Podcasts about cloudera fast forward labs

Machine Learning Operations (MLOps or ML Ops) is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently, as defined in various publications.  In this podcast we take on the topic of  MLOPs. What is it and is it like DevOps for AI?  Turns out it's broader than you might think including everything monitoring to governance and explainability.  Adewumni shares why it's both necessary and exciting. For her 30 second recommendation, Ade shared the Cloudera Fast Forward Labs blog which can be found here. She also mentioned a report by the Algorithmic Justice League on bug bounties for algorithmic harms which can be found here. You can follow Ade on Twitter  @Adewunmi  and  @FastForwardLabs . You can also find her on Medium medium.com/@adeadewunmi and LinkedIn here. You can follow me on Twitter @MaribelLopez and on LinkedIn here. 

In part one of an excellent series on AI product management, LinkedIn Research Scientist Peter Skomoroch and O’Reilly VP of Content Strategy Mike Loukides explained the importance of aligning AI products with your business plans and strategies. In other words, they have to deliver value, and they have to be delivered on time. Unfortunately, this is much easier said than done. I was curious to learn more about what goes into the complex AI product development process, and so for answers I turned to Yelp VP of Data Science Justin Norman, who collaborated with Peter and Mike in the O’Reilly series of articles. Justin is a career data professional and data science leader with experience in multiple companies and industries, having served as director of research and data science at Cloudera Fast Forward Labs, head of applied machine learning at Fitbit, head of Cisco’s enterprise data science office, and as a big data systems engineer with Booz Allen Hamilton. He also served as a Marine Corps Officer with a focus in systems analytics. We covered: Justin’s definition of a successful AI product The two key components behind AI products The lessons Justin learned building his first AI platform and what insights he applied when he went to Yelp. Why AI projects often fail early on, and how teams can better align themselves for success. Who or what Beaker and Bunsen are and how they enable Yelp to test over 700 experiments at any one time. What Justin learned at an airline about approaching problems from a ML standpoint vs. a user experience standpoint—and what the cross-functional team changed as a result. How Yelp incorporates designers, UX research, and product management with its technical teams Why companies should analyze the AI, ML and data science stack and form a strategy that aligns with their needs. The critical role of AI product management and what consideration Justin thinks is the most important when building a ML platform How Justin would approach AI development if he was starting all over at a brand new company Justin’s pros and cons about doing data science in the government vs. the private sector. Quotes from Today’s Episode “[My non-traditional background] gave me a really broad understanding of the full stack [...] from the physical layer all the way through delivering information to a decision-maker without a lot of time, maybe in an imperfect form, but really packaged for what we're all hoping to have, which is that value-add information to be able to do something with.” - Justin “It's very possible to create incredible data science products that are able to provide useful intelligence, but they may not be fast enough; they may not be [...] put together enough to be useful. They may not be easy enough to use by a layperson.” -Justin “Just because we can do things in AI space, even if they're automated, doesn't mean that it's actually beneficial or a value-add.” - Justin “I think the most important thing to focus on there is to understand what you need to be able to test and deploy rapidly, and then build that framework.” - Justin “I think it's important to have a product management team that understands the maturity lifecycle of building out these capabilities and is able to interject and say, ‘Hey, it's time for us to make a different investment, either in parallel, once we've reached this milestone, or this next step in the product lifecycle.’” - Justin “...When we talk about product management, there are different audiences. I think [Yelp’s] internal AI product manag

Welcome to another episode of Develomentor. Today's guest is Alice Albrecht. Alice is currently an independent consultant. She is also building a brain-computer interface startup. Prior to this, she led the strategy and advising portions of Cloudera Fast Forward Labs, where she was also a research engineer.Before joining Cloudera Fast Forward Labs, Alice worked in both finance and technology companies as a practicing data scientist, data science leader, and - most recently - a data product manager. In addition to teaching machines to do cool things, Alice is passionate about mentoring and helping others grow in their careers. Alice holds a PhD from Yale in cognitive neuroscience where she studied how humans summarize sensory information from the world around them and the neural substrates that underlie those summaries.Click Here –> For more information about tech careersEpisode Summary“We’ve had this huge surge in the use of deep learning and neural networks and all of these more complicated and very, very data-hungry techniques and organizations have seen a lot of benefit from those. But I think that in the next couple of years we’re going to see a plateau in the advancement there and we’re going to have to rethink the way that we’re developing these algorithms.”—Alice AlbrechtIn this episode we’ll cover:How Alice started college when she was 15 and was on the fast track to becoming a professorWhy Alice left academia to work in the private sector and technologyHow a neuroscience background gives Alice an advantage when working in data scienceWhat Alice looks for when hiring data scientistsWhy Alice believes deep learning will hit a plateau in the next couple of yearsYou can find more resources and a full transcript in the show notesTo learn more about our podcast go to https://develomentor.com/To listen to previous episodes go to https://develomentor.com/blog/Follow Alice AlbrechtTwitter: @AliceAlbrechtLinkedIn: linkedin.com/in/alice-albrecht-6379868/Follow Develomentor:Twitter: @develomentorFollow Grant IngersollTwitter: @gsingersLinkedIn: linkedin.com/in/grantingersoll

For the final episode of our Strata Data series, we’re joined by Eric Colson, Chief Algorithms Officer at Stitch Fix, whose presentation at the conference explored “How to make fewer bad decisions.” Our discussion focuses in on the three key organizational principles for data science teams that he’s developed at Stitch Fix. Along the way, we also talk through the various roles data science plays at the company, explore a few of the 800+ algorithms in use at the company spanning recommendations, inventory management, demand forecasting, and clothing design. We discuss the roles of Stitch Fix’splatforms team in supporting the data science organization, and his unique perspective on how to identify platform features. The complete show notes for this episode can be found at https://twimlai.com/talk/257. For more from the Strata Data conference series, visit twimlai.com/stratasf19. I want to send a quick thanks to our friends at Cloudera for their sponsorship of this series of podcasts from the Strata Data Conference, which they present along with O’Reilly Media. Cloudera’s long been a supporter of the podcast; in fact, they sponsored the very first episode of TWiML Talk, recorded back in 2016. Since that time Cloudera has continued to invest in and build out its platform, which already securely hosts huge volumes of enterprise data, to provide enterprise customers with a modern environment for machine learning and analytics that works both in the cloud as well as the data center. In addition, Cloudera Fast Forward Labs provides research and expert guidance that helps enterprises understand the realities of building with AI technologies without needing to hire an in-house research team. To learn more about what the company is up to and how they can help, visit Cloudera’s Machine Learning resource center at cloudera.com/ml.

In this episode of our Strata Data conference series, we’re joined by Burcu Baran, Senior Data Scientist at LinkedIn. At Strata, Burcu, along with a few members of her team, delivered the presentation “Using the full spectrum of data science to drive business decisions,” which outlines how LinkedIn manages their entire machine learning production process. In our conversation, Burcu details each phase of the process, including problem formulation, monitoring features, A/B testing and more. We also discuss how her “horizontal” team works with other more “vertical” teams within LinkedIn, various challenges that arise when training and modeling such as data leakage and interpretability, best practices when trying to deal with data partitioning at scale, and of course, the need for a platform that reduces the manual pieces of this process, promoting efficiency. The complete show notes for this episode can be found at https://twimlai.com/talk/256. For more from the Strata Data conference series, visit twimlai.com/stratasf19. I want to send a quick thanks to our friends at Cloudera for their sponsorship of this series of podcasts from the Strata Data Conference, which they present along with O’Reilly Media. Cloudera’s long been a supporter of the podcast; in fact, they sponsored the very first episode of TWiML Talk, recorded back in 2016. Since that time Cloudera has continued to invest in and build out its platform, which already securely hosts huge volumes of enterprise data, to provide enterprise customers with a modern environment for machine learning and analytics that works both in the cloud as well as the data center. In addition, Cloudera Fast Forward Labs provides research and expert guidance that helps enterprises understand the realities of building with AI technologies without needing to hire an in-house research team. To learn more about what the company is up to and how they can help, visit Cloudera’s Machine Learning resource center at cloudera.com/ml. I’d also like to send a huge thanks to LinkedIn for their continued support and sponsorship of the show! Now that I’ve had a chance to interview several of the folks on LinkedIn’s Data Science and Engineering teams, it’s really put into context the complexity and scale of the problems that they get to work on in their efforts to create enhanced economic opportunities for every member of the global workforce. AI and ML are integral aspects of almost every product LinkedIn builds for its members and customers and their massive, highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.

Today, in the first episode of our Strata Data conference series, we’re joined by Shioulin Sam, Research Engineer with Cloudera Fast Forward Labs. Shioulin and I caught up to discuss the newest report to come out of CFFL, “Learning with Limited Label Data,” which explores active learning as a means to build applications requiring only a relatively small set of labeled data. We start our conversation with a review of active learning and some of the reasons why it’s recently become an interesting technology for folks building systems based on deep learning. We then discuss some of the differences between active learning approaches or implementations, and some of the common requirements of an active learning system. Finally, we touch on some packaged offerings in the marketplace that include active learning, including Amazon’s SageMaker Ground Truth, and review Shoulin’s tips for getting started with the technology. The complete show notes for this episode can be found at https://twimlai.com/talk/255. For more from the Strata Data conference series, visit twimlai.com/stratasf19. I want to send a quick thanks to our friends at Cloudera for their sponsorship of this series of podcasts from the Strata Data Conference, which they present along with O’Reilly Media. Cloudera’s long been a supporter of the podcast; in fact, they sponsored the very first episode of TWiML Talk, recorded back in 2016. Since that time Cloudera has continued to invest in and build out its platform, which already securely hosts huge volumes of enterprise data, to provide enterprise customers with a modern environment for machine learning and analytics that works both in the cloud as well as the data center. In addition, Cloudera Fast Forward Labs provides research and expert guidance that helps enterprises understand the realities of building with AI technologies without needing to hire an in-house research team. To learn more about what the company is up to and how they can help, visit Cloudera’s Machine Learning resource center at cloudera.com/ml.

A special edition of Big Data News featuring a number of quick interviews at the booths in the community expo hall. A big thank you to the brave people there that were willing to face the Roving Roaring Mike at the Barcelona Dataworks summit a couple, of weeks ago. 03:04 Attunity    https://www.attunity.com/   07:41 Cloudera Fast Forward Labs https://www.cloudera.com/products/fast-forward-labs-research.html    11:09 DataVard https://www.datavard.com   17:19 Cazena https://www.cazena.com/   22:39 Syncsort https://www.syncsort.com   26:22 Accenture https://www.accenture.com   30:44 Unravel Data https://unraveldata.com Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

In this episode of our Strata Data conference series, we’re joined by Justin Norman, Director of Research and Data Science Services at Cloudera Fast Forward Labs. Fast Forward Labs was an Applied AI research firm and consultancy founded by Hilary Mason, who’s TWiML Talk episode remains an all-time fan favorite. My chat with Justin took place on the 1 year anniversary of Fast Forward Labs’ acquisition by Cloudera, so we start with an update on the company before diving into a look at some of recent and upcoming research projects. Specifically, we discuss their recent report on Multi-Task Learning and their upcoming research into Federated Machine Learning for AI at the edge. To learn more about Cloudera and CFFL, visit Cloudera's Machine Learning resource center at cloudera.com/ml. For the complete show notes, visit https://twimlai.com/talk/185.