According to this Harvard Business Review article, the title of a data scientist is considered the sexiest job of the 21st century. To become an effective data scientist, upskilling in mathematics, programming, statistical analysis and computing may be an imperative requirement. Over the past decade the need for cloud computing allowing customers to purchase on-demand pay-as-you-go infrastructure has become the prime technology of choice for most companies. Over time this has opened doors for creating machine learning and AI tools that can benefit data science teams to build effective projects in the Cloud.
In this article, I evaluate how Azure developed a machine learning service tool that helps novice-to-expert data scientists with their Machine Learning projects.
First, let’s define exactly what Azure Machine Learning is:
Azure Machine Learning is a cloud service that maximises and manages the machine learning project lifecycle. It takes you through multiple stages from defining the problem such as identifying whether for example a patient has type 1 diabetes to exploring the data through cleaning, normalising, training, and validating the model and verify how accurate the model is in predicting that this patient has indeed type 1 diabetes to deploy the model and then finally monitor and manage the lifecycle.
As you can tell, it is an iterative process, which means this tool is great in the short and in the long run.
A wide range of multi-billion dollars companies from various sectors have benefited from this service, such companies include but are not limited to:
- The Boeing Company – Airlines, Airports & Air Services.
- National Grid – Electricity, Oil & Gas.
- Kimberly-Clark – Cosmetics, Beauty Supply & Personal Care.
- Verint Systems Inc. – Software Manufacturers.
To better understand what this tool can offer, it only makes sense to look at some of its key features.
When it comes to AI/ ML the projects defined tend to work with large amounts of data. When using deep learning especially, storage and High GPU is required. In layman terms, it’s like buying a house which requires more money to spend on significant amounts of renovation and maintenance. Cloud computing mitigates this problem by allowing us to leverage the infrastructure on demand, this means paying for usage only which has proven to save money immediately and in the long run.
Algorithms on demand
Knowing which algorithm to implement and run requires up-to-date knowledge and skills. Azure offers a cheat sheet which allows customers to work with now, and later select which algorithm fits their project. This functionality speeds up decision-making and prevents customers from missing out on unknown models.
No Code & Code Options
Azure features a couple of notable options to build models on: The first option is through notebooks which requires programming knowledge such as python. The second option offers a drag and drop feature from the lifecycle into the provided canvas making the tool more accessible for teams who may want to offer more insights to domain experts or analysts among others.
Easy Cloud tool integration
Monitoring and managing the lifecycle is the final step of the machine learning process. Azure already has tools for monitoring purposes such as linking statuses to a log analytics workspace and using services in the cloud to store code in repositories and run pipelines making the tool more efficient.
In conclusion, Azure Machine Learning has immense potential, it is easy to use, targets various skill sets and can save customers significant time and money.