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PODCAST

MLOps.community

Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)

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All Episodes

00:58:22

Real-time Feature Pipelines, A Personal History //...

MLOps.community ·

2021/01/08

MLOps community meetup #46! Last Wednesday, we talked to Hendrik Brackmann, Director of Data Science and Analytics at Tide. // Abstract: Tide is a U.K.-based FinTech startup with offices in London, Sofia, and Hyderabad. It is one of the first, and the largest business banking platform in the UK, with over 150,000 SME members. As of 2019, one of Tide’s main focuses is to be data-driven. This resulted in the forming of a Data Science and Analytics Team with Hendrik Brackmann at its head. Let's

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01:00:25

Machine Learning Design Patterns // Sara Robinson //...

MLOps.community ·

2020/12/29

Coffee Sessions #24 with Sara Robinson of Google, Machine Learning Design Patterns co-hosted by Vishnu Rachakonda. //Bio Sara is a Developer Advocate for Google Cloud, focusing on machine learning. She inspires developers and data scientists to integrate ML into their applications through demos, online content, and events. Before Google, she was a Developer Advocate on the Firebase team. Sara has a Bachelor’s degree from Brandeis University. When she’s not writing code, she can be found on a spin

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01:12:03

SRE for ML Infra // Todd Underwood // MLOps Coffee...

MLOps.community ·

2020/12/22

Coffee Sessions #23 with Todd Underwood of Google, Followups from OPML Talks on ML Pipeline Reliability co-hosted by Vishnu Rachakonda. //Bio Todd is a Director at Google and leads Machine Learning for Site Reliability Engineering Director. He is also Site Lead for Google’s Pittsburgh office. ML SRE teams build and scale internal and external ML services and are critical to almost every Product Area at Google. Before working at Google, Todd held a variety of roles at Renesys. He was in charge of

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00:54:03

How To Move From Barely Doing BI to Doing AI // Joe...

MLOps.community ·

2020/12/20

MLOps community meetup #45! Last Wednesday, we talked to Joe Reis, CEO/Co-Founder of Ternary Data. // Abstract: The fact is that most companies are barely doing BI, let alone AI. Joe discussed ways for companies to build a solid data foundation so they can succeed with machine learning. This meetup covers the continuum from cloud data warehousing to MLOps. // Bio: Joe is a Data Engineer and Architect, Recovering Data Scientist, 20 years in the data game. Joe enjoys helping companies make sense of

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00:55:42

Deep in the heart of data // Carl Steinbach // MLOps...

MLOps.community ·

2020/12/18

Coffee Sessions #22 with Carl Steinbach of LinkedIn, Deep in the Heart of Data. //Bio Carl is a Senior Staff Software Engineer and currently the Tech Lead for LinkedIn's Grid Development Team. He is a contributor to Emerging Architectures for Modern Data Infrastructure //Other links referenced by

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00:36:04

When machine learning meets privacy - Episode 7

MLOps.community ·

2020/12/17

ML and Encryption - It's all about secure insights #7! In this episode, we've invited Théo Ryffel, Founder of Arkhn and founding member of the Open-Mined community. // Abstract: In this episode, Théo introduces us to the concept of encrypted Machine Learning, when and the best practices to have it applied in the development of Machine Learning based solutions, and the challenges of building a community. //Other links to check on

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00:35:53

When Machine Learning meets privacy - Episode 6

MLOps.community ·

2020/12/14

**Privacy-preserving ML with Differential Privacy** Differential privacy is without a question one of the most innovative concepts that came around in the last decades, with a variety of different applications even when it comes to Machine Learning. Many are organizations already leveraging this technology to access and make sense of their most sensitive data, but what is it? How does it work? And how can we leverage it the most? To explain this and provide us a brief intro on Differential Privacy,

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00:56:22

Human-centric ML Infrastructure: A Netflix Original...

MLOps.community ·

2020/12/14

MLOps community meetup #44! Last Wednesday, we talked to Savin Goyal, Tech lead for the ML Infra team at Netflix. // Abstract: In this conversation, Savin talked about some of the challenges encountered and choices made by the Netflix ML Infrastructure team while developing tooling for data scientists. // Bio: Savin is an engineer on the ML Infrastructure team at Netflix. He focuses on building generalizable infrastructure to accelerate the impact of data science at Netflix. // Other links to check

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00:47:26

A Conversation with Seattle Data Guy // Benjamin...

MLOps.community ·

2020/12/08

Coffee Sessions #21 with Benjamin Rogojan of Seattle Data Guy, A Conversation with Seattle Data Guy //Bio Ben has spent his career focused on all forms of data. He has focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. He has also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. Ben privately consults on data

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01:04:06

Monzo Bank - An MLOps Case Study // Neal Lathia //...

MLOps.community ·

2020/12/07

Coffee Sessions #20 with Neal Lathia of Monzo Bank, talking about Monzo Bank - An MLOps Case Study //Bio Neal is currently the Machine Learning Lead at Monzo in London, where his team focuses on building machine learning systems that optimise the app and help the company scale. Neal's work has always focused on applications that use machine learning - this has taken him from recommender systems to urban computing and travel information systems, digital health monitoring, smartphone sensors, and

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00:33:24

When Machine Learning meets privacy - Episode 5

MLOps.community ·

2020/12/03

**The intersection between DataOps and privacy** DataOps is considered by many as the new era of data management, a set of principles that emphasizes communication, collaboration, integration, and automation of cooperation between the different teams in an organization that have to deal with data: data engineers, data scientists to data analysts. But is there any relation between DataOps and data privacy protection? Can organizations leverage DataOps to ensure that their data is privacy

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00:22:57

When Machine Learning meets privacy - Episode 4

MLOps.community ·

2020/11/26

**Are Privacy Enhancing Technologies a myth** Data Privacy and machine learning are here to stay, and there’s no doubt they’re the hot trends to be following. But do they need to clash with each other? Can we have these titans to co-exist? It seems like finally 2020 and 2021 will be the years where Privacy Enhancing Technologies. But after all what are they? How are these techs being used and leveraged by organizations? Useful

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01:01:06

Introducing Data Downtime: From Firefighting to...

MLOps.community ·

2020/11/24

Coffee Sessions #19 with Barr Moses of Monte Carlo, Introducing Data Downtime: How to Prevent Broken Data Pipelines with Observability co-hosted by Vishnu Rachakonda //Bio Barr Moses is CEO & Co-Founder of Monte Carlo, a data observability company backed by Accel and other top Silicon Valley investors. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and among other functions, built the data/analytics team. Prior

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00:58:39

The Current MLOps Landscape // Nathan Benaich &...

MLOps.community ·

2020/11/23

MLOps community meetup #43! Last Wednesday, we talked to Nathan Benaich, General Partner at Air Street Capital and Timothy Chen, Managing Partner at Essence VC about The MLOps Landscape. // Abstract: In this session, we explored the MLOps landscape through the eyes of two accomplished investors. Tim And Nathan shared with us their experience in looking at hundreds of ML and MLOps companies each year to highlight major insights they have gained. What do the ML infrastructure and tooling landscape

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00:51:44

When Machine Learning meets privacy - Episode 3 with...

MLOps.community ·

2020/11/19

**AI and ethical dilemmas** Artificial Intelligence is seen by many as a vehicle for great transformation, but for others, it still remains a mystery, and many questions remain unanswered: will AI systems rule us one day? Can we trust AI to rule our criminal systems? Maybe create political campaigns and dominate political advertisements? Or maybe something less harmful, do our laundry? Some of these questions may sound absurd, but they are for sure making people shift from thinking purely about

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00:58:53

UN Global Platform // Mark Craddock // Co-Founder &...

MLOps.community ·

2020/11/16

MLOps community meetup #42! Last Wednesday, we talked to Mark Craddock, Co-Founder & CTO, Global Certification and Training Ltd (GCATI), about UN Global Platform. // Abstract: Building a global big data platform for the UN. Streaming 600,000,000+ records / day into the platform. The strategy developed using Wardley Maps and the Platform Design Toolkit. // Bio: Mark contributed to the Cloud First policy for the UK Public sector and was one of the founding architects for the UK Governments

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00:35:37

When Machine Learning meets Data Privacy - Episode 2...

MLOps.community ·

2020/11/12

What are regulations saying about data privacy? We are already aware of the importance of using Machine Learning to improve businesses, nevertheless to feed Machine Learning, data is a must, and in many cases, this data might even be considered sensitive information. So, does this mean that with new privacy regulations, access to data will be more and more difficult? ML and Data Science have their days counted? Or Will Machine beat privacy? To answer all these questions I’ve invited Cat Coode, an

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01:00:54

When You Say Data Scientist Do You Mean Data...

MLOps.community ·

2020/11/10

In this episode, we talked to Elizabeth Chabot, Consultant at Deloitte, about When You Say Data Scientist Do You Mean Data Engineer? Lessons Learned From StartUp Life. // Key takeaways: If you have a data product that you want to function in production, you need MLOps Education needs to happen about the data product life cycle, noting that ML is just part of the equation Titles need to be defined to help outside users understand the differences in roles // Abstract: ML and AI may sound sexy to

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01:00:29

Metaflow: Supercharging Our Data Scientist...

MLOps.community ·

2020/11/10

MLOps community meetup #41! Last Wednesday was an exciting episode that some attendees couldn't help to ask when is the next season of their favorite series! The conversation was around Metaflow: Supercharging Data Scientist Productivity with none other than Netflix’s very own Ravi Kiran Chirravuri. // Abstract: Netflix's unique culture affords its data scientists an extraordinary amount of freedom. They are expected to build, deploy, and operate large machine learning workflows autonomously

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00:47:22

Luigi in Production // MLOps Coffee Sessions #18 //...

MLOps.community ·

2020/11/09

Coffee Sessions #18 with Luigi Patruno of ML in Production, a Centralized Repository of Best Practices Summary Luigi Patruno and ML in production MLOps workflow: Knowledge sharing and best practices Objective: learn! Links: ML in production: https://mlinproduction.com/ Why you start MLinProduction: https://mlinproduction.com/why-i-started-mlinproduction/ Luigi Patruno: a man whose goal is to help data scientists, ML engineers, and AI product managers, build and operate machine learning systems in

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514 results

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