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by spicyramen
2001 days ago
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What i have seen in the field was a frenzy of doing ML. What happened was that first of all companies needed to understand what ML was. Then understand the tools available. Once you start exploring the tools you will find that at every stage of a ML pipeline there is 2 or more different ways of doing things. S3/GCS, BigQuery, Spark, Beam, TensorFlow, Pytorch, Google, Azure, Amazon, notebooks, Jupyter, JupyterHub..KubeFlow, TFX...etc. okay you pick the tools needed, then you need to put them together...and hire people...that's challenging. I believe we need to wait for AutoML pipelines from data análisis to.prediction to start seeing really advancement in production systems. |
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