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🌟Speaker Spotlight: Raghav Awasthi 🌟 We're excited to feature Raghav Awasthi on the Technophiles podcast! In this episode titled "Getting Started with MLOps," Raghav takes us on a journey through the essential principles of MLOps, offering valuable insights for anyone looking to integrate machine learning into production environments efficiently. If you're exploring the intersection of machine learning and operations, this episode is for you! 🎤About Raghav: - MLOps Specialist: With extensive experience in deploying machine learning models at scale, Raghav shares best practices for managing ML workflows. - Strategic Innovator: Passionate about simplifying complex processes, Raghav emphasizes operational excellence in Al-driven projects. - Tech Educator: Raghav is committed to empowering the tech community by sharing his in-depth knowledge of MLOps and real-world examples. In this episode, Raghav covers: - Core MLOps Concepts: Understanding the foundations of MLOps and how it bridges the gap between data science and production. - Tools & Frameworks: Key technologies that simplify model deployment, making it easier to scale ML solutions. - Challenges & Solutions: Learn how to overcome common obstacles in MLOps and ensure smooth model integration into production systems. Tune in for this deep dive into the world of MLOps and get ready to streamline your ML projects with expert advice from Raghav! 🎧Listen here: - Spotify: https://lnkd.in/ehQvCtsQ - Podbean: https://lnkd.in/e8shrWNU - YouTube: https://lnkd.in/eG3SrV3f #TechnophilesPodcast #SpeakerSpotlight #RaghavAwasthi #MLOps #MachineLearning #TechLeadership #Allnnovation #ModelDeployment #PodcastGuest #TechCommunity #DataScience #OperationalExcellence #ListenNow #MLFrameworks

How to create great docs with ex Stripe Head of Docs David Nunez 👇 Had a chance to co-host an MLOps Community podcast episode with David Nunez who ran docs at the GOAT of all docs Stripe (thanks a ton for inviting me Demetrios Brinkmann !) 💡 Here are my top takeaways: • 𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗶𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗮 𝘁𝗼𝗻: Show diagrams, show code, and make it inviting. Whitespace signals that the docs will be approachable and easy to scan. • 𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹 𝘂𝘀𝗲𝗿 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 is to get a dev to complete a scenario and ask them to do things as they normally would and “think out loud”. That uncovers so much. • If you cannot just 𝗮𝘀𝗸 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗱𝗲𝘃𝘀 to take a look at that doc and ask them “what are your takeaways”. See if they align with the goal of the doc • 𝗖𝗿𝗲𝗮𝘁𝗲 𝗲𝘀𝗰𝗮𝗽𝗲 𝗵𝗮𝘁𝗰𝗵𝗲𝘀. People will land on the wrong pages. For example, when you google Stripe checkout you can end up on the dev-focused or no-code-focused page. Put a link/button at the top to help people go to the page they actually wanted. • 𝗗𝗼𝗰𝘀 𝗠𝗩𝗣. Don’t think of any other docs before you nail the first quickstart experience where a dev understands what the product is, and accomplishes something in one 15-30min sitting.  • 𝗟𝗮𝘁𝗲𝗿, 𝗮𝗹𝗶𝗴𝗻 𝘆𝗼𝘂𝗿 𝗱𝗼𝗰𝘀 𝘄𝗶𝘁𝗵 𝗱𝗲𝘃 𝘀𝘁𝗮𝗴𝗲𝘀: getting started, design, build, deploy, manage. It will make it easier for devs to achieve more. • 𝗛𝗼𝘄 𝘁𝗼 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗱𝗼𝗰𝘀: customer satisfaction on doc pages 60%+ is good and a north star of time to do X like time to first API call, or time to first dashboard created. • 𝗟𝗼𝗼𝗸 𝗮𝘁 𝘀𝗲𝗮𝗿𝗰𝗵 𝗿𝗲𝘀𝘂𝗹𝘁𝘀. It uncovers the language devs use and the features they want. Update your search internals to that. Also many folks search in google for “X {BRAND}” or “X {BRAND} docs” so you can treat those searches similarly. So much more insights in there so do give it a listen/wathc. Full episode: https://lnkd.in/dKMQwh3N #developermarketing #devrel #developerexperience #saasmarketing #opensource #branding

New Craft of Open Source episode! In this one, Ben Rometsch chatted to ari zilka, who's building mydecisive.ai. It's all about observability. They talked about things like: - Defining observability - Ari's take on OpenTelemetry and its impact on the observability space - Issues with controlling observability data - How to get more control over DevOps data streams - Grassroots open source where the community goes first vs. open source when the big companies move first Check it out on Spotify, Apple Podcasts, etc. Or here: https://lnkd.in/dSA5qmFr #opensource #observability #devops #data #opensourcecommunity #buildinpublic

Learn more about Caliper on the Yet Another Infra Deep Dive podcast. #finops #shiftleft #aws #cloudoptimization #snowflake #cloudcostmanagement

The episode is live now ▶️ If you're new to Microservices, then this podcast will get a lot of insights! Thanks for having me Solomon Eseme

I just had a fun 🎙0800-DEVOPS conversation with Dan Garfield from Codefresh. We touched upon everything from KubeCon, GitOps, DORA regulations, the future of Continuous Delivery, AI, and their bright future joining Octopus Deploy. Stay tuned, as this episode will go live next week! Meanwhile, listen to other amazing conversations in the archive. Simply open your favorite podcast app and search for 🎙0800-DEVOPS. Direct links are in the comments. #devops #engineeringmanagement #leadership

Check out Guy and Simon's new podcast on AI Native Development, reflecting on where AI assisted dev is today and where it may be going in the future as the whole process of development is slowly reoriented around the new potential of what LLMs can do. One big theme is how the act of programming may shift specifying the how to specifying the what - devs write down intent and let LLMs figure out the code. A big question is what, exactly, this looks like. In the world of today, devs write a comment, and the LLM writes a few lines of code. But in the world of the future, the vision is that the LLM is doing even more. How do devs really specify their intent enough to let the LLM know what they're looking for? Answers come from many different perspectives and disciplines. The formal methods community has thought longest about building unambiguous specifications, and has also seen closest just how challenging it is to make a spec unambiguous and then get a developer to write it. Programming by example initially popularized a lot of the ideas of specifying a few examples and then inferring a program, particularly in the domain of things like building scraping scripts with CoScripter. A ton of work in program synthesis, relying on enumerative search with some clever heuristics rather than LLMs for code generation, played a lot with making everything more live and interactive, where a dev can tweak some specs and get a new program. More recently, model based testing is thinking about new representations for specs. I think the biggest takeaway is that specifying intent is harder than it seems and that there may be no solution for all problems and all domains. Going down the formal path to unambiguity is attractive, but can make intent even harder to write than code. Staying informal seems easy, but inevitability means things will go wrong, and requires devs to have a clear way to look at what's getting generated, check it, and iterate fast. And there's a tradeoff between focusing on inferring intent through a few examples and making broader more declarative statements. Beyond all that, there's also a huge challenge with modularity. The biggest challenge with programming by example style systems was that examples could be contradictory (and even when they weren't changing them could have unpredictable impacts). On the one hand, it's great to only specify your intent once, and have it hold across a whole codebase. Wouldn't it be great to write one policy to, say, always make implementation choices to prioritize minimizing time over memory usage, or vice versa, and have an LLM figure out how? But what happens when this conflicts with intent somewhere else, which says this needs to be low enough memory usage to run on a specific device? Even keeping intent much lower level may still raise this issue, as one of the diciest challenges in programming is dealing with hidden and unexpected effects. Look forward to seeing how this all gets resolved.

What would you ask Kelsey Hightower? Kubernetes legend, open source advocate, and friend of Docker is recording an episode of CRAFTED. with Dan Blumberg on Monday. Drop your question in the comments and *maybe* it will be featured in the episode!

🚀 New Podcast Episode Alert! 🎧 https://lnkd.in/dSmiGnzR The JUXT (juxt.pro) team sits down with Niall Murphy, CEO of Stanza and co-author of Site Reliability Engineering: How Google Runs Production Systems 👉 click (… see more) for additional info ⬇️ In this episode, XTDB Head of Product Jeremy Taylor delves into Niall's unique insights from his time at Google and they discuss: - How Google's infrastructure was transformed in the late 2000s and its impact on the SRE landscape - The persistent gap between discovering 'signals' and taking 'actions' in distributed systems - Reflections on the current challenges in SRE tooling and reliability practices 🎧 https://lnkd.in/dSmiGnzR We hope you enjoy this insightful episode! Share your thoughts or any points that resonated with you. Happy listening! #SRE #Leadership #Infrastructure #TechInnovation #Podcast #SiteReliabilityEngineering #Google #DistributedSystems

🚨 Calling all the product managers and engineering enthusiasts! 🔊McKinsey Product Academy is launching a new podcast series - McKinsey on Building Products. We host conversations with distinguished product and engineering leaders covering topics ranging from product-led growth to generative AI. Our first episode features Gokul Rajaram, board member of Coinbase and Pinterest, discussing product thinking and the future of innovation💡 Tune in to Apple (https://lnkd.in/gpA4F8_y) or Spotify (https://lnkd.in/gT5F529X) and keep an eye on our upcoming episodes very soon! #ProductThinking #Innovation #ProductManagement #ProductDevelopment