Apr 1, 2021
The rate at which technology is advancing is incredible. From all of the use cases we’ve explored in this podcast alone, we’ve seen some mind-boggling uses of AI. As innovators, we’re always trying to push the boundaries of what’s possible – in this case via AI – but we also need resources to support rapid innovation when we get ahead of our skis. DevOps is a field that is enabling more safe and efficient deployment of software. Similarly, MLOps is exploding on the seen to enable ML teams to get models into production faster and more securely.
In this episode, I sit with Diego Oppenheimer, Co-Founder and CEO of Algorithmia. We discuss topics such as Diego’s experience building legendary products at Microsoft, optimizing the deployment of ML models, who is actually leveraging AI at scale, leveraging a freemium strategy to build champions, the importance of customer feedback, and much much more.
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Timestamps:
2:15 Moving finance as a career starter to business intelligence Microsoft
3:00 How a career in business intelligence tooling led to expertise in predictive analytics and ML
6:13 Developing a passion for using data to provide insights to clients that had gone unnoticed
8:44 The origin of Algorithmia | “No tools existed to optimize deployment of ML models”
10:53 What matters to DevOps? What is MLOps and why is it important?
12:42 Where Algorithmia sits within the ML Pipeline
14:00 How IT orgs and Data Scientists/ML engineers interact with the platform
15:23 KPIs and ROI metrics used when communicating value to clients (see algorithmia.com/resources)
18:45 Who is actually putting AI/ML into production?
20:25 Figuring out who the initial customer would be and what pains they’d experience
23:56 Creating a freemium model to entice data scientists | Building champions in an organization
26:15 Determining the first key hires as a startup
27:35 Establishing a process for getting early customer feedback
31:00 Favorite tech tool: Microsoft Excel
32:22 Why Diego has been inspired by Tableau and their approach to the data space
34:00 team.algorithmia.com – Try the product for free