Coffee Sessions #47 with Jeremy Howard, fast.ai, AutoML, Software Engineering for ML. //Abstract Advancement in ML Workflows: You've been around the ML world for long enough to have seen how much workflows, tooling, frameworks, etc. have matured and allowed for greater scale and access. We'd love to reflect on your personal journey in this regard and hear about your early experiences putting models into production, as well as how you appreciate/might improve the process now. Data Professional Diversity and MLOps: Your work at fast.ai, Kaggle, and now with NBDEV has played a huge part in supercharging a diverse ecosystem of professionals that contribute to ML-like ML/data scientists, researchers, and ML engineers. As the attention turns to putting models into production, how do you think this range of professionals will evolve and work together? How will things around building models change as we build more? Turning Research into Practice: You've consistently been a leader in applying cutting-edge ideas from academia into practical code others can use. It's one of the things I appreciate most about the fast.ai course and package. How do you go about picking which ideas to invest in? What advice would you give to industry practitioners charged with a similar task at their company? // Bio Jeremy Howard is a data scientist, researcher, developer, educator, and entrepreneur. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, the chair of WAMRI, and is Chief Scientist at platform.ai. Previously, Jeremy was the founding CEO of Enlitic, which was the first company to apply deep learning to medicine, and was selected as one of the world’s top 50 smartest companies by MIT Tech Review two years running. He was the President and Chief Scientist of the data science platform Kaggle, where he was the top-ranked participant in international machine learning competitions for 2 years running. He was the founding CEO of two successful Australian startups (FastMail, and Optimal Decisions Group–purchased by Lexis-Nexis). Before that, he spent 8 years in management consulting, at McKinsey & Co, and at AT Kearney. Jeremy has invested in, mentored, and advised many startups, and contributed to many open-source projects. He has many media appearances, including writing for the Guardian, USA Today, and The Washington Post, appearing on ABC (Good Morning America), MSNBC (Joy Reid), CNN, Fox News, BBC, and was a regular guest on Australia’s highest-rated breakfast news program. His talk on TED.com, “The wonderful and terrifying implications of computers that can learn”, has over 2.5 million views. He is a co-founder of the global Masks4All movement. // Other Links: jhoward.fastmail.fm enlitic.com jphoward.wordpress.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Jeremy on LinkedIn: https://www.linkedin.com/in/howardjeremy/ Timestamps: [00:00] Introduction to Jeremy Howard [02:11] Jeremy's background [03:10] Workflow [12:59] Platform development [19:53] Balancing API [22:57] Moment of inefficiency [27:42] Helpful tactics [29:05] University of tools evolving   [41:10] Resources to solve problems [43:30] Jupiter notebooks [47:20] Putting Jupiter notebooks into production [48:42] MBDev [51:20] Jeremy's experiences and frustrations with putting ML into production

Coffee Sessions #46 with Pablo Estevez, What We Learned from 150 Successful ML-enabled Products at Booking.com. //Abstract While most of the Machine Learning literature focuses on the algorithmic or mathematical aspects of the field, not much has been published about how Machine Learning can deliver meaningful impact in an industrial environment where commercial gains are paramount. We conducted an analysis on about 150 successful customer-facing applications of Machine Learning, developed by dozens of teams in Booking.com, exposed to hundreds of millions of users worldwide, and validated through rigorous Randomized Controlled Trials. Our main conclusion is that an iterative, hypothesis-driven process, integrated with other disciplines was fundamental to build 150 successful products enabled by Machine Learning. // Bio Pablo Estevez is the Principal Data Scientist at Booking.com. He has worked on recommendations, personalization, and experimentation across the Booking.com website, as well as as a manager on several machine learning, data science, and product development teams. // Other Links Talk on the topic: https://www.youtube.com/watch?v=ljhtfrtuNqw&t=4h24m30s The paper: https://blog.kevinhu.me/2021/04/25/25-Paper-Reading-Booking.com-Experiences/bernardi2019.pdf --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Pablo on LinkedIn: https://www.linkedin.com/in/estevezpablo/

MLOps community meetup #70! Last Wednesday, we talked to Monika Venckauskaite, Senior Machine Learning Engineer at Vinted. //Abstract One of the areas, that is the most transformed by ML these years is cybersecurity. Traditionally, SIEM (Security Intelligence and Event Management) is performed by human analysts. However, as the cyber powers and tools of the world are growing, we need more and more of these specialists. The entire area of cybersecurity is experiencing a shortage of talent. This is where the ML is coming in to help us. Cybersecurity ML systems require a lot of expertise from specialists as well as unique ways of handling user-sensitive data. This imposes various architectural solutions. In this talk, Monika introduces us to the ways of using ML in cybersecurity and the unique challenges we face. //Bio Monika is a keen and curious ML engineer, loving to build systems. She's started in machine learning as a master's student, looking for Higgs Boson and Dark matter within the CERN data. Later on, Monika moved to the IT industry and worked on various machine learning projects, including Open Source Intelligence Tools and a distributed system for ML cybersecurity analytics. Currently, Monika works as an MLOps engineer, improving the MLOps platform that is used in production to shipping models to a 45 million-user platform. Monika also works in a start-up that is innovating satellite communication. In her free time, she loves books, traveling, and playing some music. // Takeaways Cyber threats are all around us. ML as technology is both a savior and a threat. GDPR and sensitive user data bring in extra challenges for cybersecurity intelligence systems, leading to more complex architectural decisions. ML helps to fight the talent shortage. Cybersecurity requires real-time ML systems and reacting ASAP. ----------- Connect With Us ✌️-------------    Join our Slack community:  https://go.mlops.community/slack Follow us on Twitter:  @mlopscommunity Sign up for the next meetup:  https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Monika on LinkedIn: https://www.linkedin.com/in/monika-in-space/ Timestamps: [00:00] Introduction to Monika Venčkauskaitė [05:50] Monika's background in tech [08:50] Machine Learning in Cyber Security [09:37] Content [10:19] Our world is run by machines     [11:16] Cybersecurity Threats [12:44] Cybersecurity Incident Response                         Cycle:                         1. Identify           2. Protect           3. Detect           4. Respond           5. Recover [25:05] The Iceberg                          Surface Web - 4% Indexed and easily searchable            Deep Web - 90% Not Indexed, tougher to find            Dark Web - 6% Obscured, difficult to discover [47:45] Recommendation: AI Superpowers: China, Silicon Valley, And The New World Order by Kai-Fu Lee (https://www.amazon.com/AI-Superpowers-China-Silicon-Valley/dp/132854639X) [50:54] "I think we are going in the same direction but our implementations are different."

Coffee Sessions #45 with Diego Oppenheimer of Algorithmia, Enterprise Security and Governance MLOps. //Abstract MLOps in the enterprise is difficult due to security and compliance. In this MLOps Coffee Session, the CEO of Algorithmia, Diego talks to us about how we can better approach MLOps within the enterprise. This is an introduction to essential principles of security in MLOps and why it is crucial to be aware of security best practices as an ML professional. // Bio Diego Oppenheimer is co-founder and CEO of Algorithmia. Previously, he designed, managed, and shipped some of Microsoft’s most used data analysis products including Excel, Power Pivot, SQL Server, and Power BI. He holds a Bachelor’s degree in Information Systems and a Master’s degree in Business Intelligence and Data Analytics from Carnegie Mellon University. --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Diego on LinkedIn: https://www.linkedin.com/in/diego/ Timestamps: [00:00] Thank you Diego and Algorithmia for sponsoring this session! [01:04] Introduction to Diego Oppenheimer [02:55] Security [04:42] "The level of scrutiny for apps and development and that of the operational software is much higher." [07:40] "We take the Ops part of MLOps very, very seriously and it's really about the operational side of the equation." [09:22] MLSecOps [11:42] "The code doesn't change, but things change cause the data changed." [15:23] Maturity of security [18:45] "To a certain degree, we have general parameters of software DevOps In software engineering and DevOps, and we're adapting it to this new world of ML."   [19:03] Development workflow [20:58] "In the ideal world, you're just sitting in your data science platform, your auto ML platform, whatever it is that you're working with, you can push a model." [22:50] Security, responsibility and authentication [23:38] "What you don't want to learn is how to do automation every single time there's a new use case. That's just not a good use of your time."  [24:30] Hurdles needed to be cleared [24:47] "I would argue that there's no such thing as Bulletproof in software. That doesn't exist. It never has and never will." [26:25] Machine Learning security risks                         1. Operational risk           2. Brand risk           3. Strategic risk [28:23] Machine Learning security risk standards [31:11] "There's a world where you can reverse engineer a model by essentially feeding a whole bunch of data and understanding where that comes back." [33:55] How to change the mindset of relaxed companies when it comes to security [35:19] "It takes time and money to figure out security." [37:52] Conscientious when building systems [39:44] "Look at the end result of the workflow and understand the value of that workflow, which you should know at that point because if you're going into an ML workflow without understanding what the end value is going to be, it's not a good sign." [40:19] Root cause analysis [41:00] Threat modeling [41:14] "There's a natural next step where there's threat modeling for ML systems and it's a task that gets built and understood, and nobody's going to enjoy doing it."   [43:07] Security as code [45:29] MLRE

Coffee Sessions #44 with Grant Wright of SEEK Ltd., Autonomy vs. Alignment: Scaling AI Teams to Deliver Value. /Abstract Setting AI teams up for success can be difficult, especially when you’re trying to balance the need to provide teams with autonomy to innovate and solve interesting problems while ensuring they are aligned to the organizations' strategy. Operating models, rituals and processes can really help to set teams up for success; but, there is no right answer, and as you scale and priorities change your approach needs to change too. Grant shares some of his learnings in establishing a cross-functional team of data scientists, engineers, analysts, product managers, and otologists to solve employment information problems at SEEK, and how the team has evolved as they’ve scaled from a team of 30 in Melbourne Australia to over 100 team members across 5 countries in the past three years. // Bio Grant heads the Artificial Intelligence & Product Analytics teams at SEEK, where he leads a global team of over 120 Data Scientists, Software Engineers, Ontologists, and AI Product Managers who deliver AI Services to online employment and education platforms across the Asia Pacific and the Americas. Grant has held various strategy and product and tech leadership roles over the past 15 years, with experience in scaling, AI teams to deliver outcomes across multiple geographies. Grant holds a Bachelor of Computer and Information Science (Software Development) and a Bachelor of Business (Economics) from the Auckland University of Technology. --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Grant on LinkedIn: https://www.linkedin.com/in/wrightgrant/

In this Machine Learning System Design Review, Shaji Chennan Kunnummel walks us through the system design for Pinterest’s near-real-time architecture for detecting similar images. We discuss their usage of Kafka, Flink, rocksdb, and much more. Starting with the high-level requirements for the system, we discussed Pinterest’s focus on debuggability and an easy transition from their batch processing system to stream processing. We then touch on the different system interfaces and components involved such as Manas—Pinterest’s custom search engine—and how it all ends up in their custom graph database, downstream Kafka streams, and to Pinterest’s feature store—Galaxy. With Shaji’s expert knowledge of the system, we were able to do a deep dive into the system’s architecture and some of its components. // Experiences 15+ years of experience in software product development. Led multiple teams in a highly agile, collaborative, and cross-functional environment. Designed and implemented highly scalable, fault-tolerant, and optimized distributed systems that scale to handle millions of requests per second. In-depth knowledge of Object-oriented programming and design patterns in C++/Java/Python/Golang. Designed and built complex data pipelines and microservices to train and serve machine learning models. Built analytics pipelines for processing and mining high-volume data set using Hadoop and Map-Reduce frameworks. In-depth knowledge of distributed storage, consistency models, NoSQL data modeling, Cloud computing environment (AWS and Google Cloud).

MLOps community meetup #69! Last Wednesday we talked to Emmanuel Raj, Senior Machine Learning Engineer at TietoEvry. //Abstract The talk focuses on simplifying/demystifying MLOps, encourages others to take steps to learn this powerful SE method. We also talked about Emmanuel's journey in ML engineering, the evolution of MLOps, daily life, and SE problems, and what's next in MLOps (fusion of AIOps, EU AI regulations impact on MLOps workflow, etc). //Bio Emmanuel Raj is a Finland-based Senior Machine Learning Engineer. He is a passionate ML Researcher, Software engineer, speaker, and author. He is also a Machine Learning Engineer at TietoEvry and a Researcher at Arcada University of Applied Sciences in Finland. With over 6+ years of experience building ML solutions in the industry, he has worked on multiple domains such as Healthcare, Manufacturing, Finance, Retail, e-commerce, aviation, etc.    Emmanuel is passionate about democratizing AI and bringing state-of-the-art research to the industry. He has a keen interest in R&D in technologies such as Edge AI, Blockchain, NLP, MLOps, and Robotics. He believes the best way to learn is to teach and he is passionate about teaching about new technologies, that's one reason for writing a book and making an online course on MLOps.    Emmanuel is the author of the book "Engineering MLOps". The book covers industry best case practices and hands-on implementation to Rapidly build, test, and manage production-ready machine learning life cycles at scale. There is a big evolution happening in Data science for good, and we are moving away from notebooks and models sharing to a collaborative way of working via MLOps. We will discuss this big evolution of DevOps, MLOps, Data Engineering, Data Science, and Data-Driven business in the meetup. ----------- Connect With Us ✌️-------------    Join our Slack community:  https://go.mlops.community/slack Follow us on Twitter:  @mlopscommunity Sign up for the next meetup:  https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Emmanuel on LinkedIn: https://www.linkedin.com/in/emmanuelraj7/ // Other Links:   www.emmanuelraj.com https://www.youtube.com/watch?v=m32k9jcY4pY https://www.youtube.com/watch?v=1sGECHbc9zg

MLOps community meetup #68! Last Wednesday we talked to Veselina Staneva of TeachableHub, Simarpal Khaira of Intuit, and Korri Jones of Chick-fil-A, Inc. //Abstract Building, Designing, or even just casting the vision for MLOps for your company, whether a large corporation or an agile start-up up, shouldn't be a nigh-impossible task. Complex, but not an impossible mountain to climb.    In this meetup, we talked about the steps necessary to unlock the potential of data science for your organization, regardless of size. //Bio Veselina Staneva - Co-founder & Head of Product, TeachableHub Over the past few years, Vesi work at a product company called CloudStrap.io, where together with her team they are simplifying cloud technologies and crafting modern solutions that lay a solid foundation for digital transformation at scale. Vesi's main focus currently is their new product TeachableHub.com - an ML deployment and serving platform for teams, where she heads Product and Customer Development. In the past, Vesi had quite a diverse experience in managing projects for global enterprise companies such as telecommunications and internet service provider GTT and managed printing services giant HPInc, as well as deep-diving into e-commerce business development while running online stores on 7 Amazon markets as well as WordPress shops, where she managed to get from 0 to $30K MRR in less than a year without a dollar spent on paid advertising. In Vesi's free time, she enjoys spending the rest of her energy doing all kinds of sports, as well as participate in non-professional triathlons and mountain bike ultra races. Simarpal Khaira - Senior Product Manager, Intuit Simarpal is the product manager driving product strategy for Feature Management and Machine Learning tools at Intuit. Prior to Intuit, he was at Ayasdi, a machine learning startup, leading product efforts for machine learning solutions in the financial services space. Before that, he worked at Adobe as a product manager for Audience Manager, a data management platform for digital marketing. Korri Jones - Senior Lead Machine Learning Engineer, Chick-fil-A, Inc. Korri Jones is a Sr Lead Machine Learning Engineer and Innovation Coach at Chick-fil-A, Inc. in Atlanta, Georgia where he is focused on MLOps. Prior to his work at Chick-fil-A, he worked as a Business Analyst and product trainer for NavMD, Inc., was an adjunct professor at Roane State Community College, and instructor for the Project GRAD summer program at Pellissippi State Community College and the University of Tennessee, Knoxville. Korri's accolades are just as diverse, and he was in the inaugural 40 under 40 for the University of Tennessee in 2021, Volunteer of the year with the Urban League of Greater Atlanta with over 1000 hours in a single calendar year and has received the “Looking to the Future” award within his department at Chick-fil-A among many others, including best speaker awards in business case competitions.  However, the best award he has received so far is being a loving husband to his wife Lydia. ----------- Connect With Us ✌️-------------    Join our Slack community:  https://go.mlops.community/slack Follow us on Twitter:  @mlopscommunity Sign up for the next meetup:  https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vesi on LinkedIn: https://www.linkedin.com/in/veselina-d-staneva/ Connect with Simar on LinkedIn: https://www.linkedin.com/in/simarpal-khaira-6318959/ Connect with Korri on LinkedIn: https://www.linkedin.com/in/korri-jones-mba-780ba56/

Coffee Sessions #43 with Kyle Gallatin of Etsy, Maturing Machine Learning in Enterprise. //Abstract The definition of Data Science in production has evolved dramatically in recent years. Despite increasing investments in MLOps, many organizations still struggle to deliver ML quickly and effectively. They often fail to recognize an ML project as a massively cross-functional initiative and confuse deployment with production. Kyle will talk about both the functional and non-functional requirements of production ML, and the organizational challenges that can inhibit companies from delivering value with ML. // Bio Kyle Gallatin is currently a Software Engineer for Machine Learning Infrastructure at Etsy. He primarily focuses on operationalizing the training, deployment, and management of machine learning models at scale. Prior to Etsy, Kyle delivered ML microservices and lead the development of MLOps workflows at the pharmaceutical company Pfizer. In his spare time, Kyle mentors data scientists and writes ML blog posts for Towards Data Science. --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Kyle on LinkedIn: https://www.linkedin.com/in/kylegallatin/ // Takeaways Data science is still poorly defined and there is a large variance in organizational maturity   Basically, everything we need for mature ML in modern organizations exists technically except for the strategy, mentality, organization, and governance Organizations who poorly define data science often overburden their data scientists, but there are expectations that data scientists know some engineering Operationalizing data science is not that different from software engineering, and software engineering can be one of the most valuable skill sets for a data scientist. // Q&A with Kyle as a data science mentor:   https://www.youtube.com/watch?v=7byRQGHD39w&t=1s

MLOps community meetup #66! Last Wednesday we talked to Alfredo Deza, Author and Speaker. //Abstract In this episode, the MLOps community talks about the importance of bringing DevOps principles and discipline into Machine Learning. Alfredo explains insights around creating the MLOps role, automation, constant feedback loops, and the number one objective - to ship Machine Learning models into production.    Additionally, we covered some aspects of getting started with Machine Learning that is critical, in particular how democratization ML knowledge is critical to a better environment, from libraries to courses, to production results. Spreading the knowledge is key! //Bio Alfredo Deza is a passionate software engineer, speaker, author, and former Olympic athlete. With almost two decades of DevOps and software engineering experience, he teaches Machine Learning Engineering and gives lectures around the world about software development, personal development, and professional sports.    Alfredo has written several books about DevOps and Python including Python For DevOps and Practical MLOps. He continues to share his knowledge about resilient infrastructure, testing, and robust development practices in courses, books, and presentations.   Alfredo Deza is the author of Python for DevOps and Practical MLOps. ----------- Connect With Us ✌️-------------    Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Chris on LinkedIn: https://www.linkedin.com/in/chrisbergh/ Timestamps: [00:00] Introduction to Alfredo Deza [03:00] Alfredo's background in tech [13:15] Who is this book for? [14:15] "The reason why we need a Machine Learning book is that there's definitely a knowledge gap." [16:05] Hierarchy of MLOps [17:16] "Automation has to be the basis of pretty much, everything." [19:03] Logging - "When in doubt, log it out!" [24:50] Maturity [29:55] "The notion of self-healing is very appealing." [31:20] Learning Test [37:40] "Catch things as early as possible. Anything that comes at the end of the process, the closer you are to the production, the more expensive it could get."   [37:54] "Expensive can be the dollar amount in engineering time, or it can be the dollar amount in services that you're using to produce, and the dollar amount on how long it would take to ship the version that fixes the problem." [39:20] "Why not scan your containers before they hit the production and catch anything that has a critical vulnerability announced?" [40:08] Interrupibility standards and pains [42:34] "It is critical that we make it easier. How about we no longer point fingers and stigmatize people who don't do Machine Learning. The more people doing Machine Learning today, the better we're off." [45:50] Simple and opinionated or flexible and complex   [46:45] "You have to strike a balance but you have to stay true to your principles."   [50:38] Abstraction Layers [56:57] Take a risk or stay safe? [57:20] "I think, you're gonna have risk everywhere you are. You're gonna have risk when you hire a Machine Learning Engineer. You're gonna have a risk with a Data Scientist. You're gonna have a risk with a Software Engineer."