DeepOps for Business: Building an AI-First Company

What Data Scientists Want

Data scientists want to build a state of the art models through an iterative process. Neural network developers move the needle through these experiments, and through massive trial and error, data scientists refine these models, driving innovation.

So What’s the Problem?

A company has a lot of machines to run code and move data. This creates a processing issue. Data scientists are running machines at their desks using cobbled together an on-premise solution. Others are using legacy cloud systems, flipping between on-premise and scaled clouds.

The Risks of Experimentation

Data scientists might tweak and tweak again, retrying models without committing because no one cares about the garbage changes. Everything is fine until that one critical tweak that results in quality change, and now no one has a record or systematic documentation.

Building the Folder

In a typical folder, there’s a whole bunch of data with a bit of metadata. Running a new architecture allows you to drag a few files from your primary folder, allowing you to run the experiment. However, the folder system doesn’t work if you delete the folder or if you want to run many experiments.

Using a Database

A database does solve some of that issue, but databases aren’t suitable for all types of information. If you run a query once and then the same query a month later, you may not get the same results because of the database changes.

Complicating the issue

Most companies only have version control for the code, not the model or data. It makes critical questions challenging to answer because your scientists are now hunting for answers when things fall apart.

The DeepOps Answer

What if we could take everything we’ve learned in DevOps and apply it to questions like these to transform the way we think of version control and data experimentation? If you can’t reproduce your results, the evolution of your product is lost.

A Culture of Development

The biggest key for AI-transformation is creating a culture of responsibility throughout the pipeline. Each person has the keys to innovation, testing, and production. There are four key building blocks:

  • Test
  • Automate
  • Monitor

Greenfield’s DeepOps for Business Checklist:

Automating Documentation:

  • Params
  • Results
  • Compare
  • Query Data
  • Stream Data
  • Job queue
  • Cost speed knobs

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