MLOps coffee sessions coming at you with our primer episode talking bout kfserving! David Aponte and Demetrios Brinkmann dive deep into what model serving is in machine learning, what different types of serving there is, what serverless means, API endpoints, streaming and batch data and a bit of coffee vs tea banter. ||Show Notes|| ML in Production is Hard Blog article by Nikki: http://veekaybee.github.io/2020/06/09/ml-in-prod/?utm_campaign=Data_Elixir&utm_source=Data_Elixir_289 Interactive ...
Jun 13, 2020•50 min•Season 1Ep. 1
MLOps.community meetup #17 a deep dive into the open source ML framework Hermoine built on top of MLflow with Neylson Crepalde Key takeaways for attendees: MLOps problems are dealt with tools but also with processes Open-source framework Hermione can help in a lot of parts of the operations process Abstract: In Neylson's experience with Machine Learning projects, he has encountered a series of challenges regarding agile processes to build and deploy ML models in a professional cooperative enviro...
Jun 11, 2020•1 hr 1 min•Season 1Ep. 17
Venture Capital in Machine Learning Startups With John Spindler CEO of Capital Enterprise. John Spindler CEO of Capital Enterprise. We talked about what trends he has been seeing within MLOps, ML companies and also how he evaluates a deal. John Spindler has over 15 years experience as an entrepreneur and business advisor/consultant and as well as being responsible for the day to day management of Capital Enterprise he is also a general partner at AI Seed, an early-stage fund that invests in high...
Jun 06, 2020•57 min•Season 1Ep. 16
Human In The Loop Machine Learning and how to scale it with Robert Munro. This conversation centered around the components of Human-in-the-Loop Machine Learning systems and the challenges when scaling them. Most machine learning applications learn from human examples. For example, autonomous vehicles know what a pedestrian looks like because people have spent 1000s of hours labeling “pedestrians” in videos; your smart device understands you because people have spent 1000s of hours labeling the i...
Jun 04, 2020•55 min•Season 1Ep. 15
The amazing Byron Allen talks to us about why MLflow and Kubeflow are not playing the same game! ML flow vs Kubeflow is more like comparing apples to oranges or as he likes to make the analogy they are both cheese but one is an all-rounder and the other a high-class delicacy. This can be quite deceiving when analyzing the two. We do a deep dive into the functionalities of both and the pros/cons they have to offer. Byron is a Senior Consultant at Servian - a data consultancy in Australia that als...
May 28, 2020•55 min•Season 1Ep. 14
Resume building and Interviewing tips for data scientists and Machine learning engineers. When on the job hunt there are some tested tips and tricks that can be applied to your resume and interviews which will give you a leg up on the rest of the competition. Anthony Kelly host of the AI in Action podcast and Executive Search Consultant focused on Machine Learning and Data Science sat down with us to talk about what some of the best resumes and CV's have in common. We spoke about optimizing your...
May 27, 2020•58 min•Season 1Ep. 13
MLOps meetup #12 // What are the advantages for a data scientist to know data engineering? What good is learning Data Engineering skills? These days full stack is overflowing with all the different things you need to know about so why learn data Engineering now? Our guest on this meetup will make the case for what the advantages are if you do decide to learn data engineering and also go into depth on how to do data engineering in the cloud. Dan Sullivan is a software architect and data scientist...
May 21, 2020•1 hr
MLOps community meetup #11 Machine Learning at scale in Mercado Libre with Carlos de la Torre Mercado Libre hosts the largest online commerce and payments ecosystem in Latin America. The IT department built Fury: a PaaS framework for the development and deployment of multi-cloud, multi-technology, microservices. This platform leveraged the growth of the IT area, which now counts ~4000 people. As such, it lacked support for machine-learning based solutions: an experimentation environment for data...
May 16, 2020•59 min•Season 1Ep. 11
MLOps.community meetup #9 with Charles Martin - 10 years deploying Machine Learning in the Enterprise: The Inside Scoop! Why do some machine learning projects succeed while others fall down completely? In this discussion, we will discuss the real-world challenges that Enterprises face in deploying ML solutions, focussing on challenges with existing, legacy dev-ops environments and how certain patterns of success emerge to help combat failure. Dr. Martin runs a boutique consultancy in San Francis...
May 14, 2020•1 hr 3 min•Season 1Ep. 9
Meet up #10 Saurav Chakravorty sat down with us to talk about his vision of how MLOps reflect the old Indian story of blind men and an Elephant. As a lead data scientist at Brillo Saurav has build many MLOps pipelines and experienced using different ML platforms. He comes to talk with us about the difficulties of taking an ML platform from infancy to production and other key factors he has seen within the MLOps space. Today data science is a field that is an aggregation of people from various ba...
May 08, 2020•55 min•Season 1Ep. 10
Linkedin, Spotify, Volvo, JP Morgan, and many other market leaders are leveraging Kubeflow to simplify the creation and the efficient deployment of Machine Learning models on Kubernetes. This presentation will provide an update on the Kubeflow 1.0 release, and review the Community’s best practices to support Critical User Journeys, which optimize ML workflows. As a data scientist will often need to build (and save) hundreds of variants of their model, this session will provide a deeper dive into...
May 01, 2020•1 hr 4 min•Season 1Ep. 8
What does the MLOps pipeline at London Based FinTech startup TrueLayer look like? London Based Fintech start-up TrueLayer decided to use Machine Learning instead of a rule-based system in mid-2019 and in our 7th meetup we spoke to their lead data scientist Alex Spanos about everything that entailed. During the meetup, we dove into how TrueLayer architected their MLOps pipeline for their Open Banking API: more specifically which tools they use and why, what prompted them to use machine learning, ...
Apr 24, 2020•57 min•Season 1Ep. 7
In our 6th meetup, we spoke with the CEO of Scribble Data Dr. Venkata Pingali. Scribble helps build and operate production feature engineering platforms for sub-fortune 1000 firms. The output of the platforms is consumed by data science and analytical teams. In this talk we discuss how we understand the problem space, and the architecture of the platform that we built for preparing trusted model-ready datasets that are reproducible, auditable, and quality checked, and the lessons learned in the ...
Apr 16, 2020•59 min•Season 1Ep. 6
In our 5th meetup, we spoke with the Brasilian ML Engineer Flavio Clesio. Machine Learning Systems play a huge role in several businesses from the Banking industry to recommender systems in entertainment applications until health domains. The era of " A Data Scientist with a Script in a single machine " is officially over in high stakes ML. We're entering an era of Machine Learning Operations (MLOps) where those critical applications that impact society and businesses need to be aware of aspects...
Apr 15, 2020•55 min•Season 1Ep. 5
MLOps Community Meetup #4 With Shubhi Jain In the 4th online meetup for our MLOps.community We spoke with Shubhi Jain, Machine Learning Engineer and an all-around great guy! Every organization is leveraging machine learning (ML) to provide increasing value to their customers and understand their business. You may have created models too. But, how do you scale this process now? In this case study, we looked at how to pinpoint inefficiencies in your ML data flow, how SurveyMonkey tackled this, and...
Apr 10, 2020•58 min•Season 1Ep. 4
MLOps community meetup #3! Last Wednesday we talked to Phil Winder, CEO, Winder Research. //Abstract Phil Winder of Winder Research joined us for the 3rd instalment of our MLOps community meetup. In this clip taken from the long conversation, he speaks about why or why not he sees companies automating the retraining of Machine Learning Models. You can find the whole conversation here: https://www.youtube.com/watch?v=MRES5IxVnME The topic of conversation for our virtual meetup was an in-depth loo...
Apr 03, 2020•57 min•Season 1Ep. 3
MLOps community meetup #2! Last Wednesday we talked to Charles Radclyffe, Technology Governance and ESG Specialist, AI Ethics. What does best in class AI/ML governance look like in financial services? For this episode, we are joined by Charles Radclyffe, who until very recently was the Head of AI at Fidelity. Some of his other feats include starting 3 companies, TEDx talks, and advising the likes of HSBC, Barclays, Morgan & Stanley, and Deutsche Bank. He has focused his career on solving tou...
Mar 26, 2020•1 hr 2 min•Season 1Ep. 2
The 1st MLOps.community meetup on 3.18.2020 featuring Luke Marsden from Dotscience. What is MLOps and how can it help me work remotely? The first episode of our weekly MLOps community virtual meetup with CEO and founder of the MLOps platform dotscience Luke Marsden talk to us about the current state of Machine Learning, what some of the main difficulties are at this stage when developing models, how the machine learning lifecycle differs from traditional software development and a deep dive of c...
Mar 23, 2020•34 min•Season 1Ep. 1
