The promise of AI in enterprise settings is enormous, but so are the privacy and security challenges. How do you harness AI's capabilities while keeping sensitive data protected within your organization's boundaries? Private AI—using your own models, data, and infrastructure—offers a solution, but implementation isn't straightforward. What governance frameworks need to be in place? How do you evaluate non-deterministic AI systems? When should you build in-house versus leveraging cloud services? As data and software teams evolve in this new landscape, understanding the technical requirements and workflow changes is essential for organizations looking to maintain control over their AI destiny. Manasi Vartak is Chief AI Architect and VP of Product Management (AI Platform) at Cloudera. She is a product and AI leader with more than a decade of experience at the intersection of AI infrastructure, enterprise software, and go-to-market strategy. At Cloudera, she leads product and engineering teams building low-code and high-code generative AI platforms, driving the company’s enterprise AI strategy and enabling trusted AI adoption across global organizations. Before joining Cloudera through its acquisition of Verta, Manasi was the founder and CEO of Verta, where she transformed her MIT research into enterprise-ready ML infrastructure. She scaled the company to multi-million ARR, serving Fortune 500 clients in finance, insurance, and capital markets, and led the launch of enterprise MLOps and GenAI products used in mission-critical workloads. Manasi earned her PhD in Computer Science from MIT, where she pioneered model management systems such as ModelDB — foundational work that influenced the development of tools like MLflow. Earlier in her career, she held research and engineering roles at Twitter, Facebook, Google, and Microsoft. In the episode, Richie and Manasi explore AI's role in financial services, the challenges of AI adoption in enterprises, the importance of data governance, the evolving skills needed for AI development, the future of AI agents, and much more. Links Mentioned in the Show: ClouderaCloudera Evolve ConferenceCloudera Agent StudioConnect with ManasiCourse: Introduction to AI AgentsRelated Episode: RAG 2.0 and The New Era of RAG Agents with Douwe Kiela, CEO at Contextual AI & Adjunct Professor at Stanford UniversityRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

At PyData Berlin, community members and industry voices highlighted how AI and data tooling are evolving across knowledge graphs, MLOps, small-model fine-tuning, explainability, and developer advocacy.

• Igor Kvachenok (Leuphana University / ProKube) combined knowledge graphs with LLMs for structured data extraction in the polymer industry, and noted how MLOps is shifting toward LLM-focused workflows.
• Selim Nowicki (Distill Labs) introduced a platform that uses knowledge distillation to fine-tune smaller models efficiently, making model specialization faster and more accessible.
• Gülsah Durmaz (Architect & Developer) shared her transition from architecture to coding, creating Python tools for design automation and volunteering with PyData through PyLadies.
• Yashasvi Misra (Pure Storage) spoke on explainable AI, stressing accountability and compliance, and shared her perspective as both a data engineer and active Python community organizer.
• Mehdi Ouazza (MotherDuck) reflected on developer advocacy through video, workshops, and branding, showing how creative communication boosts adoption of open-source tools like DuckDB.

Igor Kvachenok Master’s student in Data Science at Leuphana University of Lüneburg, writing a thesis on LLM-enhanced data extraction for the polymer industry. Builds RDF knowledge graphs from semi-structured documents and works at ProKube on MLOps platforms powered by Kubeflow and Kubernetes.

Connect: https://www.linkedin.com/in/igor-kvachenok/

Selim Nowicki Founder of Distill Labs, a startup making small-model fine-tuning simple and fast with knowledge distillation. Previously led data teams at Berlin startups like Delivery Hero, Trade Republic, and Tier Mobility. Sees parallels between today’s ML tooling and dbt’s impact on analytics.

Connect: https://www.linkedin.com/in/selim-nowicki/

Gülsah Durmaz Architect turned developer, creating Python-based tools for architectural design automation with Rhino and Grasshopper. Active in PyLadies and a volunteer at PyData Berlin, she values the community for networking and learning, and aims to bring ML into architecture workflows.

Connect: https://www.linkedin.com/in/gulsah-durmaz/

Yashasvi (Yashi) Misra Data Engineer at Pure Storage, community organizer with PyLadies India, PyCon India, and Women Techmakers. Advocates for inclusive spaces in tech and speaks on explainable AI, bridging her day-to-day in data engineering with her passion for ethical ML.

Connect: https://www.linkedin.com/in/misrayashasvi/

Mehdi Ouazza Developer Advocate at MotherDuck, formerly a data engineer, now focused on building community and education around DuckDB. Runs popular YouTube channels ("mehdio DataTV" and "MotherDuck") and delivered a hands-on workshop at PyData Berlin. Blends technical clarity with creative storytelling.

Connect: https://www.linkedin.com/in/mehd-io/

Send us a text In this episode, we're joined by Sam Debruyn and Dorian Van den Heede who reflect on their talks at SQL Bits 2025 and dive into the technical content they presented. Sam walks through how dbt integrates with Microsoft Fabric, explaining how it improves lakehouse and warehouse workflows by adding modularity, testing, and documentation to SQL development. He also touches on Fusion’s SQL optimization features and how it compares to tools like SQLMesh. Dorian shares his MLOps demo, which simulates beating football bookmakers using historical data,nshowing how to build a full pipeline with Azure ML, from feature engineering to model deployment. They discuss the role of Python modeling in dbt, orchestration with Azure ML, and the practical challenges of implementing MLOps in real-world scenarios. Toward the end, they explore how AI tools like Copilot are changing the way engineers learn and debug code, raising questions about explainability, skill development, and the future of junior roles in tech. It’s rich conversation covering dbt, MLOps, Python, Azure ML, and the evolving role of AI in engineering.

What does AI transformation really look like inside a 180-year-old company? In this episode of Data Unchained, we are joined by Younes Hairej, founder and CEO of Aokumo Inc, a trailblazing company helping enterprises in Japan and beyond bridge the gap between business intent and AI execution. From deploying autonomous AI agents that eliminate the need for dashboards and YAML, to revitalizing siloed, analog systems in manufacturing, Younes shares what it takes to modernize legacy infrastructure without starting over. Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US

ArtificialIntelligence #EnterpriseAI #AITransformation #Kubernetes #DevOps #GenAI #DigitalTransformation #OpenSourceAI #DataInfrastructure #BusinessInnovation #AIInJapan #LegacyModernization #MetadataStrategy #AIOrchestration #CloudNative #AIAutomation #DataGovernance #MLOps #IntelligentAgents #TechLeadership

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Send us a text Welcome to the cozy corner of the tech world! Datatopics is your go-to spot for relaxed discussions around tech, news, data, and society. In this episode of Data Topics, we sit down with Nick Schouten — data engineer at dataroots — for a full recap of KubeCon Europe 2025 and a deep dive into the current and future state of Kubernetes. We talk through what’s actually happening in the Kubernetes ecosystem — from platform engineering trends to AI infra challenges — and why some teams are doubling down while others are stepping away. Here’s what we cover: What Kubernetes actually is, and how to explain it beyond the buzzwordWhen Kubernetes is the right choice (e.g., hybrid environments, GPU-heavy workloads) — and when it’s overkillHow teams are trying to host LLMs and AI models on Kubernetes, and the blockers they’re hitting (GPUs, complexity, cost)GitOps innovations spotted at KubeCon — like tools that convert UI clicks into Git commits for infrastructure-as-codeWhy observability is still one of Kubernetes’ biggest weaknesses, and how a wave of new startups are trying to solve itThe push to improve developer experience for ML and data teams (no more YAML overload)The debate around abstraction vs control — and how some teams are turning away from Kubernetes entirely in favor of simpler toolsWhat “vibe coding” means in an LLM-driven world, and how voice-to-code workflows are changing how we write infrastructureWhether the future of Kubernetes is more “visible and accessible,” or further under the hoodIf you're a data engineer, MLOps practitioner, platform lead, or simply trying to stay ahead of the curve in infrastructure and AI — this episode is packed with relevant insights from someone who's hands-on with both the tools and the teaching.

Send us a text Welcome to the cozy corner of the tech world! Datatopics is your go-to spot for relaxed discussions around tech, news, data, and society. This week, co-host Ben is joined by Jackie Janssen, former Chief Data Officer at CM, author of AI: De Hype Voorbij, and an evangelist for pragmatic, human-centered AI. Together, they trace the winding path from early tech roles to enterprise transformation, exploring how AI can actually serve humans (and not just the hype machine). In this episode: Leadership in AI transformation: From KBC to CM, lessons on creating cultural buy-in.Building effective data teams: Why the first hire isn’t always a data engineer.AI governance: What makes a strong AI Council and why CEOs should care.Product and process thinking: How MLOps, data factories, and product mindsets intersect.Agents and autonomy: The future of work with AI teammates, not just tools.The human edge in a machine world: A preview of Jackie’s next book on rediscovering humanity in the age of AI.Curious about Jackie’s take on AI agents, cultural inertia, or what really makes a great data strategy tick? Tune in, you might just find a new way to think about your tech stack and your team.

In this podcast episode, we talked with Bartosz Mikulski about Data Intensive AI.

About the Speaker: Bartosz is an AI and data engineer. He specializes in moving AI projects from the good-enough-for-a-demo phase to production by building a testing infrastructure and fixing the issues detected by tests. On top of that, he teaches programmers and non-programmers how to use AI. He contributed one chapter to the book 97 Things Every Data Engineer Should Know, and he was a speaker at several conferences, including Data Natives, Berlin Buzzwords, and Global AI Developer Days. 

In this episode, we discuss Bartosz’s career journey, the importance of testing in data pipelines, and how AI tools like ChatGPT and Cursor are transforming development workflows. From prompt engineering to building Chrome extensions with AI, we dive into practical use cases, tools, and insights for anyone working in data-intensive AI projects. Whether you’re a data engineer, AI enthusiast, or just curious about the future of AI in tech, this episode offers valuable takeaways and real-world experiences.

0:00 Introduction to Bartosz and his background 4:00 Bartosz’s career journey from Java development to AI engineering 9:05 The importance of testing in data engineering 11:19 How to create tests for data pipelines 13:14 Tools and approaches for testing data pipelines 17:10 Choosing Spark for data engineering projects 19:05 The connection between data engineering and AI tools 21:39 Use cases of AI in data engineering and MLOps 25:13 Prompt engineering techniques and best practices 31:45 Prompt compression and caching in AI models 33:35 Thoughts on DeepSeek and open-source AI models 35:54 Using AI for lead classification and LinkedIn automation 41:04 Building Chrome extensions with AI integration 43:51 Comparing Cursor and GitHub Copilot for coding 47:11 Using ChatGPT and Perplexity for AI-assisted tasks 52:09 Hosting static websites and using AI for development 54:27 How blogging helps attract clients and share knowledge 58:15 Using AI to assist with writing and content creation

🔗 CONNECT WITH Bartosz LinkedIn: https://www.linkedin.com/in/mikulskibartosz/ Github: https://github.com/mikulskibartosz Website: https://mikulskibartosz.name/blog/

🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ Check other upcoming events - https://lu.ma/dtc-events LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/

In this podcast episode, we talked with Nemanja Radojkovic about MLOps in Corporations and Startups.

About the Speaker: Nemanja Radojkovic is Senior Machine Learning Engineer at Euroclear.

In this event,we’re diving into the world of MLOps, comparing life in startups versus big corporations. Joining us again is Nemanja, a seasoned machine learning engineer with experience spanning Fortune 500 companies and agile startups. We explore the challenges of scaling MLOps on a shoestring budget, the trade-offs between corporate stability and startup agility, and practical advice for engineers deciding between these two career paths. Whether you’re navigating legacy frameworks or experimenting with cutting-edge tools.

1:00 MLOps in corporations versus startups 6:03 The agility and pace of startups 7:54 MLOps on a shoestring budget 12:54 Cloud solutions for startups 15:06 Challenges of cloud complexity versus on-premise 19:19 Selecting tools and avoiding vendor lock-in 22:22 Choosing between a startup and a corporation 27:30 Flexibility and risks in startups 29:37 Bureaucracy and processes in corporations 33:17 The role of frameworks in corporations 34:32 Advantages of large teams in corporations 40:01 Challenges of technical debt in startups 43:12 Career advice for junior data scientists 44:10 Tools and frameworks for MLOps projects 49:00 Balancing new and old technologies in skill development 55:43 Data engineering challenges and reliability in LLMs 57:09 On-premise vs. cloud solutions in data-sensitive industries 59:29 Alternatives like Dask for distributed systems

🔗 CONNECT WITH NEMANJA LinkedIn -   / radojkovic   Github - https://github.com/baskervilski

🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/... Check other upcoming events - https://lu.ma/dtc-events  LinkedIn -   / datatalks-club    Twitter -   / datatalksclub    Website - https://datatalks.club/ 

Summary In this episode of the Data Engineering Podcast Bartosz Mikulski talks about preparing data for AI applications. Bartosz shares his journey from data engineering to MLOps and emphasizes the importance of data testing over software development in AI contexts. He discusses the types of data assets required for AI applications, including extensive test datasets, especially in generative AI, and explains the differences in data requirements for various AI application styles. The conversation also explores the skills data engineers need to transition into AI, such as familiarity with vector databases and new data modeling strategies, and highlights the challenges of evolving AI applications, including frequent reprocessing of data when changing chunking strategies or embedding models.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Bartosz Mikulski about how to prepare data for use in AI applicationsInterview IntroductionHow did you get involved in the area of data management?Can you start by outlining some of the main categories of data assets that are needed for AI applications?How does the nature of the application change those requirements? (e.g. RAG app vs. agent, etc.)How do the different assets map to the stages of the application lifecycle?What are some of the common roles and divisions of responsibility that you see in the construction and operation of a "typical" AI application?For data engineers who are used to data warehousing/BI, what are the skills that map to AI apps?What are some of the data modeling patterns that are needed to support AI apps?chunking strategies metadata managementWhat are the new categories of data that data engineers need to manage in the context of AI applications?agent memory generation/evolution conversation history managementdata collection for fine tuningWhat are some of the notable evolutions in the space of AI applications and their patterns that have happened in the past ~1-2 years that relate to the responsibilities of data engineers?What are some of the skills gaps that teams should be aware of and identify training opportunities for?What are the most interesting, innovative, or unexpected ways that you have seen data teams address the needs of AI applications?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI applications and their reliance on data?What are some of the emerging trends that you are paying particular attention to?Contact Info WebsiteLinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links SparkRayChunking StrategiesHypothetical document embeddingsModel Fine TuningPrompt CompressionThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

In this podcast episode, we talked with Alexander Guschin about launching a career off Kaggle.

About the Speaker: Alexander Guschin is a Machine Learning Engineer with 10+ years of experience, a Kaggle Grandmaster ranked 5th globally, and a teacher to 100K+ students. He leads DS and SE teams and contributes to open-source ML tools. 0:00 Starting with Machine Learning: Challenges and Early Steps 13:05 Community and Learning Through Kaggle Sessions 17:10 Broadening Skills Through Kaggle Participation 18:54 Early Competitions and Lessons Learned 21:10 Transitioning to Simpler Solutions Over Time
23:51 Benefits of Kaggle for Starting a Career in Machine Learning
29:08 Teamwork vs. Solo Participation in Competitions
31:14 Schoolchildren in AI Competitions 42:33 Transition to Industry and MLOps 50:13 Encouraging teamwork in student projects 50:48 Designing competitive machine learning tasks 52:22 Leaderboard types for tracking performance 53:44 Managing small-scale university classes 54:17 Experience with Coursera and online teaching 59:40 Convincing managers about Kaggle's value 61:38 Secrets of Kaggle competition success 63:11 Generative AI's impact on competitive ML 65:13 Evolution of automated ML solutions 66:22 Reflecting on competitive data science experience

🔗 CONNECT WITH ALEXANDER GUSCHINLinkedin - https://www.linkedin.com/in/1aguschin/Website - https://www.aguschin.com/

🔗 CONNECT WITH DataTalksClub Join DataTalks.Club:⁠⁠⁠⁠https://datatalks.club/slack.html⁠⁠⁠⁠ Our events:⁠⁠⁠⁠https://datatalks.club/events.html⁠⁠⁠⁠ Datalike Substack -⁠⁠⁠⁠https://datalike.substack.com/⁠⁠⁠⁠ LinkedIn:⁠⁠⁠⁠  / datatalks-club  ⁠

We talked about:

00:00 DataTalks.Club intro

02:34 Career journey and transition into MLOps

08:41 Dutch agriculture and its challenges

10:36 The concept of "technical debt" in MLOps

13:37 Trade-offs in MLOps: moving fast vs. doing things right

14:05 Building teams and the role of coordination in MLOps

16:58 Key roles in an MLOps team: evangelists and tech translators

23:01 Role of the MLOps team in an organization

25:19 How MLOps teams assist product teams

27 :56 Standardizing practices in MLOps

32:46 Getting feedback and creating buy-in from data scientists

36:55 The importance of addressing pain points in MLOps

39:06 Best practices and tools for standardizing MLOps processes

42:31 Value of data versioning and reproducibility

44:22 When to start thinking about data versioning

45:10 Importance of data science experience for MLOps

46:06 Skill mix needed in MLOps teams

47:33 Building a diverse MLOps team

48:18 Best practices for implementing MLOps in new teams

49:52 Starting with CI/CD in MLOps

51:21 Key components for a complete MLOps setup

53:08 Role of package registries in MLOps

54:12 Using Docker vs. packages in MLOps

57:56 Examples of MLOps success and failure stories

1:00:54 What MLOps is in simple terms

1:01:58 The complexity of achieving easy deployment, monitoring, and maintenance

Join our Slack: https://datatalks .club/slack.html

Demetrios Brinkmann is the co-founder of the massively global MLOps Community. We chat about AI hype vs reality, building a global tech community, and ROI of AI projects, and much more.

LinkedIn: https://www.linkedin.com/in/dpbrinkm/

MLOps Community: https://mlops.community/

Prepare-se para uma experiência inovadora e disruptiva ! No primeiro episódio do Podcast Data Hackers dublado por AI, vamos explorar os debates mais atuais e relevantes entre MLOps vs LLMOps. Este episódio marca uma nova era no nosso podcast, trazendo conteúdos em português com a precisão e naturalidade das vozes geradas por inteligência artificial.

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conheçam Demetrios Brinkmann, Co-FounderCo-Founder MLOps Community , vai compartilhar suas experiências e insights sobre como o dia a dia nesse campo evoluiu ao longo do tempo. Discutimos as principais diferenças técnicas entre MLOps e LLMOps, e o que isso significa para os profissionais que atuam ou desejam atuar nessa área.

Se você é apaixonado pela área de dados, quer entender as últimas tendências e se atualizar sobre o futuro do MLOps, não pode perder este episódio!

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Caso queira, você também pode ouvir o episódio aqui no post mesmo!

Nossa Bancada Data Hackers:

Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart. Monique Femme — Head of Community Management na Data Hackers Gabriel Lages — Co-founder da Data Hackers e Data & Analytics Sr. Director na Hotmart.

Referências no Medium.

In this podcast episode, we talked with Guillaume Lemaître about navigating scikit-learn and imbalanced-learn.

🔗 CONNECT WITH Guillaume Lemaître LinkedIn - https://www.linkedin.com/in/guillaume-lemaitre-b9404939/ Twitter - https://x.com/glemaitre58 Github - https://github.com/glemaitre Website - https://glemaitre.github.io/

🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks-club.slack.com/join/shared_invite/zt-2hu0sjeic-ESN7uHt~aVWc8tD3PefSlA#/shared-invite/email Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/u/0/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ Check other upcoming events - https://lu.ma/dtc-events LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/

🔗 CONNECT WITH ALEXEY Twitter - https://twitter.com/Al_Grigor Linkedin - https://www.linkedin.com/in/agrigorev/

🎙 ABOUT THE PODCAST At DataTalksClub, we organize live podcasts that feature a diverse range of guests from the data field. Each podcast is a free-form conversation guided by a prepared set of questions, designed to learn about the guests’ career trajectories, life experiences, and practical advice. These insightful discussions draw on the expertise of data practitioners from various backgrounds.

We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links.

You can access all the podcast episodes here - https://datatalks.club/podcast.html

📚Check our free online courses ML Engineering course - http://mlzoomcamp.com Data Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp LLM course - https://github.com/DataTalksClub/llm-zoomcamp Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html

👋🏼 GET IN TOUCH If you want to support our community, use this link - https://github.com/sponsors/alexeygrigorev

If you're a company and want to support us, contact at [email protected]

The role of the data scientist is changing. Some organizations are splitting the role into more narrowly focused jobs, while others are broadening it. The latter approach, known as the Full Stack Data Scientist, is derived from the concept of a full stack software engineer, with this role often including software engineering tasks. In particular, one of the key functions of a full stack data scientist is to take machine learning models and get them into production inside software. So, what separates projects from production? Savin Goyal is the Co-Founder & CTO at Outerbounds. In addition to his work at Outerbounds, Savin is the creator of the open source machine learning management platform Metaflow. Previously Savin has worked as a Software Engineer at Netflix and LinkedIn. In the episode, Richie and Savin explore the definition of production in data science, steps to move from internal projects to production, the lifecycle of a machine learning project, success stories in data science, challenges in quality control, Metaflow, scalability and robustness in production, AI and MLOps, advice for organizations and much more.  Links Mentioned in the Show: OuterboundsMetaflowConnect with Savin on Linkedin[Course] Developing Machine Learning Models for ProductionRelated Episode: Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling AuthorRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links.

You can access all the podcast episodes here - https://datatalks.club/podcast.html

📚Check our free online courses ML Engineering course - http://mlzoomcamp.com Data Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp LLM course - https://github.com/DataTalksClub/llm-zoomcamp Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html

👋🏼 GET IN TOUCH If you want to support our community, use this link - https://github.com/sponsors/alexeygrigorev

If you’re a company, support us at [email protected]

In today's fast-paced digital world, managing IT operations is more complex than ever. With the rise of cloud services, microservices, and constant software deployments, the pressure on IT teams to keep everything running smoothly is immense. But how do you keep up with the ever-growing flood of data and ensure your systems are always available? AIOps is the use of artificial intelligence to automate and scale IT operations. But what exactly is AIOps, and how can it transform your IT operations? Assaf Resnick is the CEO and Co-Founder of BigPanda. Before founding BigPanda, Assaf was an investor at Sequoia Capital, where he focused on early and growth-stage investing in software, internet, and mobile sectors. Assaf’s time at Sequoia gave him a front-row seat to the challenges of IT scale, complexity, and velocity faced by Operations teams in rapidly scaling and accelerating organizations. This is the problem that Assaf founded BigPanda to solve. In the episode, Richie and Assaf explore AIOps, how AIOps helps manage increasingly complex IT operations, how AIOps differs from DevOps and MLOps, examples of AIOps projects, a real world application of AIOps, the key benefits of AIOps, how to implement AIOps, excitement in the space, how GenAI is improving AIOps and much more.  Links Mentioned in the Show: BigPandaGartner: Market Guide for AIOps Platforms[Course] Implementing AI Solutions in BusinessRelated Episode: Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at SnowflakeSign up to RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society.

Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style!

In this episode, we're joined by special guest Vitale Sparacello, an MLflow Ambassador, to delve into a myriad of topics shaping the current and future landscape of AI and software development: MLflow Deep Dive: Exploring the MLflow Ambassador program, MLflow's role in promoting MLOps practices, and its expansion to support generative AI technologies.Introducing Llama 3: Discussing Meta's newest language model, Llama 3, its capabilities, and the nuanced policy of its distribution, featured on platforms like Groq. Read more here.Emerging AI Tools: Evaluating Open-Parse for advanced text parsing and debating the longevity of PDF documents in the age of advanced AI technologies.OpenTofu vs. Terraform Drama: Analyzing the ongoing legal dispute between Terraform and OpenTofu, with discussions around code ethics and links to OpenTofu's LinkedIn, and their official blog response.The Future of AI Devices: Are smartphones the endgame, or is there a future for specialized AI wearables? Speculating on the evolution of AI devices, focusing on the Human AI Pin review, Rabbit R1 and Dot Computer Go check out the Youtube video after so you don't miss out Murilo in that suit he promised (with a duck tie of course).

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's dive into episode #36 titled "Altman's Chips, Mojo Gets Fast and TUIs", featuring Nemanja Radojkovic, an MLOps Lead and Educator, as our special guest. In this episode, we explore a variety of cutting-edge topics: Text-based User Interfaces (TUIs) Rediscovered: Delving into the resurgence of TUIs with Ratatui and gping. Are we witnessing a TUI renaissance? Ratatui | gpingThe Surprising Length of ChatGPT's System Prompt: Unpacking the implications of a 1700 token system prompt. Is there more than meets the eye? Reddit discussionMojo Outpaces Rust in DNA Sequence Parsing: A closer look at how Mojo outperforms Rust by 50% in benchmarks. Is Mojo the new king? Modular's blog postSam Altman's Vision for the Future of Chips and AI: Examining Altman's ambitious plan to reshape the business of chips and AI with trillions of dollars. WSJ article | Reuters on Microsoft's AI chipsThe Real Challenge in Generating Code: Discussing the misconception that generating code is the hard part, with insights into the complexities of software engineering beyond code generation. Nick Scialli's blog Follow Nemanja on LinkedIn and check out his courses.  Intro music courtesy of fesliyanstudios.com

We are in a Generative AI hype cycle. Every executive looking at the potential generative AI today is probably thinking about how they can allocate their department's budget to building some AI use cases. However, many of these use cases won't make it into production. In a similar vein, the hype around machine learning in the early 2010s led to lots of hype around the technology, but a lot of the value did not pan out. Four years ago, VentureBeat showed that 87% of data science projects did not make it into production. And in a lot of ways, things haven’t gotten much better. And if we don't learn why that is the case, generative AI could be destined to a similar fate.  Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice. In the episode, Adel and Eric explore the reasons why machine learning projects don't make it into production, the BizML Framework or how to bring business stakeholders into the room when building machine learning use cases, the skill gap between business stakeholders and data practitioners, use cases of organizations have leveraged machine learning for operational improvements, what the previous machine learning hype cycle can teach us about generative AI and a lot more.  Links Mentioned in the Show: The AI Playbook: Mastering the Rare Art of Machine Learning Deployment by Eric SiegelGenerating ROI with AIBizML Cheat SheetGooderSurvey: Machine Learning Projects Still Routinely Fail to Deploy[Skill Track] MLOps Fundamentals