Lessons Learned While Helping Enterprises Adopt Machine Learning

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No Limit Leadership

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81: From Nothing to a Billion: The Leadership Playbook They Don’t Teach You w/ Harry L Allen 36:51

לפני 10 ימים36:51

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36:51

Traditional banks often lack personalized service, and local businesses struggle to find true partnership in financial institutions. Meanwhile, higher education faces scrutiny over relevance and ROI in a world where information is nearly free. Harry Allen helped launch Studio Bank to blend technology with high-touch service, fueled by community investment. At Belmont, he's applying the same entrepreneurial mindset to modernize university operations and embed practical learning experiences, like a one-of-a-kind partnership with Dolly Parton, into academia. In this episode, Harry L. Allen, co-founder of Studio Bank and now CFO at Belmont University, unpacks the bold vision behind launching a community-first bank in a city dominated by financial giants. He shares the leadership lessons that shaped his journey, how to lead through crisis, and why mentorship is the key to filling today's leadership vacuum. Key Takeaways Leveraging both financial and social capital creates a unique, community-first banking model. High-tech doesn't mean low-touch, Studio Bank fused innovation with personal relationships. Leadership means showing up, especially during crisis. Universities must shift from being information hubs to delivering real-world experience. Succession and mentorship are vital to cultivating the next generation of leaders. Chapters 00:00 Introduction to Harry L. Allen 01:49 The Birth of Studio Bank 04:29 Leveraging Technology in Community Banking 07:25 The Courage to Start a New Venture 10:37 Leadership Challenges in High Growth 13:02 Leading Through Crisis: The COVID Experience 17:55 Transitioning from Banking to Education 21:16 The Role of Leadership in Higher Education 25:16 Adapting to Challenges in Higher Education 30:04 The Leadership Vacuum in Society 33:17 Advice for Emerging Leaders 35:21 The American Dream and Community Impact No Limit Leadership is the go-to podcast for growth-minded executives, middle managers, and team leaders who want more than surface-level leadership advice. Hosted by executive coach and former Special Forces commander Sean Patton, this show dives deep into modern leadership, self-leadership, and the real-world strategies that build high-performing teams. Whether you're focused on leadership development, building a coaching culture, improving leadership communication, or strengthening team accountability, each episode equips you with actionable insights to unlock leadership potential across your organization. From designing onboarding systems that retain talent to asking better questions that drive clarity and impact, No Limit Leadership helps you lead yourself first so you can lead others better. If you're ready to create a culture of ownership, resilience, and results, this leadership podcast is for you.…

לפני 7 שנים 31:32

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In this episode of the Data Show, I spoke with Francesca Lazzeri, an AI and machine learning scientist at Microsoft, and her colleague Jaya Mathew, a senior data scientist at Microsoft. We conducted a couple of surveys this year—“How Companies Are Putting AI to Work Through Deep Learning” and “The State of Machine Learning Adoption in the Enterprise”—and we found that while many companies are still in the early stages of machine learning adoption, there’s considerable interest in moving forward with projects in the near future. Lazzeri and Mathew spend a considerable amount of time interacting with companies that are beginning to use machine learning and have experiences that span many different industries and applications. I wanted to learn some of the processes and tools they use when they assist companies in beginning their machine learning journeys.

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Lessons Learned While Helping Enterprises Adopt Machine Learning

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published לפני 7 שנים

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443 פרקים

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1
Machine Learning for Operational Analytics and Business Intelligence 51:41

לפני 6 years51:41

51:41

In this episode of the Data Show, I speak with Peter Bailis, founder and CEO of Sisu, a startup that is using machine learning to improve operational analytics. Bailis is also an assistant professor of computer science at Stanford University, where he conducts research into data-intensive systems and where he is co-founder of the DAWN Lab.…

1
Machine Learning and Analytics for Time Series Data 40:33

לפני 6 years40:33

40:33

In this episode of the Data Show, I speak with Arun Kejariwal of Facebook and Ira Cohen of Anodot (full disclosure: I’m an advisor to Anodot). This conversation stemmed from a recent online panel discussion we did, where we discussed time series data, and, specifically, anomaly detection and forecasting. Both Kejariwal (at Machine Zone, Twitter, and Facebook) and Cohen (at HP and Anodot) have extensive experience building analytic and machine learning solutions at large scale, and both have worked extensively with time-series data. The growing interest in AI and machine learning has not been confined to computer vision, speech technologies, or text. In the enterprise, there is strong interest in using similar automation tools for temporal data and time series.…

1
Understanding Deep Neural Networks 39:34

לפני 6 years39:34

39:34

In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.…

1
Becoming a Machine Learning Practitioner 33:24

לפני 6 years33:24

33:24

In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.…

1
Labeling, Transforming, and Structuring Training Data Sets for Machine Learning 40:54

לפני 6 years40:54

40:54

In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services. Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.…

1
Taming Chaos: Preparing for Your Next Incident 29:45

לפני 6 years29:45

29:45

In this interview, Tim Craig and fellow Googler Gustavo Franco, a site reliability engineer (SRE), discuss the wide range of events that qualify as “incidents;” the need for a conscious, robust, and well-defined process for understanding them; the role of training; and how to get buy-in from management so you can spread incident response training throughout an organization.…

1
Make Data Science More Useful 35:06

לפני 6 years35:06

35:06

In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses. We had a great conversation spanning many topics, including: How data science can be more useful The importance of the human side of data The leadership talent shortage in data science Is data science a bubble?…

1
Acquiring and Sharing High-Quality Data 39:23

לפני 6 years39:23

39:23

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”…

1
Tools for Machine Learning Development 39:27

לפני 6 years39:27

39:27

In this week's episode of the Data Show, we're featuring an interview Data Show host Ben Lorica participated in for the Software Engineering Daily Podcast, where he was interviewed by Jeff Meyerson. Their conversation mainly centered around data engineering, data architecture and infrastructure, and machine learning (ML).…

1
Enabling End-to-End Machine Learning Pipelines in Real-World Applications 42:56

לפני 6 years42:56

42:56

In this episode of the Data Show, I spoke with Nick Pentreath, principal engineer at IBM. Pentreath was an early and avid user of Apache Spark, and he subsequently became a Spark committer and PMC member. Most recently his focus has been on machine learning, particularly deep learning, and he is part of a group within IBM focused on building open source tools that enable end-to-end machine learning pipelines.…

1
How to Get Started With Site Reliability Engineering (SRE) 38:32

לפני 6 years38:32

38:32

At Google’s 2019 Cloud Next conference, I sat down with Stephen Thorne, site reliability engineer on Google’s customer reliability engineering team and co-author of "The Site Reliability Workbook," to talk about how organizations, both large and small, can use SRE to reduce operational costs, improve reliability, and create productive cross-functional teams.…

1
Bringing Scalable Real-time Analytics to the Enterprise 37:14

לפני 6 years37:14

37:14

In this episode of the Data Show, I spoke with Dhruba Borthakur (co-founder and CTO) and Shruti Bhat (SVP of Marketing) of Rockset, a startup focused on building solutions for interactive data science and live applications. Borthakur was the founding engineer of HDFS and creator of RocksDB, while Bhat is an experienced product and marketing executive focused on enterprise software and data products. Their new startup is focused on a few trends I’ve recently been thinking about, including the re-emergence of real-time analytics, and the hunger for simpler data architectures and tools. Borthakur exemplifies the need for companies to continually evaluate new technologies: while he was the founding engineer for HDFS, these days he mostly works with object stores like S3.…

1
Applications of Data Science and Machine Learning in Financial Services 42:35

לפני 6 years42:35

42:35

In this episode of the Data Show, I spoke with Jike Chong, chief data scientist at Acorns, a startup focused on building tools for micro-investing. Chong has extensive experience using analytics and machine learning in financial services, and he has experience building data science teams in the U.S. and in China. We had a great conversation spanning many topics, including: Potential applications of data science in financial services. The current state of data science in financial services in both the U.S. and China. His experience recruiting, training, and managing data science teams in both the U.S. and China.…

1
Real-Time Entity Resolution Made Accessible 27:11

לפני 6 years27:11

27:11

In this episode of the Data Show, I spoke with Jeff Jonas, CEO, founder and chief scientist of Senzing, a startup focused on making real-time entity resolution technologies broadly accessible. He was previously a fellow and chief scientist of context computing at IBM. Entity resolution (ER) refers to techniques and tools for identifying and linking manifestations of the same entity/object/individual. Ironically, ER itself has many different names (e.g., record linkage, duplicate detection, object consolidation/reconciliation, etc.). ER is an essential first step in many domains, including marketing (cleaning up databases), law enforcement (background checks and counterterrorism), and financial services and investing. Knowing exactly who your customers are is an important task for security, fraud detection, marketing, and personalization. The proliferation of data sources and services has made ER very challenging in the internet age. In addition, many applications now increasingly require near real-time entity resolution. We had a great conversation spanning many topics including: Why ER is interesting and challenging How ER technologies have evolved over the years How Senzing is working to democratize ER by making real-time AI technologies accessible to developers Some early use cases for Senzing’s technologies Some items on their research agenda…

1
Why Companies are in Need of Data Lineage Solutions 34:31

לפני 6 years34:31

34:31

In this episode of the Data Show, I spoke with Neelesh Salian, software engineer at Stitch Fix, a company that combines machine learning and human expertise to personalize shopping. As companies integrate machine learning into their products and systems, there are important foundational technologies that come into play. This shouldn’t come as a shock, as current machine learning and AI technologies require large amounts of data—specifically, labeled data for training models. There are also many other considerations—including security, privacy, reliability/safety—that are encouraging companies to invest in a suite of data technologies. In conversations with data engineers, data scientists, and AI researchers, the need for solutions that can help track data lineage and provenance keeps popping up. There are several San Francisco Bay Area companies that have embarked on building data lineage systems—including Salian and his colleagues at Stitch Fix. I wanted to find out how they arrived at the decision to build such a system and what capabilities they are building into it.…

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