Data-Centric-AI Development The Platform Approach

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2 min read
Machine Learning project lifecycle

Dear friends,

It can take 6 to 24 months to bring a machine learning project from concept to deployment, but a specialized development platform can make things go much faster.

My team at Landing AI has been working on a platform called LandingLens for efficiently building computer vision models. In the process, I’ve learned important lessons about how such platforms can help accelerate the machine learning project lifecycle:

  • Data collection: Ambiguity in labels (what is the “correct” value of y?) plagues many projects. If the labels are inconsistently defined, it’s impossible to achieve a high test-set accuracy. But it’s difficult to find these inconsistencies manually and to convince stakeholders (often subject-matter experts) to resolve them. An MLOps platform can identify problems and encourage consistency.
  • Model training: The ability to write code to train a model in TensorFlow or PyTorch is a valuable skill. But even for skilled engineers, it’s faster to use a no-code platform that lets you do this via mouse clicks (to manage data augmentation, link the data and model, manage GPU training resources, keep track of data/model versions, and provide visualizations and metrics for error analysis).
  • Production deployment: Many teams can execute a successful proof of concept and achieve high-test set accuracy. But to secure budgets and approval for deployment, a small demo can help others see a project’s value. A platform can make it easy to implement a demo that runs not just in a Jupyter notebook but in a lightweight deployment environment such as a mobile app or simple edge device.

It used to take me months to deploy a model. With a no-code platform, I can train a RetinaNet demo, carry out error analysis, use a data-centric approach to clean up inconsistent data, retrain, and deploy to an edge device — all in 60 minutes. I get a thrill every time I go through the machine learning project lifecycle so quickly.

Platforms like this can help a variety of AI projects across all industries. LandingLens works well for visual inspection in areas as diverse as automotive, semiconductor, and materials, I’m hoping to make it more widely available. Its sweet spot is computer vision problems (detection or segmentation) with 30 to 10,000 images. If you have a business problem in computer vision that falls in this sweet spot, I’d like to hear from you. Please get in touch by filling out this form.

Keep learning!



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