fine-tuning, their observability tooling for Gen AI applications, their use of inference optimization techniques like quantization, and more. Finally, Abhijit shares the future of agentic workflows in the enterprise, the application of OpenAI o1-style reasoning in models, and the new roles and skillsets required in the evolving GenAI landscape.
The complete show notes for this episode can be found at https://twimlai.com/go/714.
On this Screaming in the Cloud Summer Replay, we revisit our conversation with Aparna Sinha, the Head of AI Product at Capital One. As a former Director of Product Management at Google Cloud, Aparan joins Corey to talk about GCP and how Corey was surprised to find that, in some ways, it was “its own universe.” She offers up why folks can expect a developer user-friendly experience when using GCP, and how it differentiates them from the litany of cloud providers out there. From focusing on developing, to a vast array of customers, GCP is bringing their best forward. Check out their conversation on how GCP is keeping its focus on the user!
Show Highlights:
(0:00) Intro
(0:48) Duckbill Group sponsor read
(1:21) Role of a Director of Outbound Product Management
(2:43) Developer experiences on Google Cloud
(8:47) The philosophy of courting developers
(11:38) The shift to serverless
(17:17) Cloud Run observations
(22:59) Duckbill Group sponsor read
(23:43) Customer involvement with Google Cloud
(28:55) Cloud Build vs. Cloud Deploy
(32:50) Google and cloud security
(38:45) Where you can find Aparna
About Aparna
Aparna Sinha is Senior Vice President and Head of Enterprise AI/ML products at Capital One. She is also a startup investor / advisor at PearVC. Aparna has a track record of successful P&L ownership, creating new revenue streams and building $B+ businesses through technical and go-to-market innovation.
She was Sr. Director of Developer Products at Google Cloud leading a 100+ member PM, UX, and DevRel Engineering team responsible for >40 cloud services and open source tools. She was an early contributor to Kubernetes, built the team and grew Google Kubernetes Engine 100x into a Top 3 revenue generator for Cloud. Prior to Cloud Aparna worked on Android, ChromeOS and Play. Previously at McKinsey & Company she was a leader in the business technology office, working with CIOs on server virtualization strategy, pricing, and SaaS.
Aparna holds a PhD in Electrical Engineering from Stanford, and a patent from Google. She served as Chair of the Governing Board of the Cloud Native Computing Foundation (CNCF).
Links:
DevOps Research Report: https://www.devops-research.com/research.html
Twitter: https://x.com/aparnabsinha
Original Episode:
https://www.lastweekinaws.com/podcast/screaming-in-the-cloud/building-a-user-friendly-product-with-aparna-sinha/
Sponsor:
The Duckbill Group: https://www.duckbillgroup.com/
In this episode of the NVIDIA AI Podcast, recorded live at the GTC 2024, host Noah Kravitz sits down with Adam Wenchel, co-founder and CEO of Arthur. Arthur enhances the performance of AI systems across various metrics like accuracy, explainability, and fairness. Wenchel shares insights into the challenges and opportunities of deploying generative AI. The discussion spans a range of topics, including AI bias, the observability of AI systems, and the practical implications of AI in business. For more on Arthur, visit arthur.ai.
Growing up in a multilingual community, Prem Natarajan became interested in language at a young age. Eventually that interest, aptitude, and curiosity translated into an interest in machine learning and technical development, and today Prem works as the chief scientist and head of enterprise AI at financial services company Capital One.
Prem joins this episode to share how Capital One’s technology teams are delivering value to customers by applying artificial intelligence in areas like fraud detection, how generative AI’s strengths stand to transform the developer experience, and why the right combination of product, science, and engineering expertise is key to successful AI and machine learning initiatives. Read the episode transcript here.
Guest bio:
As chief scientist and head of enterprise AI at Capital One, Prem Natarajan leads technology strategy, architecture, and development for the company’s enterprise data, analytics, and AI and machine learning initiatives, including advancing its AI capabilities, tools, and research efforts. Natarajan has more than two decades of experience leading science, technology, and commercialization efforts in natural language processing, speech recognition, computer vision, forecasting, and other applications of machine learning.
Me, Myself, and AI is a collaborative podcast from MIT Sloan Management Review and Boston Consulting Group and is hosted by Sam Ransbotham and Shervin Khodabandeh. Our engineer is David Lishansky, and the coordinating producers are Allison Ryder and Sophie Rüdinger.
Stay in touch with us by joining our LinkedIn group, AI for Leaders at mitsmr.com/AIforLeaders or by following Me, Myself, and AI on LinkedIn.
We encourage you to rate and review our show. Your comments may be used in Me, Myself, and AI materials.
Today we’re joined by Prem Natarajan, chief scientist and head of enterprise AI at Capital One. In our conversation, we discuss AI access and inclusivity as technical challenges and explore some of Prem and his team’s multidisciplinary approaches to tackling these complexities. We dive into the issues of bias, dealing with class imbalances, and the integration of various research initiatives to achieve additive results. Prem also shares his team’s work on foundation models for financial data curation, highlighting the importance of data quality and the use of federated learning, and emphasizing the impact these factors have on the model performance and reliability in critical applications like fraud detection. Lastly, Prem shares his overall approach to tackling AI research in the context of a banking enterprise, including prioritizing mission-inspired research aiming to deliver tangible benefits to customers and the broader community, investing in diverse talent and the best infrastructure, and forging strategic partnerships with a variety of academic labs.
The complete show notes for this episode can be found at twimlai.com/go/658.
Today we’re joined by Miriam Friedel, senior director of ML engineering at Capital One. In our conversation with Miriam, we discuss some of the challenges faced when delivering machine learning tools and systems in highly regulated enterprise environments, and some of the practices her teams have adopted to help them operate with greater speed and agility. We also explore how to create a culture of collaboration, the value of standardized tooling and processes, leveraging open-source, and incentivizing model reuse. Miriam also shares her thoughts on building a ‘unicorn’ team, and what this means for the team she’s built at Capital One, as well as her take on build vs. buy decisions for MLOps, and the future of MLOps and enterprise AI more broadly. Throughout, Miriam shares examples of these ideas at work in some of the tools their team has built, such as Rubicon, an open source experiment management tool, and Kubeflow pipeline components that enable Capital One data scientists to efficiently leverage and scale models.
The complete show notes for this episode can be found at twimlai.com/go/653.
In this episode, Nathan sits down with Adam Wenchel, CEO of Arthur.ai. Adam founded the AI security company back in 2019, before GPT-2 existed. In this episode, Adam shares his unique perspective on the AI security landscape, drawing from years building commercial AI systems. They discuss the attacks Adam set out to defend against, the changing priorities of executives in the rush to adopt LLMs, and the LLM-specific techniques Adam has developed. If you're looking for an ERP platform, check out our sponsor, NetSuite: http://netsuite.com/cognitive
We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com.
TIMESTAMPS:
(00:00:00) Episode Preview
(00:03:45) Adam's background in AI and starting Arthur AI in 2019
(00:05:52) The release of ChatGPT as a watershed moment for generative AI
(00:07:09) Differences between traditional cybersecurity and AI security
(00:09:51) Early examples of AI security issues like boundary detection attacks in fraud systems
(00:12:39) - Mitigating risks of AI systems through observability and robust training
(00:14:40) - Financial services governance of AI models and its challenges today
(00:15:12) Sponsors: Netsuite | Omneky
(00:21:18) - Motivations for governance like staying compliant with regulations
(00:21:40) - The mix of incentives shaping earlier AI governance, like explainability
(00:28:14) - Using LMs to evaluate the security of other LMs
(00:30:03) - Dynamics between training and evaluating future LMs
(00:38:10) - The state of reasoning capabilities in large LMs
(00:44:35) - Corporate urgency around adopting generative AI technologies
(00:46:51) - Common enterprise use cases for generative AI and security concerns
(00:50:45) - Techniques for reducing hallucinations in retrieval augmented LMs
(00:53:15) - Benchmarking LMs on specific organizational tasks versus generic benchmarks
(00:56:30) - Metrics beyond accuracy like concision and hedging
(01:01:20) - Automatically detecting anomalies and hallucinations
(01:09:20) - Relationships between Arthur AI and foundation model providers
(01:11:52) - Where Cohere shines: multilingualism and not hedging
(01:13:43) - Anticipating future watershed moments and steady progress
(01:19:03) - Whether we can ever fully solve AI alignment and safety
LINKS:
Arthur.ai: https://www.arthur.ai/
X/Social:
@apwenchel (Adam)
@itsArthurAI (Arthur.ai)
@labenz (Nathan)
@eriktorenberg
@CogRev_Podcast
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Today we’re joined by Bayan Bruss, Vice President of Applied ML Research at Capital One. In our conversation with Bayan, we covered a pair of papers his team presented at this year’s ICML conference. We begin with the paper Interpretable Subspaces in Image Representations, where Bayan gives us a dive deep into the interpretability framework, embedding dimensions, contrastive approaches, and how their model can accelerate image representation in deep learning. We also explore GOAT: A Global Transformer on Large-scale Graphs, a scalable global graph transformer. We talk through the computation challenges, homophilic and heterophilic principles, model sparsity, and how their research proposes methodologies to get around the computational barrier when scaling to large-scale graph models.
The complete show notes for this episode can be found at twimlai.com/go/641.
Today we’re joined by Disha Singla, a senior director of machine learning engineering at Capital One. In our conversation with Disha, we explore her role as the leader of the Data Insights team at Capital One, where they’ve been tasked with creating reusable libraries, components, and workflows to make ML usable broadly across the company, as well as a platform to make it all accessible and to drive meaningful insights. We discuss the construction of her team, as well as the types of interactions and requests they receive from their customers (data scientists), productionized use cases from the platform, and their efforts to transition from batch to real-time deployment. Disha also shares her thoughts on the ROI of machine learning and getting buy-in from executives, how she sees machine learning evolving at the company over the next 10 years, and much more!
The complete show notes for this episode can be found at twimlai.com/go/606
Today we’re joined by Ali Rodell, a senior director of machine learning engineering at Capital One. In our conversation with Ali, we explore his role as the head of model development platforms at Capital One, including how his 25+ years in software development have shaped his view on building platforms and the evolution of the platforms space over the last 10 years. We discuss the importance of a healthy open source tooling ecosystem, Capital One’s use of various open source capabilites like kubeflow and kubernetes to build out platforms, and some of the challenges that come along with modifying/customizing these tools to work for him and his teams. Finally, we explore the range of user personas that need to be accounted for when making decisions about tooling, supporting things like Jupyter notebooks and other low level tools, and how that can be potentially challenging in a highly regulated environment like the financial industry.
The complete show notes for this episode can be found at twimlai.com/go/595
Today we’re joined by Bayan Bruss, a Sr. director of applied ML research at Capital One. In our conversation with Bayan, we dig into his work in applying various deep learning techniques to tabular data, including taking advancements made in other areas like graph CNNs and other traditional graph mining algorithms and applying them to financial services applications. We discuss why despite a “flood” of innovation in the field, work on tabular data doesn’t elicit as much fanfare despite its broad use across businesses, Bayan’s experience with the difficulty of making deep learning work on tabular data, and what opportunities have been presented for the field with the emergence of multi-modality and transformer models. We also explore a pair of papers from Bayan’s team, focused on both transformers and transfer learning for tabular data.
The complete show notes for this episode can be found at twimlai.com/go/591
Summary
Communication and shared context are the hardest part of any data system. In recent years the focus has been on data catalogs as the means for documenting data assets, but those introduce a secondary system of record in order to find the necessary information. In this episode Emily Riederer shares her work to create a controlled vocabulary for managing the semantic elements of the data managed by her team and encoding it in the schema definitions in her data warehouse. She also explains how she created the dbtplyr package to simplify the work of creating and enforcing your own controlled vocabularies.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold.
Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
Your host is Tobias Macey and today I’m interviewing Emily Riederer about defining and enforcing column contracts and controlled vocabularies for your data warehouse
Interview
Introduction
How did you get involved in the area of data management?
Can you start by discussing some of the anti-patterns that you have encountered in data warehouse naming conventions and how it relates to the modeling approach? (e.g. star/snowflake schema, data vault, etc.)
What are some of the types of contracts that can, and should, be defined and enforced in data workflows?
What are the boundaries where we should think about establishing those contracts?
What is the utility of column and table names for defining and enforcing contracts in analytical work?
What is the process for establishing contractual elements in a naming schema?
Who should be involved in that design process?
Who are the participants in the communication paths for column naming contracts?
What are some examples of context and details that can’t be captured in column names?
What are some options for managing that additional information and linking it to the naming contracts?
Can you describe the work that you have done with dbtplyr to make name contracts a supported construct in dbt projects?
How does dbtplyr help in the creation and enforcement of contracts in the development of dbt workflows
How are you using dbtplyr in your own work?
How do you handle the work of building transformations to make data comply with contracts?
What are the supplemental systems/techniques/documentation to work with name contracts and how they are leveraged by downstream consumers?
What are the most interesting, innovative, or unexpected ways that you have seen naming contracts and/or dbtplyr used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on dbtplyr?
When is dbtplyr the wrong choice?
What do you have planned for the future of dbtplyr?
Contact Info
Twitter
Website
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about 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 hosts@dataengineeringpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Links
dbtplyr
Great Expectations
Podcast Episode
Controlled Vocabularies Presentation
dplyr
Data Vault
Podcast Episode
OpenMetadata
Podcast Episode
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Support Data Engineering Podcast
Summary
Spark is a powerful and battle tested framework for building highly scalable data pipelines. Because of its proven ability to handle large volumes of data Capital One has invested in it for their business needs. In this episode Gokul Prabagaren shares his use for it in calculating your rewards points, including the auditing requirements and how he designed his pipeline to maintain all of the necessary information through a pattern of data enrichment.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold.
Your host is Tobias Macey and today I’m interviewing Gokul Prabagaren about how he is using Spark for real-world workflows at Capital One
Interview
Introduction
How did you get involved in the area of data management?
Can you start by giving an overview of the types of data and workflows that you are responsible for at Capital one?
In terms of the three "V"s (Volume, Variety, Velocity), what is the magnitude of the data that you are working with?
What are some of the business and regulatory requirements that have to be factored into the solutions that you design?
Who are the consumers of the data assets that you are producing?
Can you describe the technical elements of the platform that you use for managing your data pipelines?
What are the various ways that you are using Spark at Capital One?
You wrote a post and presented at the Databricks conference about your experience moving from a data filtering to a data enrichment pattern for segmenting transactions. Can you give some context as to the use case and what your design process was for the initial implementation?
What were the shortcomings to that approach/business requirements which led you to refactoring the approach to one that maintained all of the data through the different processing stages?
What are some of the impacts on data volumes and processing latencies working with enriched data frames persisted between task steps?
What are some of the other optimizations or improvements that you have made to that pipeline since you wrote the post?
What are some of the limitations of Spark that you have experienced during your work at Capital One?
How have you worked around them?
What are the most interesting, innovative, or unexpected ways that you have seen Spark used at Capital One?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on data engineering at Capital One?
What are some of the upcoming projects that you are focused on/excited for?
How has your experience with the filtering vs. enrichment approach influenced your thinking on other projects that you work on?
Contact Info
@gocool_p on Twitter
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about 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 hosts@dataengineeringpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Links
Apache Spark
Blog Post
Databricks Presentation
Delta Lake
Podcast Episode
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Support Data Engineering Podcast
About Aparna
Aparna Sinha is Director of Product for Kubernetes and Anthos at Google Cloud. Her teams are focused on transforming the way we work through innovation in platforms. Before Anthos and Kubernetes, Aparna worked on the Android platform. She joined Google from NetApp where she was Director of Product for storage automation and private cloud. Prior to NetApp, Aparna was a leader in McKinsey and Company’s business transformation office working with CXOs on IT strategy, pricing, and M&A. Aparna holds a PhD in Electrical Engineering from Stanford and has authored several technical publications. She serves on the Governing Board of the Cloud Native Computing Foundation (CNCF).
Links:
DevOps Research Report: https://www.devops-research.com/research.html
Twitter: https://twitter.com/apbhatnagar
The Alexa Prize Socialbot Grand Challenge is a competition for university students to create a social bot that can converse coherently and engagingly with humans. This year's prize goes to the team from Emory University in Atlanta, Georgia. I speak with Prem Natarajan, vice president of natural understanding in the Alexa AI organization, about the prize, the evolution of conversational AI, its current challenges and its future promise
We spoke with Capital One Senior Software Engineer Kyle Nicholson on how modern machine learning techniques have become a key tool for financial and credit analysis.
Today we’re joined by Dave Castillo, Managing VP for ML at Capital One and head of their Center for Machine Learning. In our conversation, we explore Capital One’s transition from “lab-based” ML to enterprise-wide adoption and support of ML, surprising ML use cases, their current platform ecosystem, their design vision in building this into a larger, all-encompassing platform, pain points in building this platform, and much more.
When you hear of AI and machine learning, it’s easy to think of technology companies leading the charge. Capital One is determined to change that. In a conversation with AI Podcast host Noah Kravitz, Nitzan Mekel, managing vice president of machine learning at Capital One, explained how the banking giant is integrating AI and machine learning into customer-facing applications such as fraud-monitoring and detection, call center operations and customer experience.
In this, the final episode of our Strata Data Conference series, we’re joined by Zachary Hanif, Director of Machine Learning at Capital One’s Center for Machine Learning.
We start our discussion with a look at the role of graph analytics in the ML toolkit, including some important application areas for graph-based systems. Zach gives us an overview of the different ways to implement graph analytics, including what he calls graphical processing engines which excel at handling large datasets, & much m
Learn about the announcements from Google Cloud Next, including GKE On-Prem, Cloud Services Platform, and Istio 1.0. Google's product management lead for Kubernetes and CNCF governing board member Aparna Sinha joins Adam and Craig to discuss what's new.
Do you have something cool to share? Some questions? Let us know:
web: kubernetespodcast.com
mail: kubernetespodcast@google.com
twitter: @kubernetespod
News of the week Rugby Sevens World Cup
Kubernetes wins the OSCON award for most impactful Open Source project
When Does Kubernetes Become Invisible And Ubiquitous?
Links from the interview Aparna Sinha on Twitter Google Power Women Of The Cloud
Cloud Services Platform: Launch blog
Web site
GKE On-Prem
Knative
Cloud Build
Bringing the best of serverless to you
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In this episode I’m joined by Adam Wenchel, vice president of AI and Data Innovation at Capital One, to discuss how Machine Learning & AI are being integrated into their day-to-day practices, and how those advances benefit the customer. In our conversation, we look into a few of the many applications of AI at the bank, including fraud detection, money laundering, customer service, and automating back office processes. Adam describes some of the challenges of applying ML in financial services and how Capital One maintains consistent portfolio management practices across the organization. We also discuss how the bank has organized to scale their machine learning efforts, and the steps they’ve taken to overcome the talent shortage in the space. The notes for this show can be found at twimlai.com/talk/147.
Tim Hockin and Aparna Sinha joined the show to talk about the backstory of Kubernetes inside Google, how Tim and others got it funded, the infrastructure of Kubernetes, and how they’ve been able to succeed by focusing on the community.
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Aparna Sinha – GitHub, X
Adam Stacoviak – Website, GitHub, LinkedIn, Mastodon, X
Jerod Santo – Website, GitHub, LinkedIn, Mastodon, X
Show Notes:
kubernetes.io
Large-scale cluster management at Google with Borg
Kubernetes The Hard Way
Go Time #20: Kubernetes, Containers, and Go with Kelsey Hightower
Bringing Pokémon GO to life on Google Cloud
Google Cloud Platform
Kubernetes on Wikipedia
kubernetes/minikube
Something missing or broken? PRs welcome!
George Moore drove trucks for years. But he knew he wanted to do more with his life, and his wife encouraged him to go back to school, finish his degree, and pursue the tech career he’d started long ago. So he did. He started at help desk, and slowly climbed his way up to his current role, as master software engineer. He shares his incredible journey filled with uncertainties and perseverance, and how it’s shaped him as a developer and a person.
Show Links
Partner with Dev & CodeNewbie! (sponsor)
Selenium
SoapUI
Lotus Notes
Scratch
University of Maryland
Capital One
Codeland Conf
Codeland 2019
George Moore
George Moore is a master software engineer at Capital One, with experience in help desk, QA, automation, and mobile development.
