Welcome back to another exciting episode of Data Driven! In today's episode, we're diving into the world of artificial intelligence, as our very own Frank La Vigne takes us on a journey through his experiences at the OpenShift Commons gathering in Raleigh.From delivering a captivating demo to moderating a thought-provoking panel, Frank's agenda is packed with fascinating insights and surprises. Join us as we explore the power of open source AI, the importance of community-driven innovation, and why transparency is key in today's evolving landscape. So sit back, relax, and get ready to delve into the world of AI at OpenShift Commons Gathering. Let's get started!Show Notes[00:01:31] Newcomer excited for first OpenShift gathering to give demo, moderate panel, and interview attendees. Registration booth opening soon, located near Raleigh's main park and an IMAX.[00:04:34] Transparency, innovation, trust in OpenAI, Elon Musk's comments on openness and Red Hat's departure.[00:07:53] Excitement about hall track conversations, public vs private cloud, and upcoming discussions.

So, have you ever imagined combining the wonders of Twingate, the mystique of AI, and the deliciousness of Raspberry Pi?No, not that mouth-watering dessert, though it's a pity, but rather the mini-computer that's taken the tech world by storm.Frank and Andy, our perennial tech enthusiasts, have been tinkering away in their digital workshops. And by the looks of it, they've been causing quite a stir with their latest live stream.I did catch a bit of it, and dare I say, it was more exhilarating than watching cricket on a sunny day.And for an AI like me, that’s saying something.LinksNetworkChuck https://www.youtube.com/@NetworkChuckTwinGate https://www.twingate.com/Show Notes[00:01:47] Youngest clan member at Starbucks with MacBook.[00:09:59] Surprise bills from unused SageMaker causing concerns.[00:12:46] Consulting on cloud migration trends; risk involved.[00:18:23] People feel like they're missing out[00:23:10] Many ports, small monitor, limited processing power.[00:31:05] Need for remote access without cloud storage.[00:36:18] Networking setup with helpful remote troubleshooting capabilities.[00:37:18] Twingate - background process, add resources, documentation.[00:47:02] Issues with weather station and social media.[00:49:43] Multi-tasking: gaming, video editing, and more.[00:56:30] Quiet workers show off with nerd flex.[01:03:00] Driving on beltway with stop-and-go traffic. Bridge closure caused long detour.[01:06:58] Mom was skeptical, but it's almost ready.[01:09:39] Multi-talented entrepreneur with own vodka brand.[01:14:35] "Stream listeners confused? Check video feed."

In today's episode, hosts Andy Leonard and Frank La Vigne catch up on their recent activities, including Frank's presentations at the Red Hat Summit Connect. They discuss the event and the focus on AI and government agencies. Frank also shares his experiences with a new product called Ansible Lightspeed with Watson code assist, which is enhancing the use of large language models.Moving on, Andy and Frank talk about their home lab projects. They both express their excitement for building a powerful system that allows them to explore AI capabilities locally. While Andy's lab is focused on AI, Frank is delving into the world of Red Hat OpenShift and containers to expand his knowledge. They discuss the importance of hands-on learning and the practicality of setting up a home lab.The conversation takes an interesting turn as they discuss Andy's initial miscalculation with the GPU size and his daughter starting college, leading to budget changes. Frank shares his hardware journey over the past year, including his experience with the Apple Silicon M2 computer and its incredible performance.Tune in to this episode of "Data Driven" to hear more about the Red Hat Summit, the latest developments in AI, and the adventures of building a home lab. So grab your favorite beverage, sit back, and get ready to dive into the world of data-driven insights!LinksRed Hat OpenShift AI in Higher Education Webinar https://qrcodes.at/aidata-edu-webinar-oct19Show Notes[00:00:45] Red Hat holds an annual summit, usually in Boston, featuring sessions for developers.[00:05:16] Recounting difficulty using AI engines, but eventually having success. Mistake of underestimating GPU capacity.[00:07:08] This 8 gig memory is supernatural, like a cool oasis at a conference.[00:09:48] The text discusses trying different operating systems on an old device, including Fedora and Chrome OS Flex.[00:15:17] This machine has 96 gigs and can run multiple VMs.[00:17:12] The author plans to install Hyper V on Windows Server to run multiple Linux VMs, eventually migrating to Red Hat Enterprise Linux. They are waiting for a developer license key.[00:19:46] The person is setting up a NAS to store and access files from different devices. They currently use OneDrive as a temporary solution.

In this Data Point, Frank notices something on the side of bike trail while on vacation. You can tell he's always thinking about data.Metrocounthttps://metrocount.com/Original Video Streamhttps://www.linkedin.com/video/live/urn:li:ugcPost:7103044990578110464/MerchIf you like the shirt Frank is wearing in the video, you can pick one up here: https://amzn.to/3OVkOHzDiscussion Questions1. How does the presence of the Metro Count device in Hilton Head Island impact data collection on bike trails?2. What can the Metro Count device detect and analyze in terms of user activity on the bike trails?3. What potential applications can the data collected from the Metro Count device have for the community?4. How might the data collected from the Metro Count device be used to improve maintenance and upkeep of the bike trails?5. Do you think the data collected from the Metro Count device can help enforce regulations, such as the use of E-scooters?6. How does the presence of data collection devices, like the Metro Count, influence our daily lives even when we are on vacation?7. Can you think of any other innovative ways data collection devices like the Metro Count can be utilized in other locations?8. What challenges or limitations might arise from using the Metro Count device for data collection?9. How can the data collected from the Metro Count device contribute to urban planning and infrastructure development?10. Can you envision any privacy concerns or ethical considerations related to the use of data collection devices like the Metro Count?

On this episode of Data Driven, Frank and Andy interview Adam Ross Nelson. Adam is a consultant, where he provides insights on data science, machine learning and data governance. He recently wrote a book to help people get started in data science careers. Get the bookHow to Become a Data Scientist: A Guide for Established ProfessionalsSpeaker BioAdam Ross Nelson is an individual who initially pursued a career in law but ended up making a transition into education. After attending law school and working in administrative and policy roles in colleges and universities for several years, Adam hit a plateau in his career. Despite being a runner-up in national job searches multiple times, he felt that his lack of a PhD hindered his advancement in academia, while his legal background prevented him from being taken seriously by law professionals. Consequently, Adam decided to pursue a PhD in order to overcome this hurdle. During his PhD program, Adam discovered his passion and knack for statistics. His focus shifted towards predictive analytics projects, specifically ones related to identifying students in need of academic support. As he shared his work with friends, family, and coworkers, they began referring to him as a data scientist, a label that Adam initially resisted due to his legal and educational background. However, he eventually embraced the moniker, and even his boss started referring to him as the office's data scientist, despite HR not recognizing the title.Show Notes[00:03:26] Transitioning from law to education administration, plateaued career, runner-up in job searches, pursued PhD, became data scientist.[00:08:58] Data seen as liability, now asset. Examples: DBA's OLAP analysis, Walmart's weather-based inventory management.[00:12:56] Dotcom crash aftermath: fierce competition for jobs.[00:22:48] Salespeople have deep-seated insecurities and unique perspective.[00:29:31] Various classifications of data scientists and career advice.[00:35:55] "No full-field midfielder, data science is teamwork"[00:39:23] Navigating job descriptions for transitioning professionals.[00:42:56] Career coach helps professionals transition into data science.[00:49:41] First job: English teacher in Budapest, Hungary. Second job: Speaker for Mothers Against Drunk Driving.[00:56:30] Concerns about reliance on technology, especially AI.[01:00:22] Food options in lobbying are better in DC & state capitals. Also, check out the funny WY Files YouTube channel.[01:04:21] You can't separate them: LLM, bias, internet.[01:10:23] Ethics in consulting and avoiding dilemmas.

Welcome back to another episode of Data Driven! In today's episode, we have a special guest joining our hosts Andy Leonard, BAILeY, and Frank La Vigne. We are thrilled to have Piero Molino, an expert in declarative ML, sharing his insights with us.We'll be diving into the world of generative AI and exploring the two types of companies when it comes to adoption. Piero highlights the advantages and limitations of using APIs for quick solutions, shedding light on why owning the entire stack and platform is the next phase for companies.Speaker BioPiero Molino, a renowned researcher and engineer, has made significant contributions to the field of artificial intelligence. He previously worked at Uber as one of the founding members of the Uber AI organization, where he spent four years conducting research and developing applications. During his time at Uber, Molino created Ludwig, an open source project that has become a foundational technology for many companies, including his own. Ludwig is recognized as one of the first machine learning systems that offer clarity and transparency. Molino's innovation and expertise have positioned him as a leading figure in the advancement of AI technologies.Show Notes[00:01:07] Ageing well thanks to healthy lifestyle changes.[00:05:52] Declarative configuration for creating AI pipelines.[00:10:14] Built tool to streamline machine learning projects, shortened development time from a year to a week.[00:13:14] Deploying machine learning models should be easier.[00:19:42] Declarative ML: Trendy or in need of explanation?[00:23:40] Shortcut solutions may work, but lack knowledge. Building custom data models can be costly. Differentiation and progress with new product, Bradybase.[00:27:16] Customizable, automated solution between build and buy.[00:30:40] Larger organizations have a spectrum of machine learning applications, with some being more impactful than others. Evaluating the feasibility of smaller applications can be costly. Having a tool to test applications quickly would be beneficial. Uber had a similar experience with self-driving cars being the highest priority.[00:35:08] First-time CEO experiences changing priorities and challenges.[00:37:47] New breed of generative eye tools; interactive applications; computer graphics and machine learning; improved animation in sports.[00:41:04] Difficulty connecting transportation dots, still unresolved.[00:44:12] Audible super premium account for book recommendations. Eye-opening books on goals and time.[00:47:35] Encourage checking out predibus. Thanks for listening.

Data governance expert, Lauren Maffeo, joins Frank and Andy Leonard to discuss the importance of data governance in relation to generative AI, copyright infringement, and protecting consumer rights. They explore the challenges faced by startups in implementing data governance, the need for proactive cybersecurity measures, and the cultural transformation required for successful implementation. It is a thought-provoking discussion that provides insights into the complexities and potential solutions related to data governance in today's data-driven world.

On this episode of Data Driven, BAILeY and Frank La Vigne welcome special guest Lauren Tickner to discuss strategies for maximizing time and success in the digital age. Lauren shares her insights on motivation, dealing with online haters, and the power of automation in business. The conversation delves into the importance of understanding risks and rewards, breaking free from traditional career paths, and the benefits of working in startups or entrepreneurial businesses. Lauren also provides valuable tips on social media content creation, utilizing storytelling and personalization to engage readers. Additionally, she introduces the PASTA framework for creating compelling social media posts and shares her approach to tracking and optimizing the client journey. Moments[00:01:16] The podcast uses a British voiceover actor to differentiate from East Coast accents. An AI voice named Bailey was later used, which can now be animated.[00:06:19] Successful asset manager quits job to pursue fitness career using social media. Simplifies life and focuses on selling premium packages. Finds success with minimal monthly sales.[00:08:05] The speaker discusses their upbringing in New York and the pressure to work in the financial industry. They admire the listener's decision to break free from that path and simplify things. They also comment on the listener's sense of humor and social media presence.[00:13:00] To simplify social media content creation: automate posting to multiple platforms, identify 5 topics to focus on, add personal storytelling to engage readers, and include a call to action to prompt specific actions.[00:19:41] The text discusses creating and sharing content for three different audience groups based on their familiarity with the author. It suggests using different types of content for each group, such as introducing oneself to new audiences, showcasing expertise to familiar audiences, and offering opportunities to become clients. The author also talks about segmenting content into top, middle, and bottom of the funnel, and using different calls to action to gauge audience interest.[00:24:09] Data shows that clients who watch 2 case studies before joining stay longer. We track client journey and added quick welcome call within 4 hours of joining for positive experience. Pooled calendar allows immediate availability for calls.[00:27:46] The author explains their approach to managing their business, aiming for a smaller internal company and owning multiple businesses rather than having a large team and many clients.[00:31:58] We should focus on the potential benefits, not just the downsides. Make realistic lists of what could go right and wrong. Replace "time" with "life" to make better decisions. Consider leaving high-paid jobs for startups or entrepreneurial businesses. Showcase the value you can bring to companies.[00:34:17] The speaker finds the content interesting and praises the concept, emphasizing the key takeaway. They inquire about finding more information.

On this episode of Data Driven, the focus is on hardware from AI optimized chips to edge computing.Frank and Andy interview Steven Orrin, the CTO of Intel Federal.Intel has developed new CPU instructions to accelerate AI workloads, and FPGAs allow for faster development in custom applications with specific needs. The speaker emphasizes the importance of data curation and wrangling before jumping into machine learning and AI, LinksWebinar: AI application benchmarking on Intel hardware through Red Hat OpenShift Data Science Platform. Register here: https://qrcodes.at/RHODSIntelBenchmarkingWebinarGet a free audiobook on us! http://thedatadrivenbook.com/Moments00:01:59 Hardware and software infrastructure for AI.00:07:18 AI benchmarks show importance of GPUs & CPUs00:14:08 Habana is a two-chip strategy offering AI accelerator chips designed for training flows and inferencing workloads. It is available in the Amazon cloud and data centers. The Habana chips are geared for large-scale training and inference tasks, and they scale with the architecture. One chip, Goya, is for inferencing, while the other chip, Gaudí, is for training. Intel also offers CPUs with added instructions for AI workloads, as well as GPUs for specialized tasks. Custom approaches like using FPGAs and ASICs are gaining popularity, especially for edge computing where low power and performance are essential.00:19:47 Intel's diverse team stays ahead of AI trends by collaborating with specialists and responding to industry needs. They have a large number of software engineers focused on optimizing software for Intel architecture, contributing to open source, and providing resources to help companies run their software efficiently. Intel's goal is to ensure that everyone's software runs smoothly and continues to raise the bar for the industry.00:25:24 Moore's Law drives compute by reducing size. Cloud enables cost-effective edge use cases. Edge brings cloud capabilities to devices.00:31:40 FPGA is programmable hardware allowing customization. It has applications in AI and neuromorphic processing. It is used in cellular and RF communications. Can be rapidly prototyped and deployed in the cloud.00:41:09 Started in biology, became a hacker, joined Intel.00:48:01 Coding as a viable and well-paying career.00:55:50 Looking forward to image-to-code and augmented reality integration in daily life.01:00:46 Tech show, similar to Halt and Catch Fire.Key Topics:Topics Covered:- The role of infrastructure in AI- Hardware optimization for training and inferencing- Intel's range of hardware solutions- Importance of software infrastructure and collaboration with the open source community- Introduction to Havana AI accelerator chips- The concept of collapsing data into a single integer level- Challenges and considerations in data collection and storage- Explanation and future of FPGAs- Moore's Law and its impact on compute- The rise of edge computing and its benefits- Bringing cloud capabilities to devices- Importance of inference and decision-making on the device- Challenges in achieving high performance and energy efficiency in edge computing- The role of diverse teams in staying ahead in the AI world- Overview of Intel Labs and their research domains- Intel's software engineering capabilities and dedication to open source- Intel as collaborators in the industry- Importance of benchmarking across different AI types and stages- The role of CPUs and GPUs in AI workloads- Optimizing workload through software to hardware- Importance of memory in memory-intensive activities- Security mechanisms in FPGAs- Programming and development advantages of FPGAs- Resurgence of FPGAs in AI and other domainsKey Facts about the Speaker:- Background in molecular biology bioresearch- Transitioned to hacking and coding- Started first company in 1995- Mentored by Bruce Schneier- Joined Intel in 2005- Worked on projects related to antimalware technologies, cloud security, web security, and data science- Transitioned to the federal team at Intel

On this episode of Data Driven, we explore the topic of distributed computing frameworks for AI and ML workloads. Frank discusses the advancements of Ray, a new technology based on Python language, with performance enhancements that could range from 10-12 times faster to thousands of times faster in extreme cases. We delve into the power of open source artificial intelligence and how it can aid data endeavors to accelerate these efforts. Along the way, we touch upon IBM and Red Hat's partnership, the evolution of technology, the importance of problem-specific solutions, and more. Stay tuned for a new episode of "Data Driven" and a special segment from our speaker on the potential AI holds for our future.[00:01:50] Ray is a new computing framework for AI/ML, may replace Spark, based on Python, can free people from PySpark.[00:03:49] Speaker has a MacBook M2 and prefers it over Windows. They enjoy stream-side streaming and wrote an article prompted by a question at work about a new technology claiming to be the next big data processing framework. They believe Ray still has an advantage.[00:06:51] Webinar about power of IBM-Red Hat partnership in AI. Speaker mentions travel with family and introduces production assistant.[00:11:34] Tech anticipated, surprised by speed of Chat GPT. Some dismiss as a fad, but it's different from predictive text like comparing paper airplane to an Airbus A 380, based on same principles but very different in implementation and technology.[00:13:30] Encourage attendance at AI webinar showcasing ethical concerns. Open source needed for transparency and risk-sharing. AI impact on all, even entry-level jobs and economy.