ABSTRACT

Each cloud service provider currently offers their customers similar computational services and upsell their offering through unique and compelling AI functionality. Due to the complexity of the available services, companies often lack a set of criteria to clearly distinguish between providers, platforms, or product instances in order to make the best decision for their needs.

The main goal of this paper is to offer a structured view of the commercial AI cloud solutions currently on the market. It proposes a trifold perspective according to: the business focus areas, the technologies driving AI and the start-up situation in 2019.

Additionally, performing predictive analysis in a real-time scenario is not a simple task. Besides the technical and administrative aspects that need to be considered, it requires a strong commitment from the main stakeholders. The secondary objective of the paper is to offer a set of guidelines for a successful cognitive computing approach.

This paper is made up of four sections and has been shared over two parts:

Part one, which you can access here, consists of:

■ Introduction: Distinguishing between Narrow AI and Artificial General Intelligence (AGI) and how Cognitive Computing Systems are a step in the right direction toward achieving AGI.

■ Chapter 1: which tackles the trends in the enterprise cloud market. It covers issues related to operational costs, main players, market share and growth forecast. It sets the scene for understanding how the complexity of the ML domain and the speed at which these services are evolving makes it difficult to find a reliable, up to date, comparison between the available services.

■ Chapter 2: This chapter approaches this endeavour by looking at the AI domain from three different perspectives. First, we examine the key initiatives that drive companies to use AI: Insights, User Experience and Process Automation. Second, we analyse the classification of services proposed by the public cloud vendors and examine the position of the AI services among them. Finally, the 2019 start-ups that use the publicly available services are presented and the relation between the unicorns and their preferred technologies is highlighted.

Part two:

■ Chapter 3: This chapter focuses on the AI functionalities, grouped around the three key initiatives. The services offered by the main players in the cloud market are referred according to their commercial names and compared at a high level.

■ Conclusion: The paper ends with a set of general recommendations and an Annex which presents a summary of the different concepts offered as a service.

3. AI functionalities offered by the main cloud providers

The cloud providers offer similar tools for the most important AI functionalities, as presented in detail in Table 4. Due to the high competition, all the companies adopted the red ocean strategy which also involves a continuous value-cost trade-off and the differentiation vs low-cost approach.

In this chapter, we present a comparison between these tools together with the differentiation factors of each vendor.

Table 4. AI Tools offered by the main cloud providers.

Computer Vision

The Computer Vision related functionalities enable computers to gain, ideally, high-level understanding from images and video sequences in a similar way to humans. Computer vision algorithms are designed to automatically identify objects, people, text, scenes, and human activities. Moravec's paradox states that low-level perception skills, such as recognising or following an object in an image, are much more difficult to emulate by computers than high-level, abstract reasoning, such as chess playing. This is especially true in the computer vision domain because of the unpredictable environmental conditions, object topology or shapes, occlusions etc.

Traditionally, computer vision algorithms tackled these problems using camera geometry and predefined geometrical properties of objects together with a limited set of image primitives. Pixel-wise probabilistic classifiers (such as naive bayes), logistic regression or the usual distance-functions were used for object recognition with error rates hovered around 26%.

However, in 2012, convolutional neural networks (CNNs) represented a breakthrough in the history of computer vision, leading to a dramatic increase in the accuracy of the object detection methods. Even though CNNs have been around since the 80s, they became practical only after the machines became powerful enough and the GPUs started being used at a large scale for training and inference.

Nowadays, cloud providers offer both the tools and the necessary hardware to run such computationally intensive algorithms:

■ Amazon Rekognition can recognise and extract objects, recognise activities and faces, perform highly accurate emotion analysis, capture the paths of people in a scene and even detect inappropriate content. Amazon Rekognition can also be used to recognise text from images, using the Amazon Textract service, launched in May 2019. Amazon Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables.
■ Amazon Rekognition Video can analyse live streams in order to detect and understand activities and emotions, recognises and tracks people, objects, celebrities, and inappropriate content. Amazon Rekognition Video uses Amazon Kinesis Video Streams to receive and process a video stream.

■ Google Cloud Vision offers powerful pre-trained machine learning models but also enables the user to automate the training of custom machine learning models (AutoML Vision) both in the cloud and on edge devices. Moreover, Google has a dedicated team of people for high-quality annotation of images, videos, and text.
■ Cloud Video Intelligence offers pre-trained (API) and trainable machine learning models (AutoML Video) that can be used for recognising objects, places and actions in video streams in near real-time.

■ Microsoft’s Azure Vision Services provides developers with the necessary tools for image classification and tagging of thousands of objects within an image, for face recognition and OCR capabilities, both in the cloud and on edge devices. Also, it offers advanced algorithms for computer vision.
■ Video Indexer is a cloud application built on Azure Media Analytics, Azure Search, Cognitive Services (such as the Face API, Microsoft Translator, the Computer Vision API, and Custom Speech Service). Video Indexer enables the extraction from videos of insights such as face detection, celebrity identification, OCR, content moderation, labels identifications, scene segmentation, black frame detection, keyframe extraction, rolling credits etc.

Since OCR belongs to the Computer Vision domain, this recent study [Harding, 2019], shows a comparison between the accuracy and price for three image samples: a hand-written letter , webpage text , and text written on a whiteboard . The authors claim that, in their experiment, Microsoft’s solution performed much better than the other solutions and was also cheaper.

Table 5. Accuracy and price comparison between three of the most important cloud providers. Source [Harding, 2019].

Natural Language Processing

The Natural Language Processing related functionalities deal with automatic speech recognition which is used by developers that need to add speech-to-text capability in their applications. The first really successful implementation of an end-to-end speech recognition functionality was released in 2006 by the research group lead by Jürgen Schmidhuber. The group used a recurrent neural network called the long short-term memory (LSTM) together with a connectionist temporal classification (CTC) training algorithm [Graves et. al, 2006]. The same LSTM approach was used in 2015 by Google for speech recognition in its smartphones and in the Google Translate service. LSTM was also used by Apple for Siri and the Quicktype function, by Amazon in Alexa and by Facebook for automatic translations. Currently, developers can use speech-related functionalities from all the main cloud providers:

Machine Learning and Deep Learning

The Machine Learning and Deep Learning services provide software engineers and data scientists with the ability to build, train, and deploy machine learning models. The machine learning workflow includes operations related to data preparation, the choice of the appropriate algorithm, the choice of the platform for training the model, and the platform for the inference. Then, the developer has to scale and manage the cloud/edge/hybrid production environment, to ensure the correct operation and deliver the expected results.

All three cloud providers considered in this article offer end-to-end solutions for building custom machine learning solutions:

■ Amazon SageMaker framework is a fully managed service that covers the entire ML workflow, including a mechanical-turk approach for difficult data labelling. It provides a visual interface, improved by tools such as SageMaker Neo and SageMaker Ground Truth
■ Google Cloud ML Engine is a managed service that enables developers and data scientists to build ML models and bring them into production. All functionalities can benefit from the ML libraries such as Tensorflow, PyTorch and scikit-learn, and can run on top of hardware accelerators, such as Google’s TPUs (Tensor Processing Units) or classic GPUs. Moreover, Kubeflow, a high-level python SDK, enable ML architects to build ML pipelines by orchestrating the jobs, the models and visualising the results.
■ Microsoft’s Azure Machine Learning Studio gives access to a visual workspace that can be used to easily build, test, and iterate on a predictive analysis model. No programming required. Other cognitive services, for vision, speech or language processing, are available and pre-trained models can be accessed through AutoML.

Conclusions and recommendations

This paper presents an overview of the market expansion of the main cloud providers and underpins the current trends with a focus on AI services. The commercial cloud solutions available can be classified according to several criteria: the business focus areas (Insights, User Experience and Process Automation), the technologies driving AI (Computer Vision, Natural Language Processing and Machine Learning and Deep Learning) and the start-up landscape in 2019. Our conclusion is that, currently, the main focus of young companies is on AI solutions to improve user experience followed by process automation services and solutions for fraud detection.

The AI functionalities offered commercially all offer similar performance, with a few weak spots for each provider. In conclusion, the cloud market is not a zero-sum and a hybrid approach is recommended in order to take advantage of the best performance capabilities of each commercial solution.

Performing predictive analysis in a real-time scenario is not a simple task. Besides the technical and administrative aspects that need to be considered, it requires a strong commitment from the main stakeholders. It is crucial to educate and engage the senior management leaders in discussions about strategic priorities in relation to AI automation, building digital twins, predictive analysis, human activity augmentation, etc.

The development team has to find ways to gather data from any relevant physical device within the customer environment. The development process must begin with an exploratory phase that shall bring to light the principal components of the data involved. Then, the most suitable machine learning model and provider(s) can be determined. It is recommendable to develop a Proof of Concept (PoC) first in order to limit start-up costs and to have lean progress by checking periodically the results.

References

[Graves et. al, 2006], Graves, Alex; Fernández, Santiago; Gomez, Faustino (2006). "Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks". In Proceedings of the International Conference on Machine Learning, ICML 2006: 369–376.

[Harding, 2019] Bill Harding, 2019 Examples to Compare OCR Services: Amazon Textract/ Rekognition vs Google Vision vs Microsoft Cognitive Services

[Barot, 2019], Soyeb Barot, Solution Comparison for Cloud-Based AI Services, ID: G00377714, Gartner, 23 May 2019