Cloud AI Services and AutoML – a growing trend

The 4th annual Artificial Intelligence Dev Conference at DeveloperWeek was a full day of thought leadership talks and technical workshops on the newest technologies and best practices in AI development. Cloud AI services and automated machine learning (AutoML) were the subjects of one of the talks, and in this article we’ll explore why they’re so popular and how they can help your organisation. 

Cloud computing has been around for years and most of the key providers now offer cloud-based AI services. These leverage the providers’ extensive expertise and deep pockets, and make it easier for business users (from SMEs to large budget-sensitive corporations) to get started in AI. It makes it less expensive for them in terms of the heavy lifting processor technology that AI demands, and also means they don’t have to buy in costly AI and ML expertise (or not as much anyway). 

IBM Cloud, Amazon Web Services, Google Cloud and Microsoft Azure all provide AI as a Service, and are making technological advances all the time. Here are some of the AI shortcuts they can currently give you: 

• IBM Cloud has a number of pre-built AI applications including Watson Speech to Text, Watson Visual Recognition, Watson Natural Language Classifier and Watson Discovery. They let you get deep insights from your data, interact with customers and train your AI systems on the language of your industry. AI lifecycle management tools such as Watson Machine Learning are also available. 

• AWS Machine Learning and Artificial Intelligence services include Amazon Polly for Text-to-Speech (TTS), Amazon Lex for conversational agents (STT), Amazon Rekognition for image and video analysis, Amazon Forecast to build accurate forecasting models, and Amazon Comprehend for advanced text analytics. 

• Google Cloud has a number of AI offerings, such as Cloud Vision API and Cloud Video Intelligence API, that build on its core search expertise. They let you identify objects, images, text within images and video, and explicit content. Text-to-Speech API, Speech-to-Text API and Natural Language API are also available, as well as DialogFlow for building chatbots. And Google gives you access to the Tensor Processing Unit (TPU) which is up to 30x faster than standard CPUs / GPUs. 

• Microsoft Azure AI services include the conversational Azure Bot Service and Azure Cognitive Services, which let you apply AI to the usual language, speech, vision and decision-making scenarios. Azure Machine Learning is also offered which includes a code-first and drag-and-drop designer. 

AutoML

That brings us on to AutoML. Machine learning is at the forefront of today's AI activities but it can require in-house expertise that is not always available. AutoML can help with this. It allows non-experts to make use of ML models without having to learn complex algorithms and advanced data science techniques.  

Normally with a ML application, you have a dataset of input data points to train on. Expertise is then needed to perform the data pre-processing, feature engineering, feature extraction and feature selection to make the dataset suitable for machine learning. Once you’ve done that, you then need to perform algorithm selection and hyperparameter optimisation to maximise predictive performance. AutoML dramatically simplifies this.

It is offered to various degrees by the main Cloud AI service providers, as well as other companies.  The Azure Machine Learning service, for example, lets you create models with the automated machine learning no-code UI or through a code-first approach. It can then intelligently select from a wide array of algorithms and hyperparameters to help you build highly accurate models. Google Cloud AutoML also enables you to train high quality models, even if you have limited ML expertise.

Cloud AI Services and AutoML can get you up and running fast. They are a growing trend. 

JBI Training provides a range of industry-leading AI/ML and Cloud training courses including:

• AI for Business & IT Staff training course (1 day) where you learn about the possibilities offered by AI as well as the terminology and market trends – See our AI for Business & IT Staff training course outline
• Data Science and AI/ML (Python) training course (5 days) where you get a comprehensive introduction to Data Science, AI and ML with Python – See our Data Science and AI/ML (Python) training course outline 
• Amazon Web Services (AWS) introduction training course (2 days) where you gain an understanding of what AWS offers – See our AWS intro training course outline  
• Azure Cloud training course (3 days) where you gain an understanding of what Azure Cloud Computing offers – See our Azure cloud training course outline 
• Google Cloud Platform  (3 days) where you learn to develop cloud-based applications on GCP – See our Google Cloud Platform training course outline 

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