Mission: To curate and create resources for practitioners to design, develop, deploy, and maintain ML applications at scale to drive measurable positive business impact.
Machine Learning starts with collecting data.
An overview of MLOps processes.
Integrate Early and Iterate Often for Successful ML in Production
Set up your ML project for success: integrate early and iterate often.
Thinking about product and user experience is even more important in ML assisted products.
Failure is the best teacher, albeit an expensive one.
ML development today looks similar to how software development looked while it transitioned from the waterfall to iterative development process.
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