Every now and then we come across the term Machine Learning. It is the buzzword and is taking the technology world by storm. You’ll be amazed to know that public clouds are now providing machine learning services. Artificial intelligence and machine learning, unlike other cloud-based Phone services, are now available through various delivery models. Cognitive computing, ML model management, GPU-based computing and ML model serving are few of the many diverse models which make the AI and ML services available via the cloud.
Machine learning is a set of AI and technologies which is closely related to pattern recognition and computational learning. It is a modern name to an old concept which was first defined back in 1959 when the idea of “Computer’s capacity to learn without reprogramming” was put forward. It was once a really far star for most of the enterprises but now the service is available on public clouds. The fast technological advancement which made ML available on public cloud calls for enlightenment. That is particularly what we are going to discuss in this article.
We are going to educate you all with delivery models that are being adopted by public cloud providers. It will certainly be of great help to all the businesses in order to find themselves the most suitable cloud-based machine learning and Artificial intelligence services. Like Iaas, PaaS, and SaaS the original cloud delivery models, ML and AI clouds spin the infrastructure and provide high-level APIs. Let’s discuss the public cloud models which provide AI and ML services.
Cognitive computing in layman language is computerizing the human thoughts. It can be delivered into certain sets of API’s (Application programming interface). These API’s offer a clear computer vision, NLP (natural language processing) and speech services. Developing such API’s is not big of a task for the developers. They certainly don’t have to know minute details about the machine learning, data processing pipelines, etc.
The increase in consumption of these services has boosted the quality of cognitive services. The increasing use of these services and the bulk of data flowing in every day is making the precision of these services quite accurate. The predictions are becoming more reasonable and precise with every passing day.
The recent addition to cognitive computing is that of the automated machine learning (AutoML). In AutoML service n cognitive computing, developers can utilize the APIs after making use of the training services with a customized set of data. AutoML is offering the facility to consume pre-training models and customer training models from the very scratch. The biggest example of cognitive services is that of Microsoft Cognitive services, IBM Watson API and Google cloud AI APIs’.
If you’re thinking of adding AI capabilities into your existing or newly developed applications, you can ask the developers to evaluate cognitive services in the public cloud. It will enable you to tap into all the available AI services. These API’s are equivalent to that of the SaaS, you only pay for your usage.
When a cognitive API’s doesn’t meet the requirements, you can always seek ML PaaS to build yourself a better-customized machine learning model. For example, A cognitive system may have been smart enough to identify the vehicle as a car but it cannot provide the specifications about the car like its model, make, etc. Assume that you have a large set of data labeled according to their make and model. The data science team can totally rely on the customized ML PaaS to get and deploy the specific model which is specially tailored for a business model.
It is similar to the PaaS delivery model for which the developers bring their special set of codes which instruct a model against custom data. The data scientist test runs these codes in the local environment before putting them on the job in the public clouds.
Amazon SageMaker, Google Cloud ML Engine, and IBM Watson Studio, etc.are the major examples of ML Platforms.
ML Infrastructure Services:
According to experts, ML infrastructure services are considered the IaaS of the machine learning stack. The cloud providers offer Virtual machines to support high-end Central processing units and accelerators (GPU and FPGA etc.).
It is generally for the data scientists which require raw computing power. They have to rely on the Dev ops teams for configuration and provision. The workflow in this model is not so different. It is quite similar from setting up a test for the web to selecting the number of cores of CPUs for a specific version of Python or end to end configuration. The projects which are highly relied on toolkits and libraries, organizations choose ML infrastructure for better performance. They will an ultimate control over the hardware and configurations which may not available in ML PaaS assistances. A huge company like cox communications would require ML infrastructure services to stay on top of the technologies. As they are the top-notch telecommunication company. Cox internet is serving several big enterprises with their daily functioning.
The recent investment of giants like Facebook, Google, and Microsoft into ML infrastructure has made it economical and efficient than it ever was. Even the cloud providers are now providing customized hardware for the smooth functioning of ML. Google Tensor Processing Unit (TPU) and Microsoft’s Field programmable gate arrays are good examples of customized hardware which are specifically designed for ML jobs to accelerate the process. When the customized hardware is combined with recent computing trends like ML infrastructure or Kubernetes, they become the most suitable and perfect choice for the Enterprise.
A few great examples of IaaS ML are:
- Amazon EC2 deep learning AMI supported with NVIDIA GPU
- Google Cloud TPU
- Microsoft Azure Deep learning virtual machine based on NVIDIA GPU
- IBM Bare Metal servers based on Graphics processing unit
ML has become a significant workload public cloud provider which is why they are investing in core infrastructure to attract the enterprises. I hope the article has been of great help in learning about the particular delivery models utilized to make Machine learning available on public clouds.
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