James Hood

The revolutionary multi-sensory mindfulness adventure that started it all.

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→ “Startup Wallaroo Labs wins Space Force contract to model performance of AI on edge devices” (SpaceNews)


  • Most companies wait too long to think about production. Start before you feel you need to do it.
  • Issues to look for when operationalization AI models: 1) Production environment, 2) integration, and 3) data pipelines.
  • ML Ops empowers data scientists to build better models, go faster, and limit business risks.
  • Edge data/predictions must be incorporated into a central AI program to enable cross machine insights.


  • Data + AI = Value — not so fast. Not so easy because of deployment issues.
  • Predictive analytics is a widely applicable use case. From space, to automotive and manufacturing.
  • Wallaroo helps data scientists to operationalize AI. From the lab to a production environment.
  • You can’t do “set and forget” with data science models. Requires constant monitoring.
  • Issues to look for when operationalization AI models
    1. Production environment — what is the hardware that will be hosting the model.
    1. Integration — How will you model input data and output insights in a production setting?
    1. Data Pipelines — Do you have the data streams expected? How are you monitoring the health of those data pipelines?
  • How MLops helps different personas
    1. Data scientists — spend more time on model development, rather than deployment/monitoring.
    1. Business Unit Leaders & MLOps engineers— Do more with less resources. Faster.
  • Empower data scientists to
    1. Be bolder — build more interesting and sophisticated models.
    1. Go Faster — Shrink innovation cycle times.
    1. Limit Business Risk — Minimize translation errors in going from data science to production.
  • Most companies wait too long to think about production. Start before you feel you need to do it.
  • Appreciate that your core IP is in data and process. Monitoring and deployment, look for tools like Wallaroo.
  • Thinking about benchmarking:
    1. “Bake-off” — Comparing models to one another. Swap between different models based on the value of predictions/insights.
    1. Raw Performance: Throughput, latency, etc. Compare across various production deployments.
  • Edge vs. centralized managed AI. If you do inference at the edge, but aren't integrating back together in a centralized place in order to learn across deployments, you’re leaving “money on the table.”

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