Making AI products never been easier.
Use Cases and Deployment Scope
I use Azure to build, train, and deploy machine learning models using Python SDK. It’s great in way that it allows several ways of software design. For businesses and institutions, Azure saves the cost and time needed to start building and running big machine learning models and deep learning neural networks, this is done through the cloud so we do have to buy expensive equipment and GPUs or even go into the hustle of understanding which one to buy. It was useful during COVID as it allows remote working using the cloud-based services, which can run on any machine with good access to the internet; I was able to run big machine learning models using just my notebook. The other good thing about it is that it’s energy efficient; with all the huge computing running on the cloud, we are not really consuming any energy, and I can leave it running on the cloud and turn off my laptop; this is great when compared to my other local machines with GPUs where I need to keep it running day and night until the models finish running. In addition to consuming a lot of energy, it also means I’ll not be able to use my machine to run any other processes. Luckily with Azure Machine learning designer, this is something from the past.
Pros
- Leverage machine learning as a service.
- Accelerate machine learning with best-breed algorithms.
- Support remote working with cloud-based services.
- Creation of compliant and secure machine learning applications.
Cons
- Limited storage for those using the free version.
- Limited number of machine learning algorithms.
- Because of its high-level design, it doesn’t give the developers much control as you don’t write much code in to do what you exactly want.
Most Important Features
- Help students build AI and machine learning models easily and quickly, making it a good in-class digital resource.
- Saves money, time, and effort by providing you access to high-end computational power.
- Facilitates remote and team working with its cloud-based services.
Return on Investment
- Reduce energy consumption caused by GPUs.
- Saves on recycling and transporting costs and maintenance caused by buying high-end equipment.
- Improve productivity as building products using Azure is easier than building everything up from scratch (e.g., machine learning and AI applications).
Alternatives Considered
Amazon Deep Learning AMIs
Other Software Used
Amazon Deep Learning AMIs, Google Cloud AI, OpenAI