Manual MLvs Auto ML

Task%204%20a-%20manualMLvsAutoML

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Auto ML is the fact of simplifying data science projects by automating the machine
learning tasks.
The machine learning process typically starts with raw data and ends with a
predictive model that can be used to make decisions. This process usually includes
the following steps:
1. Data Gathering to identify and collect input data.
2. Data Cleansing to standardize and clean the raw inputs.
3. Feature Processing to transform the input data into formats that can be easily
processed to identify the best predictor variables.

This process is impractical without significant expertise and it can easily take
statisticians weeks or months. Further, it requires frequent revision as new data
becomes available.
AutoML = AI to Train AI
The primary goal of AutoML is to make machine learning easier to use by
automating the entire process.
The obvious drawback of automated machine learning is that computers don’t have
the intuition of an experienced data scientist. However, AutoML addresses this
with one major advantage – it can try many different things really quickly! By
systematically testing a wide range of approaches, AutoML quickly builds
powerful models that would have taken significant expertise and months of time to
develop in the traditional way. The benefit is felt both at the initial deployment of
ML, which sees a greatly improved timeline, as well as on an ongoing basis, as
retraining of models can be done very quickly.
With respect to Interpretability:
Perhaps the largest impact can be felt by businesses with limited data science
resources. AutoML enables non-statisticians to train, assess and deploy models in a
way that simply wasn’t possible before. Given the widely publicized shortage of
data science talent, AutoML can play an important role in bridging the gap many
businesses face between having data and being able to effectively make decisions
with it.

With respect to Reproducibility:
Machine Learning is unlocking new sources of predictive power and can
meaningfully impact decision accuracy. It has attracted a lot of attention in recent
years as increases in data availability and computational power have made it more
practical and valuable. AutoML is moving to the forefront of this revolution by
making machine learning accessible to organizations of all sizes, leveling the
playing fields between large and small companies and increasingly shaping the
future of our economy.
Reference Links : https://digifi.io/blog/introduction-to-automated-machinelearning-automl/



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