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Friday’s TWIMLcon Executive Summit closed out a full first week at the conference! Speakers from BP, Walmart, Accenture, Qualcomm, Orangetheory Fitness, and more shared their experiences and insights on key issues faced by AI/ML leaders and teams. The day began with a keynote interview featuring Franziska Bell, VP of Data and Analytics at BP. Fran had some very strong advice on what it takes to ensure ML project success. Her principles include creating mutual partnership between the business and the data team early on in the process; working hard to ensure that the data team is actually solving the business need; and emphasizing the importance of empathy, understanding, and common goals and language among the cross-disciplinary teams building data products. The first panel of the day focused on Building the Business Case for ML Platforms and featured Divya Jain (Director of ML Platform, Adobe), Justin Norman (VP Data Science and Analytics at Yelp), and Kirk Borne (Principal Data Scientist and Executive Advisor, Booz Allen Hamilton). We discussed business value, measuring impact and ROI, build vs. buy, centralized vs. embedded teams, and standardization of infrastructure vs. flexibility. One attendee question prompted panelists to explore the topic of whether centralization was even a good thing. All panelists had strong opinions on this topic--not always in agreement--but Justin summarized it well with the following: “Businesses have many teams, those teams have requirements and those requirements should drive the platform choices. If it makes sense to centralize something... then do it. But if a team is doing something very unique with a different set of requirements than the other teams, they may need their own vertically integrated stack.” The next session had Adrian Cartier (VP of Data Science, Ocelot Consulting), Andy Minteer (Senior Director, Digital Transformation - Head AI Products, Walmart Global Tech), Jurgen Weichenberger (Data Science Senior Principal & Global AI Lead, Resources, Accenture) up to discuss Why ML projects Fail and How to Ensure Their Success. Right off the bat, Andrew challenged the idea of failure and had us rethink what failure even means. He asked: “What if the model is accurate but nobody adopts it? Isn’t that also failure?” Jurgen, who has worked with many customers in many industries, cautioned that it’s important to back up even further and to assess where the customer is on their maturity curve: Some industries are further ahead than others and that will drive a lot of what success and failure even mean to them. The panel closed with a discussion about the central role of people in the technology decisions leaders make. Jurgen offered: “It is our obligation to bring our customers on the journey with us. We need to be in the mindset that we are enabling people to do their jobs. You need to take the whole company on a journey with you... Bring them along, build trust and confidence, and show them how this can make their lives easier.” The fourth session of the day centered around what is required when Building Teams and Cultures that Support ML Innovation. For this discussion, we invited Ameen Kazerouni (Chief Analytics Officer, Orangetheory Fitness), Pardis Noorzad (Head of Data Science, Carbon Health), and Ziad Asghar (VP of AI at Qualcomm) to share their thoughts. The conversation included topics such as: what are the factors in building high-performance teams; how do we measure team success; and what is the role of culture in building teams. Sufficient budgets, common language, and shared rituals were all mentioned as key elements enabling effective teams. The impact of the pandemic on teams, namely the accelerated shift to remote work, was discussed as well. Ziad left us with this amusing thought on the topic: “If 2020 had a t-shirt, it would read: ‘Hey we can’t hear you, you’re on mute,’” illustrating how fundamental some of the challenges we face are. The final session of the day, the Executive Summit Roundtable Discussion, was a particularly animated and rich discussion. Ameen Kazerouni, Hussein Mehanna (VP, Head of ML/AI, Cruise), and Paul van der Boor (Senior Director of Data Science, Prosus Group), each shared their experiences on a topic relevant to leading ML teams and then Sam facilitated a great discussion afterwards with the attendees. A few of the many compelling ideas that came out of this section include: Ameen’s suggestion that: “The currency of an analytics team is trust, not data.” Paul’s insights from the experience of one of the teams at Prosus which has developed dashboards to granularly track the impact of every ML model they deliver on the business, and the need to understand whether a project’s key contribution is operational (improving what you already do) or innovation (doing new things). Hussein’s definition of “AI Native Products” as those that must leverage AI even at the MVP stage and his mind-blowing hypothesis (presented first at TWIMLcon!) that in order for organizations to create AI-Native Products that they need to organize internally like a neural network. We had a fun and engaging discussion after those three wrapped up and I think the summary was that we in the ML community have been spoiled with an explosion of new tools and techniques, and that at some point, there will likely be a “great reckoning” where the toolchain will all converge and MLOps will become more standardized. As one attendee, Gavin Bell, put it: “We used to have serial ports, parallel ports, printer ports, display ports, headphone jacks...and now it’s all USB-C. What is the respiration of this round of technology going to leave behind? “ Big thanks to Adrian, Ameen, Andy, Divya, Franziska, Jurgen, Justin, Kirk, Pardis, Paul, Ziad, Hussein and all of the Executive Summit attendees for a fun and stimulating day of discussion on these very important topics. And special thanks to Qualcomm, Executive Summit Platinum Sponsor. If you missed the session today, it’s not too late to register for TWIMLcon! There are still four more days of sessions next week. “Executive” tickets offer on-demand access to all of the Executive Summit sessions you missed, as well as the entirety of TWIMLcon. All tickets offer on-demand access to all regular conference sessions through the end of January, and “Pro Plus” and “Executive” tickets let you watch replay sessions whenever you like. You can check out the TWIMLcon agenda here and the speakers here. See you next week!
There's no doubt about it. Machine learning and AI are getting real in the enterprise. Many of the ML/AI leaders and practitioners I speak to report being in a similar place: With their initial POC projects maturing, and a few successes under their belts, their colleagues across the business finally get it. They're ready. They want in. Meaning, the various line-of-business leaders want to apply the power of machine learning to their own businesses. Great news, right? Maybe. The problem is that successfully delivering an ML proof-of-concept requires a very different set of skills, approaches, and technologies than successfully supporting a portfolio of ML efforts at scale and in production. I'm organizing a conference, TWIMLcon: AI Platforms, that is bringing together ML and AI leaders and practitioners as well as experts from across the industry to take a deep look at this important topic and share and learn from one another. (Check out a few of the great speakers we've announced here.) Here are the top 8 things that enterprises need to work on in order to move beyond POCs and successfully scale ML and AI across the organization. 1. How to organize your ML workflows for greater productivity and support them with platform technologies. At TWIMLcon we'll hear case studies from companies like SurveyMonkey, Zappos, NVIDIA and a bunch more, exploring various aspects of how they got there. 2. How to eliminate low-value, repetitive, or just frustrating tasks so that data scientists and ML engineers can spend their time on what’s most important. This is what Airbnb's Atul Kale calls in our interview eliminating the incidental complexity of ML, so that Data Scientists and ML Engineers can focus on its inherent complexity. 3. How to increase the speed and consistency with which useful models are created. This is the one I hear folks get the most excited about, especially those in leadership. There's growing appreciation that increasing speed and reducing cycle time is core to using ML as an engine for innovation and transformation. 4. How to eliminate the barriers to getting models into production, and how to manage their lifecycle once they’re there. Building ML models is one thing. Getting them into production and effectively managing their lifecycle is another altogether. I'm particularly looking forward to my live podcast interview with Andrew Ng, where we'll explore the things he's seen successful companies do to get deep learning into production. 5. What your peers across different industries are doing to industrialize their own machine learning efforts. Learning from others and taking what you hear back to your own organization is a cheat-code for innovation in a nascent, fast-moving space! It's also important to know how to translate what others are doing to your own unique needs, and I'll be touching on this in my keynote interview with Cruise's Hussein Mehanna. 6. How to incorporate fairness, accountability, transparency, ethics and governance into ML/AI workflows. This is an important topic that we've spent a lot of time covering on the TWIML AI Podcast. I'm excited to have Rachel Thomas (the newly appointed Director of the USF Center for Applied Data Ethics, among other things) on our Operationalizing AI Ethics panel to discuss this. 7. What are the most promising tools & technologies to help meet your ML/AI goals. The most successful model-driven companies have all invested heavily in tooling and platforms that allow them to achieve goals like faster cycle times, more accurate models, great success productionalizing models, and more. Until recently, if you wanted this kind of tooling you had to build it yourself. Now companies like SigOpt, Cloudera, Fiddler Labs, Verta AI, Valohai, Weights and Biases, and Figure Eight offer either end-to-end platforms or specialized components that can help your organization achieve it's ML goals more rapidly. (These companies and more will be on hand to demo their products at #TWIMLcon.) 8. How to organize ML and data science organizations for greater success. This is a biggie. Last but definitely not least. Organization and culture can be the wind in your sails or what holds you back. On of our panels on this topic will feature StitchFix's Eric Colson, whose interview with me on this topic you should definitely check out, and Twitter's Pardis Noorzad, whose post Models for Integrating Data Science Teams Within Organizations is worth a look. If you made it this far, I think you'd really enjoy what we've got planned for TWIMLcon: AI Platforms. Hit the link below to sign up, or reach out with any questions!