AI & Emerging Tech Trends

Craig Martell shares the biggest mistakes leaders of machine learning teams make, what to do if you have no experience leading an ML team, key skills your ML team needs, plus different models/approaches to building an ML team. You’ll hear the most expensive and time-consuming parts of machine learning, how to estimate timelines, unique tech debt, and how to manage expectations.

Melissa Binde, VP Engineering, Cloud @ Splunk, shares lessons from her time building Amazon Apollo. You’ll hear the origin story of Amazon Apollo, how to define the right problem, identify the right solution, and what makes technology endure 20+ years. Plus how to improve your engineering storytelling and pitch the right product features to the right stakeholders!

Maia Brenner (AI Specialist & Head of Business Development @ Tryolabs) shares the fundamentals to begin your company’s AI/ML journey! We cover the most common challenges & pitfalls eng leaders face when investing in AI, how to understand feasibility/impact & ROI of different AI/ML initiatives, how to build your AI roadmap, how to break down massive AI/ML projects into small experiments, and how to accelerate different phases of your AI/ML strategy with partners like Tryolabs.

Jeremy King (SVP of Engineering @ Pinterest) discusses some of the challenges, principles & frameworks behind building inclusive products. We also cover filtering decisions through your company mission, investing in rest, and emerging challenges around creating serendipity with ideas, onboarding, retaining talent, and the hard logistics of workplace flexibility.

Lewis Tuff (VP of Engineering @ Blockchain.com) joins us to share his experience on building a career in the cryptocurrency space! We cover his transition from traditional finance/tech to crypto, how to shift your mindset from centralized to decentralized, qualities that lead to success, tactics to help you gain exposure and experience, AND why it’s not too late to start your career in crypto!

This is the first episode of our new series “Engineering Founders” featuring software pioneer Anna Patterson (Founder & Managing Partner @ Gradient Ventures) who shares with us emerging trends & opportunities in AI/ML! We cover how to spot emerging trends, typical mistakes AI/ML companies & founders make, how product market fit/scaling is different vs. traditional software companies AND how to test and validate ideas in the early stages. Plus long-time listener Theo Gervet (ML Lead @ Relyance AI) joins us as a guest co-host!

We discuss the challenges with leading eng orgs through unpredictable global events (like the covid-19 pandemic) & pivoting product strategy with Bridget Frey (CTO @ Redfin). We also cover the challenges & successes of building out AI/ ML products in your engineering org, creating an “uncertainty” algorithm, incorporating mental health best practices in your teams, and addressing data bias & systemic racism in your product’s features! Featuring guest co-host Ali Littman (Interim Head of Eng @ Modern Health).

This episode features one of our most fascinating sessions from ELC Annual 2022 – we explore how businesses, creators, and researchers are building the “generative generation” & harnessing generative AI’s potential  – featuring Mira Murati, CTO @ OpenAI, Naveen Gavini, SVP, Products @ Pinterest & moderated by Vijay Karunamurthy Head of Engineering @ Scale AI. This session covers the current state & future of generative AI, its potential impacts, what is being done to mitigate potential risks, how smaller companies can take advantage of open-source generative AI technology, and the factors impacting future generative AI innovation. Interested in topics like this, and beyond? #ELCAnnual2023 is happening 8/30 & 8/31! You can get your ticket to join your peers, check out all our speakers + explore additional topics at sfelc.com/annual2023

Clemens Mewald, Head of Product @ Instabase, joins our podcast to share his insights on how to embrace generative AI tools to enhance engineering leadership, productivity, and problem solving. We also chat about creative ways to implement GenAI beyond simply chatbots, questions to ask when deciding how to leverage GenAI capabilities, rethinking the productivity aspect of generative AI, and how to use it to address cross-functional collaboration challenges within your org. Additionally, Clemens reveals how he translates his deep technical / research knowledge on generative AI into meaningful business & product outcomes.

In this episode, we’re resharing one of the most popular & exciting sessions from ELC Annual 2023, featuring a panel of experts discussing what software dev will look like in the decades to come! This conversation features Tara Hernandez, VP Developer Productivity @ MongoDB; Erik Meijer, Sr. Director of Engineering @ Meta; and Jocelyn Goldfein, Managing Director @ Zetta Venture Partners. They debate & dissect how AI is changing what software dev looks like, what capabilities future eng leaders will need to build upon, where AI technology will need to improve moving forward, and more.

In this episode, we chat with Lake Dai (Founder & Managing Partner @ Sancus Ventures), who shares her career story & how to harness AI technology in a way that is both effective & compassionate. We cover the concept of “co-parenting AI”; why ethics in AI is non-negotiable; and how to create compassionate AI. Lake also reveals how she became a veteran at serving on boards & why it is something she is passionate about. We dissect ways current eng leaders who are interested in board service can gain the right experiences & demonstrate their value as a potential board member.

In today’s episode, we’re resharing Dheeraj Pandey’s popular session from ELC Annual 2023 on the disciplined pursuit of less! As the Co-Founder, CEO & Chairman of DevRev.ai, he shares how AI tools can maximize customer impact & reduce information asymmetry between various teams, including eng, customer support, product, sales, etc., ultimately creating a more customer-centric mindset. He reveals how to leverage AI to tackle “verbs,” such as classifying, routing, attributing, summarizing and more, further streamlining productivity and empowering your org to focus on customer needs.

We discuss how to navigate the delicate balance between meeting current customer needs while also preparing for future tech trends & opportunities with Evan Welbourne, Head of AI and Data @ Samsara. Evan dissects the rapidly transforming pace of developing AI/ML products, sharing strategies for merging conversations around differing product-building processes, tips for moving seamlessly / gaining approval between product development stages, defining what customer success looks like, methods for working backward from problems, and best practices for avoiding friction throughout the product development process. He also shares frameworks for envisioning & working toward future tech possibilities while simultaneously developing hypotheses that inform future direction, creating diverse AI/ML team composition, and effectively communicating with stakeholders.

We had a blast at ELC Annual 2024, so we wanted to bring our podcast listeners some of the best highlights from popular sessions! This episode features one of the ELC Annual sessions with Anupam Singh (VP of AI & Growth Engineering @ Roblox) & Maria Kazandjieva (Co-Founder @ Graft), as they discuss building AI/ML models at a massive scale. Anupam shares how Roblox – an immersive 3D platform with more than 77 million daily active users – scaled from zero to nearly 200 different AI models. They discuss strategies for deciding when to use open source vs. creating proprietary models; how to operationalize your models for 24/7 use; the importance of data pipelines; current and future challenges to keep in mind when creating / scaling AI models; and answer some questions from the live Q&A.

We’re back with another session from ELC Annual 2024! This episode features an engaging session on collaboration & innovation in the time of AI with Anurag Agarwal, VPE, Google Workspace @ Google, and Lizzie Matusov, Co-Founder & CEO @ Quotient! In this conversation, they dissect how AI is transforming not only the products engineering teams are building but also how teams work together internally. They cover how Google / Google Workplace specifically use AI both internally & externally, strategies for creating & maintaining alignment across a large org, how Anurag addressed challenges during this transitional period, and more.

In this episode, we explore building an AI-first company and engineering org with Rong Yan (CTO @ HeyGen)! We dive into the potential of HeyGen’s interactive avatars, imagining how they can help engineering leaders scale their impact, foster team alignment, coach effectively, and accelerate decision-making. Rong shares insights on the structure of an AI-first company and optimizing for AI teams with engineering capabilities. Plus what it means to “lead with speed” and balance product quality and velocity in an AI-first company and key leadership principles, like why it’s crucial to invest in your top performers and how to act as a productivity multiplier.

Anurag Agarwal (VP, Engineering @ Google Workspace) returns to debrief the latest integrations and rapid releases of AI product experiences across Google Workspace. We discuss strategies to help teams execute / iterate faster; streamlining the decision-making process; questions to ask during prioritization conversations; identifying bottlenecks of inefficiency; gaining user insights & iterating based on that feedback. Plus how to enable easier feature discovery in your product experience. Anurag also shares insights into Google Workspace’s current projects & what he’s most looking forward to in the future!

Jim Palmer (Chief AI Officer @ Dialpad) discusses the current state and future of data governance, scaling AI responsibly, and practical strategies for companies at different stages of AI adoption. We break down the impact of red-teaming / adversarial testing on AI governance, mitigating risk, unexpected positive outcomes from red-teaming, and how to launch and scale adversarial testing efforts. Plus investments in data & governance you should be focusing on, structured data as a cheat code to durability & adaptability, building in data deletion systems and other essential tips & strategies for an evolving AI regulatory and governance environment.

In this episode, we explore how to accelerate your AI learning journey with Maher Hanafi, SVPE @ Betterworks! Maher shares his progression from using LLMs as a proof of concept to tackling real-world use cases and complex challenges like data governance, data flow, enterprise AI, and AI agents. Maher also dissects the importance of team experimentation with real use cases, revisiting AI decisions with your team, when to go horizontal vs. vertical with AI learning, overcoming POC-to-production challenges; and evaluating the ROI of your AI decisions. Special thanks to our guest co-host Corey Coto, a local leader of ELC Seattle!

The future of work is decentralized. With no one single point of failure, companies have the opportunity to unlock the full potential of anyone, anywhere. One company who is taking decentralization seriously is Twitter. As the company works to grow its global workforce to reflect the diverse voices who use the service daily, they're also looking at ways to enable anyone, anywhere to work at Twitter. Join Twitter's head of engineering, Michael Montano, as he shares more about his thoughts on building Twitter's engineering organization for the future, enabling asynchronous collaboration, and hiring diverse, specialized talent faster.

How is managing an AI team different from traditional engineering teams, and how should founders and leaders think about the collaboration between AI and engineering when scaling their organizations? In this fireside conversation, Anna Patterson from Gradient Ventures and Adrien Treuille, Co-Founder and CEO of Streamlit, will dive into best practices for streamlining collaboration between AI and engineering teams. This discussion will also draw on personal experiences to touch on the process of transitioning from engineer to founder, from a team contributor to managing teams of contributors, ultimately highlighting how to ensure effective collaboration amongst AI and engineering teams as a leader.

Craig Martell shares the biggest mistakes leaders of ML teams make, what to do if you have no experience leading an ML team, key skills your ML team needs, plus different models/approaches to building an ML team. You’ll hear the most expensive and time consuming parts of ML, how to estimate timelines, unique tech debt, and how to manage expectations.

Hear insights from Chief Technology Officer, Sriram Thiagarajan on how Ancestry, 30-year-old tech company, reinvented itself by taking advantage of technology transitions and the evolution of modern technology architectures. Sriram shares details on the successes and challenges of Ancestry’s digital transformation journey, including migrating to a 100% public cloud architecture, adopting cloud native services, and unleashing the power of AI/ML to accelerate the pace of innovation and deliver new features more frequently.

The last two years have been monumental in terms of the impact current events have had on modern business. But even before the impetus of COVID-19, there was already a more fundamental transformation taking place with the rise of the developer. Every industry is turning digital, and every company needs the best software to win the hearts and minds of customers. Join us for a discussion with Navin Chaddha, Managing Director at Mayfield, Dheeraj Pandey, Co-Founder & CEO at DevRev and former Founder, CEO & Chairman at Nutanix, and Nikil Viswanathan, Co-Founder & CEO at Alchemy for a discussion on dev-first, product-led growth, and web3 trends. What kinds of platforms and business models are thriving in today’s modern cloud-first companies? Where do developers come into the picture? And what will lead the way tomorrow?

According to Gartner, only 53% of machine learning proofs of concept (POC) are ever scaled to production. Even fewer manage to deliver the intended and measurable business value. On the bright side, Gartner projects that by 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5x increase in streaming data and analytics infrastructures. Machine learning ops (ML Ops) is a practice and methodology for preparing, deploying and managing machine learning models in production. Productizing ML Ops is hard, but in reality, it’s a requirement for AI scalability and success. In this Q&A session, Sapta Girisa, Senior Director at Lohika and Capgemini Engineering will discuss key considerations for ML Ops, why it’s essential to AI scalability and how to accelerate your AI engineering.

Advances in cloud infrastructure and the broad adoption of hybrid/remote work models have given organizations new options for driving productivity and operational efficiency. Now that so many devices are connected to our networks outside of the data center, we’re seeing a significant rise in security attacks around the world. Join us for a fireside chat with Anand Oswal, SVP, Network Security for Palo Alto Networks, to discuss how the cybersecurity industry is innovating with AI to respond to the changing threat landscape. One of the most exciting developments is adoption of Deep Learning techniques to stop attacks before they happen.

Media and e-commerce companies intuitively understand the necessity of storytelling, but the practice of narrative crafting is still developing for Machine Learning teams. Storytelling is a superpower, and one that leading technologists must acquire to effectively lead teams. Building a high-functioning ML team means uniting perspectives–on data value, on MLOps, and on how your problem domain evolves. Using examples from Scale AI, including work with media and e-commerce, Vijay will articulate how to structure your ML team's story–in a way which is both inclusive, and committed to solving the world’s most pressing challenges.

Starting with the announcement of OpenAI’s DALL·E 2 in April of this year, enthusiasts and research engineers alike have generated a tidal wave of interest in the latest quantum-leap ML advances for Generative AI—particularly for forms of human expression. DALL·E 2 pioneered a new AI technique known as Diffusion Models: learning from a huge range of photography and visual expression on the open web, then “denoising” an initial input of random noise, while using the power of Transformers to structure how the resulting image evolves based on an input prompt. Over the last 6 months, DALL·E 2 has attracted enthusiastic supporters (and a few critics, as with any new disruptive technology) in artistic, engineering, and research communities. That’s because these models power new forms of expression, including techniques that work “backwards” from images to understand the concepts the model “sees”. In this panel, we’ll compare and contrast the impact that Generative AI has had across a wide range of products and tasks, from the impact on the field of software engineering, to the workflows adopted in creative fields. We’ll also talk about the impact on curation and community on the web, and how the act of discovery and inspiration might change with workflows powered by Generative AI.