Tips and tricks for building resumes and acing interviews as a data scientist. Guest Anthony Kelly shares insights on maximizing job opportunities and navigating the current job market. Crafting a strong CV, highlighting project experiences, and optimizing LinkedIn profile are key topics discussed.

Explore why data scientists should know data engineering with Dan Sullivan, a software architect and data scientist. Learn about the advantages, challenges, and transitions in AI, MLOps, and cloud platforms. Discover the intersections of data roles, data warehouses, and data lakes in efficient data processing. Enhance data science efficiency and modeling through iterative feedback and skills in data engineering.

Mercado Libre built Fury, a platform for machine-learning solutions supporting 500 users. They discuss platform features, technology, and Carlos de la Torre's mysterious LinkedIn denial. The podcast covers challenges in ML ops, expansion within Mercado Libre, and the evolution of machine learning practices in Latin America.

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 Francisco, California that supports organizations looking to research, build, and deploy data science, machine learning, and AI products.  He has worked with clients like eBay, Blackrock, GoDaddy as well as widely successful startups such as Aardvark (acquired by Google) and Demand Media (the first public Billion dollar IPO after Google).    He is a world-renowned researcher, collaborating with UC Berkeley on the WeightWatcher project, and has taught at UC Berkeley and Stanford, and spoken at KDD, ICML, etc.   He is also currently a scientific advisor to the Page family’s Anthropocene Institute, consulting on areas including modern nuclear and quantum technologies and their response to the current pandemic.   Read more from Charles: http://calculatedcontent.com/ Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw   Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Charles on LinkedIn: https://www.linkedin.com/in/charlesmartin14/

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 backgrounds - econometrics, statistics, engineering, business analysts, and data engineers. Each of these groups has different expectations from a Machine Learning platforms. But, each group faces problems that have some common challenges - improving reproducibility, reducing technical debt, reducing the time to try new experiments. The challenge before any MLOps system is to create platforms and processes that address the needs of each of these groups.    Saurav is a tinkerer in the Machine Learning world with experience in the design and development of ML applications and processes.  In the past few years, he has been focused on improving the processes and tools around the Machine Learning teams. he explores the ideas of Auto ML, ML Ops, and model evaluation. He helps customers adopt and use the best tools and processes that allow them to scale their Data Science or Machine Learning tools. He has development experience in the open stack ML platforms and of late the managed ML services from Azure and AWS.   You can read his article about creating your own MLOps pipeline with open source tools here: https://towardsdatascience.com/mlops-reducing-the-technical-debt-of-machine-learning-dac528ef39de   Join our MLOps community slack:https://tinyurl.com/y75xmt7q Come to our next MLOps meetup: https://tinyurl.com/yajmywre   Connect with Saurav on LinkedIn: https://www.linkedin.com/in/sauravchakravorty/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

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 how an integrated storage solution simplifies model-building and increases ML productivity.    The presentation will examine how to optimize the daily workflows of data scientists, and eliminate complex and time-consuming manual tasks.   The talk will also highlight how efficient Kubeflow operations rely on Kubernetes storage primitives, such as Dynamic Volume Provisioning, Persistent Volumes and StatefulSets.  This integrated solution simplifies the configuration, operations and data protection for Kubeflow and generic K8s stateful apps in production-grade, multi-user environments. Bio: Josh Bottum is a Kubeflow Community Product Manager. His Community responsibilities include assisting users to quantify Kubeflow business value, develop critical user journeys (CUJs), triage incoming user issues, prioritize feature delivery, write release announcements and deliver Kubeflow presentations and demonstrations.  Mr. Bottum is also a VP of Arrikto. Arrikto simplifies storage operations for stateful Kubernetes applications by enabling efficient local storage architectures with data durability and portability.  Arrikto is a core code contributor to Kubeflow. Join our MLOps community slack Connect with Demetrios Brinkmann on LinkedIn

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, and how Alex sees the role of a Machine Learning Engineer. Alex has led the hiring process of Machine Learning Engineers and shared learnings on candidates and businesses alike.    Alex is the Lead Data Scientist at TrueLayer, focussing on building Open Banking API products powered by data. Prior to TrueLayer, he built predictive models in Financial Services, used social data to predict the “next-big-thing” in Fast Moving Consumer Goods and introduced Machine Learning techniques in subsurface imaging.   His academic background is in Applied Mathematics & Statistics.   Check out his blog entries for more info: https://blog.truelayer.com/improving-the-classification-of-your-transaction-data-with-machine-learning-c36d811e4257 https://alexiospanos.com/hiring-machine-learning-engineers-part-1/ https://alexiospanos.com/hiring-machine-learning-engineers-part-2/ Connect with Demetrios on LinkedIn:  https://www.linkedin.com/in/dpbrinkm/  Connect with Alex on Linkedin:  https://www.linkedin.com/in/alexspanos/ Join us on slack: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw

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 process. We will touch upon topics like classes of consumers, disciplined data transformation code, metadata and lineage, state management, and namespaces. This system and discussion complements work done on data science platforms such as Domino and Dotscience. Bio: Dr. Venkata Pingali is Co-Founder and CEO of Scribble Data, an ML Engineering company with offices in India and Canada. Scribble’s flagship enterprise product, Enrich, enables organizations to address 10x analytics/data science usecases through trusted production datasets. Before starting Scribble Data, Dr. Pingali was VP of Analytics at a data consulting firm and CEO of an energy analytics firm. He has a BTech from IIT Mumbai and a PhD from USC in Computer Science. Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Venkata on LinkedIn: https://www.linkedin.com/in/pingali/

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 like active failures and latent conditions. This talk will discuss risk assessment in ML Systems from the perspective of reliability, safety and especially causal aspects that can lead to the rise of silent risks in said systems. Slides to the talk can be found here Bio: Flavio Clesio is Machine Learning Engineer (NLP, CV, Marketplace RecSys) and at the moment works at MyHammer AG, where he helps build Core Machine Learning applications to exploit revenue opportunities and automation in decision making. Prior to MyHammer, Flavio was a Data Intelligence lead in the mobile industry, and business intelligence analyst in financial markets, specifically in Non-Performing Loans. He holds a master’s degree in computational intelligence applied in financial markets (exotic credit derivatives). This was a virtual fireside chat between Flavio Clesio, Demetrios Brinkmann and the MLOps community. Relevant links can be found below. Join our MLOps slack community: https://bit.ly/3aOTwgR and register for the next meetup here. Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with FLavio Clesio on Linkedin: https://www.linkedin.com/in/flavioclesio/

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 how to make your data more usable to accelerate ML model development.   Shubhi Jain is a machine learning engineer at SurveyMonkey where he develops and implements machine learning systems for its products and teams. Occasionally, he’ll create YouTube videos about Machine Learning in collaboration with Springboard, an e-learning platform. He’s always excited to bring his expertise and passion for Data and AI systems to the rest of the industry. In his free time, Shubhi likes hiking with his dog and accelerating his hearing loss at live music shows.   This was a virtual fireside chat between Shubhi Jain, Demetrios Brinkmann and the MLOps community. Relevant links can be found below. Join our MLOps slack community: https://bit.ly/3aOTwgR and register for the next meetup here. Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/  Connect with Shubhi Jain on Linkedin: https://www.linkedin.com/in/shubhankarjain/ Check out more of Shubhi on youtube: https://www.youtube.com/watch?v=XsD2u7hAwI8 https://www.youtube.com/watch?v=vcPNp21Mdg0 https://www.youtube.com/watch?v=92kSljmHS7U https://www.oreilly.com/strata-san-jose-2020/