#machine learning lithuania

Automated machine learning or in short the autoML is a new adoption across every vertical as more companies are trying to get the best out of their data science programs. This trend leads to a scenario in which many data scientists find themselves wondering what is it which they can offer but autoML can't. Understanding this will need you to find how autoML fits and play in the entire data science life cycle.

Today with digitalization, various organizations are looking for ways to get better knowledge and insights from their data. For better predictive analysis, there is a demand for more data scientists with AI and ML expertise.

But a highly skilled data scientist is expensive and rare to found.  So there emerged the prodigy called “citizen data scientist” to seal the gap. This is analogous to the role instead of substitution.  They are best suited to make models that are useful in predictive analysis and diagnosis. But this functionality is incomplete due to the presence of autoML that is being used by many data scientists.

An autoML aims to shorten the methods of trial and error in experiments. It works through huge models and parameters used to create those models to decide the best model available for the data.

This is a monotonous and log activity, no matter how efficient a data scientist you are. AutoML can carry our long tasks fast and yield proper output. At Airbnb, there is continuous improvement in data science workflow.

They noticed that AML tools are significant to regress and classify problems that involve tabular datasets, and this sphere is progressing. It is said that in many ways autoML can bring lots of changes in data science. They used autoML in numerous ways.

Present challenger models without any biased attitude: AML can produce a huge count of models using a training set as your existing model. This helps data scientists to choose the best models.

Recognize if any leakage in target: As AML creates fast models, any kind of data leakage in the process can be early detected.

Diagnostic: In creating canonical diagnostics like the feature importance, learning curves, partial-dependence plots, and more.

Work like a detailed explanation of data analysis, data pre-processing, hyper-parameter tuning, select and put models for making can be done automatically to some extent with an autoML.

The organization is taking initiatives in bringing together huge data with an autoML, that makes use of the concept of machine learning to create a better AI, is claimed as an affordable opportunity and democratize ML by allowing firms with fewer data scientists to create an analytical pipeline that can take care of business issues in better ways.

With several Machine Learning algorithms, an auto ML  can carry out the start to end process of using ML to real-world problems. A standard machine learning pipeline has the following features: 

Data pre-processing

Extract features

Select features

Engineer features

Select algorithm

Hyper-parameter tuning

In a Forbes article, Ryohei Fujimaki, the CEO of dot data says that the debate and discussion are less impactful if it is to look for the emphasis of AutoML as a system to reduce the role of data scientists. The main area in data science is feature engineering.  This includes computing data sources against a list of required features that are then accessed against various ML algorithms. 

Conclusion

As when auto machine learning holds a huge part it can reduce the data scientists' tasks that are huge and repetitive.   With this evolution occurring, the data science team will continue playing other essential roles in an organization, they can find out what other areas of data science life cycle needs their attention and they can transfer their attention and shift their focus.0