Open||Source||Data

This episode features an interview with Jorge Torres, Co-founder and CEO of MindsDB. MindsDB is a virtual AI database that works with existing data to help developers build AI-centered apps. In 2008, Jorge began his work on scaling solutions using machine learning as the first full-time engineer at Couchsurfing, growing the company from a few thousand users to a few million. He has also served a number of data-intensive start-ups and was a visiting scholar at UC Berkeley researching machine learning automation and explainability.In this episode, Sam and Jorge discuss the inspiration and challenges behind MindsDB, classic data science AI versus applied AI, and time series transformers.-------------------“So much data in the world is time series data, so much data. Even data that people don't know is time series, it's time series. So long as it’s moving over time, it is time series data. Whether you store it or not, that's a different thing. For having a pre-trained model on time series data, it even enabled the fact that you don't have to store all the historical data. You can just take the model and start passing data as it comes through, and then you get out the forecast. So you don't even have to have the historical data. All you need to have is the data at that given instance, and you can pass it to the model and you get an output. It's mind blowing.” – Jorge Torres-------------------Episode Timestamps:(05:20): The inspiration behind MindsDB(10:20): Classic data science AI approach vs. applied AI(22:09): What open source data means to Jorge(28:51): What excites Jorge about Nixtla and time series transformers(37:07): A question Jorge wishes to be asked(40:20): Jorge’s advice for the audience(41:38): Backstage takeaways with executive producer, Audra Montenegro-------------------Links:LinkedIn - Connect with JorgeLearn more about MindsDB open source codeLearn more about MindsDB

Sam Ramji discusses Microsoft's transformation, the impact of Kubernetes, and the rapid acceleration of AI research and development. Topics include defragmentation of the industry by Kubernetes, the transformative power of AI, the concept of cognitive economy, and implications of advancements in robotics, AI, and clean energy.

This episode features an interview with Abby Kearns, technology executive, board director, and angel investor. Her career has spanned executive leadership, product marketing, product management, and consulting across Fortune 500 companies and startups, including Puppet, Cloud Foundry Foundation, and Verizon. Abby currently serves as a board director for Lightbend, Stackpath, and Invoke. In this episode, Sam sits down with Abby to discuss the betrayal source license, the role open source plays in AI, and empowering trust.-------------------“There's so much happening so quickly that I think open source has the power to help harness a lot of that innovative conversation. In a way that I think it's going to be really, really hard to match in a proprietary way. I think open source and the ability, given the fact that we're talking about AI and data, the two are very interrelated at this point. AI is not super interesting without data. I think the power of open source right now and what's happening, I think it has to happen in open source and I think it really has to have that level of transparency and visibility. But, always the ability for everyone to step up and understand what's happening at this moment in time and shape it.” – Abby Kearns-------------------Episode Timestamps:(00:50): Sam and Abby discuss the betrayal source license(14:12): What open source data means to Abby(23:30): Abby dives into the companies she’s investing in(34:30): How nonprofits can empower trust(38:32): A question Abby wishes to be asked(40:21): Abby’s advice for the audience(43:53): Backstage takeaways with executive producer, Audra Montenegro-------------------Links:LinkedIn - Connect with AbbyTwitter - Follow AbbyRead Design the Life You Love

This episode features an interview with Daniel Lenton, Founder and CEO of Ivy, where the team is on a mission to unify the fragmented AI stack. Prior to Ivy, Daniel was a Robotics Research Engineer at Dyson and a Deep Learning Research Scientist for Amazon Prime Air. During his PhD, Daniel explored the intersection between learning-based geometric representations, ego-centric perception, spatial memory, and visuomotor control for robotics.In this episode, Sam and Daniel discuss the inspiration behind Ivy, open source reproducibility, and democratizing AI.-------------------"There's too much amazing stuff going on, from too many different parties. We just want to be the objective source of truth to show you the data and show you where your model will be doing best, and continue to do this as a service or something like this. This is high-level, some of the areas we see and going into, we really want to be a useful tool for anybody that wants to just kind of understand this fragmented complex space quickly and intuitively, and we are trying to be the tool that does that." – Daniel Lenton-------------------Episode Timestamps:(01:00): What open source data means to Daniel(05:37): The challenges of building Ivy(15:37): The future of Ivy(25:19): Who should know about Ivy(28:46): Daniel’s advice for the audience(32:00): Backstage takeaways with executive producer, Audra Montenegro-------------------Links:LinkedIn - Connect with DanielLearn more about Ivy

This episode features an interview with Demetrios Brinkmann, Founder of the MLOps Community, an organization for people to share best practices around MLOps. Demetrios fell into the Machine Learning Operations world and has since interviewed leading names around MLOps, data science, and machine learning. In this episode, Sam sits down with Demetrios to discuss LLM in production use cases, ML engineering teams, and the LLM Survey Report from the MLOps Community.-------------------"I think the most novel ones that I saw from the survey were when a chat bot would prompt a human as opposed to the human prompting the chat bot. It's almost like you have this LLM coach. And in that way, it's not necessarily like this isn't LLM in production that an end user is getting that's not outside the business or that is outside the business. It's more like internally, you can think about maybe it's an accountant and the accountant is filing my taxes for the year. As they're filing them, the LLM is prompting them on different tax laws that maybe they weren't thinking about or different ways that they could file things." – Demetrios Brinkmann-------------------Episode Timestamps:(04:30): LLMs as the new standard(19:26): Key LLM in production use cases(31:18): What open source data means to Demetrios(34:36): What Demetrios is seeing in open source AI models(42:44): One question Demetrios wishes to be asked(44:41): Demetrios’s advice for the audience(47:19): Backstage takeaways with executive producer, Audra Montenegro-------------------Links:LinkedIn - Connect with DemetriosRead the LLM Survey ReportListen to The MLOps Podcast

This bonus episode features conversations from season 5 of the Open||Source||Data podcast. In this episode, you’ll hear from Jaya Gupta, Partner at Foundation Capital; Yuliia Tkachova, Co-founder and CEO of Masthead Data; and Omoju Miller, Founder and CEO of Fimio.Sam sat down with each guest to discuss how they are building foundations for trust, inspiration, and reputation as we all race into the AI-centric future.You can listen to the full episodes from Jaya Gupta, Yuliia Tkachova, and Omoju Miller by clicking the links below.-------------------Episode Timestamps:(00:49): Jaya Gupta(01:48): Yuliia Tkachova(03:03): Omoju Miller-------------------Links:Listen to Jaya’s episodeListen to Yuliia’s episodeListen to Omoju’s episode

This episode features an interview with Jaya Gupta, Partner at Foundation Capital, where she leads early-stage investments across the enterprise software stack. Previously, Jaya was a Senior Business Analyst at McKinsey & Company focusing on software diligence and helping startups expand their go-to-market strategies.In this episode, Sam and Jaya discuss her journey to Foundation Model Ops, how software is becoming more accessible, and the democratization of AI tools.-------------------"At the end of the day, FMOps isn't just about the new tools. It's actually more about the new builders, the new workflows, and a completely new market of customers. I was on the other day, looking at LangChain's page of integrations, I don't know if you've seen it, but it's like Anyscale, Databricks, all these other huge legendary companies are integrating with LangChain, and I think it's clear that there's a huge community that is building something real and valuable." – Jaya Gupta-------------------Episode Timestamps:(01:05): What open source data means to Jaya(08:51): Jaya’s journey to Foundation Model Ops(15:58): How software is becoming more accessible(23:04): The democratization of AI tools(27:01): One question Jaya wishes to be asked(29:32): Jaya’s advice for the audience(31:51): Backstage takeaways with executive producer, Audra Montenegro-------------------Links:LinkedIn - Connect with JayaFollow Jaya on TwitterLearn more about FMOps

This episode features an interview with Omoju Miller, Founder and CEO of Fimio, a web3 reputation company. Originally from Lagos, Nigeria, Omoju holds a doctoral degree in Computer Science Education from UC Berkeley. Her expertise in machine learning and computational intelligence led her to companies such as Google and GitHub. Omoju also served as a volunteer advisor to the Obama administration’s White House Presidential Innovation Fellows.In this episode, Sam sits down with Omoju to discuss how machine learning can make applications more secure, what the future of the internet looks like, and the fascinating story behind Fimio.-------------------“So my first view is, in this future internet we have people, we also have bots, we have machines, we have code doing things. And bots sounds like such a horrible word now. [...] You need to have a level of trust on what that bot is. Everything from the humans to the machines collaborating in this decentralized world, we need to have some kind of reputation attached to each of those nodes. And the reason why we need that reputation is, as the thing scales, it becomes overwhelming to get value from it. You need something to help you filter, to find what you're looking for. Otherwise, you get stuck in that environment where you're just completely overwhelmed and you don't even know what to do. So I think of what I'm doing as just reputation to make this decentralized future slightly more attainable.” – Omoju Miller-------------------Episode Timestamps:(00:59): Omoju’s inspiration for starting Fimio(10:27): The future of smart contracts(28:47): Using mathematics to guarantee the safety of algorithms(34:34): What led Omoju to building a mathematical product(51:27): What open source data means to Omoju(55:38): One question Omoju wishes to be asked(57:47): Omoju’s advice for the audience(01:00:08): Backstage takeaways with executive producer, Audra Montenegro-------------------Links:LinkedIn - Connect with OmojuVisit Fimio

This episode features an interview with Yullia Tkachova, Co-founder and CEO of Masthead Data, an observability platform that catches anomalies in Google BigQuery in real-time. She holds degrees in Management Information Systems, Math, Statistics, and Marketing. Prior to Masthead, Yuliia designed complex BI products and solutions powered by ML and utilized by Fortune 500 companies.In this episode, Sam and Yuliia discuss how ML is shaping the future of data analytics, caring about users, and the fundamental human right to privacy.-------------------“We map those errors and anomalies on lineage, helping to understand what upstreams and downstreams are affected, what business users are affected. And that actually speeds up all the troubleshooting from hours to minutes. And this is the ultimate goal where we deliver. Because again, my belief that if you don't have this lineage piece was mapped anomalous in errors, it's not observability. It's monitoring. [...] What is also very unique to us, because Masthead operates on logs, it's triggered by logs. So, we do support streaming data. Unlike SQL-first solutions, as you can guess. We don't have to run SQL queries to see if they're anomalous, we’re triggered by logs. And this is also what sets us apart.” – Yuliia Tkachova-------------------Episode Timestamps:(01:14): What got Yuliia excited about math and statistics(11:31): The basic human right to privacy(18:21): What open source data means to Yuliia(28:00): Yuliia’s reason for building a solution focused on privacy and security(38:09): One question Yuliia wishes to be asked(42:21): Yuliia’s advice for the audience(44:46): Backstage takeaways with executive producer, Audra Montenegro-------------------Links:LinkedIn - Connect with YuliiaVisit Masthead Data

This episode features an interview with Maxim Fateev, Co-founder and CEO of Temporal, an open source, distributed, and scalable workflow orchestration engine capable of running millions of workflows. He has 20 years of experience architecting mission-critical systems at Uber, Google, Amazon, and Microsoft. In this episode, Sam sits down with Maxim to discuss workflow services, the power behind Temporal, and bringing determinism to highly complex environments.-------------------“[Temporal] has this notion of workflows, which can run for a very long time and handle external events, you can treat them as a durable actor. And they're very good at implementing a lifecycle. For example, you can have an object per model and let this object handle all the events. Like, new data came in, notify this object, this object will go and retrain it. Or, it'll run an activity to superiorly check the status. So you can have end-to-end lifecycle implemented fully in Temporal.” – Maxim Fateev-------------------Episode Timestamps:(01:03): What’s top of mind for Maxim in workflow services(04:09): What open source data means to Maxim(11:07): Maxim explains his time at AWS and building Cadence at Uber(23:09): Use cases and the community of Temporal(28:26): How Temporal is being used for ML workloads(32:28): One question Maxim wishes to be asked(36:38): Maxim’s advice for those working with complex distributed systems(39:11): Backstage takeaways with executive producer, Audra Montenegro-------------------Links:LinkedIn - Connect with MaximTemporal.ioWatch Maxim’s talk “Designing a Workflow Engine from First Principles”Replay Conference 2023