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AzureML Will Put an End to Data Science Jobs in Near Future

AzureML

Exploring the chances of AzureML replacing the data science jobs in the future.

AzureML has become a powerful tool as it empowers data scientists and developers to build, deploy and manage high-quality models faster and with confidence. AzureML expertly accelerates time to value with industry-leading MLOps (machine learning operations), open-source interoperability, and integrated tools. It aims to innovate on a secure, trusted platform designed for responsible machine learning (ML). However, the question remains if it will put an end to the data science jobs in near future.

 

What Are the Chances?

Altogether, AzureML improves productivity with the studio, the development experience that supports all ML tasks to build, train and deploy models. It collaborates with Jupyter notebooks using built-in support for popular open-source frameworks and libraries. One of the major factors is how it creates accurate models quickly with automated ML, using feature engineering and hyperparameter-sweeping capabilities. AzureML accesses the debugger, profiler, and explanations to improve model performance as you train and also uses deep Visual Studio Code integration to go from local to cloud training seamlessly and autoscale with powerful cloud-based CPU and GPU clusters.

 

How does it work?

Microsoft offers a free trial of AzureML, where, once logged in, one is presented with a simple blank screen with three tabs for “Experiments”, “Web Services”, and “Settings”. To begin an experiment, one clicks on a button to start a new experiment. Azure then presents a blank canvas with a menu of modules along the left side of the screen. A module is represented as a rectangular block with some number (usually 0-2) of incoming ports and outgoing ports. A dataset has no incoming ports and a single outgoing port. Building an experiment consists of selecting a dataset and dragging it onto the canvas. Additional modules can then be dragged onto the canvas and connected to each other with directed edges (from an outgoing port of one module to the incoming port of another). For convenience, Microsoft has pre-populated each account’s dataset repository with a large sampling of UCI datasets. Preprocessing modules include missing data scrubbing, feature selection via linear discriminant analysis, duplicate column detection, and more. Supported algorithms include multi-class neural networks, logistic regression, boosted decision trees, support vector machines, and locally deep support vector machines.

 

Conclusion

Building and testing a model is fairly simple after perusing a few examples. Overall, Azure is a mature and impressive service. It requires knowledge of the characteristics of machine learning algorithms, and certainly will not develop new ones automatically. But it does provide an environment where machine learning could be used effectively without low-level implementation of algorithmic knowledge. For the savvy end-user data scientist, Azure could potentially eliminate many hours of repetitive work by automating data preprocessing and providing a convenient environment in which it is possible to quickly explore data and generate predictions. So, it can be concluded on the note that AzureML will not end the future of data science but it would rather improve the working process with its advanced features.

 

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