Sep 9–12, 2019

Herding cats: Product management in the machine learning era

Ira Cohen (Anodot)
9:00am12:30pm Tuesday, September 10, 2019
Location: LL21 C/D
Secondary topics:  Machine Learning
Average rating: ***..
(3.50, 4 ratings)

Who is this presentation for?

  • Product managers, data science managers, CTOs, and CIOs




While the role of the manager doesn’t require deep knowledge of machine learning algorithms, it does require understanding how ML-based products should be developed and the ability to bridge the gap between product and business requirements and the inherent uncertainty at any ML-based solution. This uncertainty follows an ML-based solution at all phases: there’s rarely certainty that any ML-based solution can solve a given business problem. The development cycle involves research iterations to continuously try improving the results—a cycle that isn’t deterministic. After the deployment of a solution, the results aren’t deterministic and may be erroneous, requiring handling of those in the product design and also careful monitoring of the ML performance (e.g., a model predicting churn of a subscriber outputs a probability for churn, which by itself may be wrong because of modeling issues, input data issues, etc).

Ira Cohen walks you through the cycle of developing ML-based capabilities (or entire products) and the role of the (product) manager in each step of the cycle using real-world examples.


Initial design

  • Mapping a business problem to a ML solution (e.g., type of ML solution required, any constraints on learning time, runtime, and more)
  • Mapping the outcomes of ML to the desired outcome from the user perspective (e.g., accuracy, visualization, and product flow wrapping the ML capability)

Research phase

  • Data collection: Determining which data is available or should be produced
  • Experimentation: Determining the success criteria
  • How to know when to stop iterations

Deployment phase

  • Requirements from the production process (real time versus offline, batch operations versus stream operations, etc.)
  • How to roll out new versions and improvements

Production phase

  • Monitoring requirements to ensure ML-based service performance
  • Product design to handle uncertainty in outcomes of the ML component

Prerequisite knowledge

  • A working knowledge about the types of machine learning concepts and where they are applicable (e.g., the difference between supervised versus unsupervised learning and types of ML solutions—classification, regression, clustering, etc.)

Materials or downloads needed in advance

  • A WiFi-enabled laptop

What you'll learn

  • Discover the three things every manager should know
Photo of Ira Cohen

Ira Cohen


Ira Cohen is a cofounder and chief data scientist at Anodot, where he’s responsible for developing and inventing the company’s real-time multivariate anomaly detection algorithms that work with millions of time series signals. He holds a PhD in machine learning from the University of Illinois at Urbana-Champaign and has over 12 years of industry experience.

  • Intel AI
  • O'Reilly
  • Amazon Web Services
  • IBM Watson
  • Dataiku
  • Dell Technologies
  • Intuit
  • Gamalon
  • Hewlett Packard Enterprise
  • MapR Technologies
  • Sisu Data
  • Intuit

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