Development Cycles & Benefits


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The Cycle


Core system areas

  • Data pipeline
  • Human supervision for AI
  • Team communication and administration

Diffgram Benefits



No system


Frequency of Retraining

Hourly, Daily, or Weekly

2-6+ months


Number of Datasets

100s, 1000s.



Near real time options



0 -> 1

Diffgram shifts these concepts to become functions of your system (and level of integration with Diffgram). Experiment with many datasets and combinations. Improve performance by fine tuning sets for each store/location/sub system.

The net effect is that it enables the Data Science team to scale deep learning products. A system can ship with the human fall back (near real time option). Then as new stores and hard label cases are found datasets are split and only the rare, hard cases worked on.

Your system can undergo less initial evaluation - worries about ongoing performance are reduced because there is a clear path to continued retraining.

Development & Operation Cycles


Lean MLOps


New Paradigm

Diffgram does not provide any model evaluation or deploy. This diagram is provided to speak to the overall mindset and mental concepts at play.

Area of Focus



Trade offs

Model training & optimization

Tight connection to training data.

Expectation that model will be regularly retrained
Training data

Input/Output is part of core application.

Manual data input/output

Manual organization

Training is "one off"

Performance becomes primarily a function of training data

Better performance

Better path to fix data issues

Requires a system (like Diffgram).

Requires more engineering effort in general

Model evaluation

Set conditions for valid automatic deploy (ie above a threshold)

Sliding window approach on data for statistical evaluation methods, ie last 30, 60, or 180 days of data

Strong human integration. In addition to statistical analysis stronger “one off” and reasonableness checks.

Manually assess validity, ie looking at an Area under a Curve chart or a Confusion Matrix

Can maintain same rigor in terms of statistical checks, with tighter control on data relevancy.

Supports high frequency retraining and deploy often

Requires more engineering effort in general

Model deployment

Deploy early in fail-safe way. ie Starting as internal recommendations only

Deploy often with human in the loop

Heuristics to check for correctness

Mental model of trying to get a desired level of accuracy prior to deploying.

Projects become unstuck, no longer trying to achieve "perfect"

Projects more closely align with real world data

Reduces costs by shipping sooner and improves performance

Requires clear communication on expected failure modes

Increases operational effort.

Streaming Data - No Static Datasets

In this new paradigm there are no real "datasets" in the sense that the data is only static for the exact moment of training and evaluation. Data becomes more like a dynamic "channel" in that we have a continuous stream of new training data and it's using a conditional slice of it. The initial training process is effectively just a larger slice of the ongoing stream.

What’s Next
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