LINKS
→ “Startup Wallaroo Labs wins Space Force contract to model performance of AI on edge devices” (SpaceNews)
KEY POINTS
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Most companies wait too long to think about production. Start before you feel you need to do it.
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Issues to look for when operationalization AI models: 1) Production environment, 2) integration, and 3) data pipelines.
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ML Ops empowers data scientists to build better models, go faster, and limit business risks.
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Edge data/predictions must be incorporated into a central AI program to enable cross machine insights.
NOTES
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Data + AI = Value — not so fast. Not so easy because of deployment issues.
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Predictive analytics is a widely applicable use case. From space, to automotive and manufacturing.
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Wallaroo helps data scientists to operationalize AI. From the lab to a production environment.
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You can’t do “set and forget” with data science models. Requires constant monitoring.
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Issues to look for when operationalization AI models
1.
Production environment — what is the hardware that will be hosting the model.
2.
Integration — How will you model input data and output insights in a production setting?
3.
Data Pipelines — Do you have the data streams expected? How are you monitoring the health of those data pipelines?
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How MLops helps different personas
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Data scientists — spend more time on model development, rather than deployment/monitoring.
2.
Business Unit Leaders & MLOps engineers— Do more with less resources. Faster.
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Empower data scientists to
1.
Be bolder — build more interesting and sophisticated models.
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Go Faster — Shrink innovation cycle times.
3.
Limit Business Risk — Minimize translation errors in going from data science to production.
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Most companies wait too long to think about production. Start before you feel you need to do it.
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Appreciate that your core IP is in data and process. Monitoring and deployment, look for tools like Wallaroo.
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Thinking about benchmarking:
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“Bake-off” — Comparing models to one another. Swap between different models based on the value of predictions/insights.
2.
Raw Performance: Throughput, latency, etc. Compare across various production deployments.
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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.”
