Machine Learning and AI

Machine Learning and Artificial Intelligence

Organizations turn to AWS and Azure for ML and AI

Machine Learning and Artificial Intelligence platforms now integrate into both AWS and Microsoft Azure cloud environments.  Taking big data and analytics gathered in the cloud, developers create algorithms enabling ML and AI to actually learn!  In summary, how do organizations utilize ML and AI to gain competitive advantage, improve operational efficiencies and increase profitability?

To answer this question, let’s use an example for an agricultural business.  Information regarding the amount of sunlight, water, temperature and other factors gathered and organized in AWS or Azure cloud.  Using Machine Learning and Artificial Intelligence tools such as AWS SageMaker or Microsoft AI, developers seamlessly create dynamic algorithms.  To elaborate, these algorithms use the organized data to accurately predict the amount of product grown and harvested, even before fields are plowed!  In addition, ML and AI assist the decision makers in planning for optimal land utilization, number of workers and amount of resources needed to increase growth and harvest rates.

Let’s take a look at some of the ML and AI platforms offered by AWS and Microsoft in more depth.

Amazon SageMaker

Amazon SageMaker is a fully-managed service covering the entire machine learning workflow to label and prepare your data.  In addition, developers choose an algorithm, train the algorithm, tune and optimize it for deployment, make predictions, and take action. 

SageMaker seamlessly configures and optimizes TensorFlow, Apache MXNet, PyTorch, Chainer, Scikit-learn, SparkML, Horovod, Keras, and Gluon.  Commonly used machine learning algorithms are built-in and tuned for scale, speed, and accuracy with over a hundred additional pre-trained models and algorithms available in AWS Marketplace.  In addition, developers have the capability to bring any other algorithm or framework.

Amazon Forecast

Amazon Forecast uses machine learning to combine time series data with additional variables to build forecasts.  Forecast requires no machine learning experience to get started.  Developers only need to provide historical data, plus any additional data that you believe may impact specific forecasts.  For example, the demand for a certain fragrance of candle change with the seasons and store location.  This complex relationship possesses a high degree of difficulty to determine on its own, however machine learning uncovers unique correlations.  Once data is provided, Amazon Forecast automatically examines it, identifies relevancy, and creates a forecasting model capable of making predictions with 50% more accuracy compared to observing time series data alone.

AWS ML and AI infographic
AWS ML stack capabilities

Microsoft Azure AI

Microsoft Azure AI is a Python-based machine learning service with automated machine learning and edge deployment capabilities.  Developers deploy machine learning models using Azure Machine Learning, Azure Databricks and ONNX.  In addition, developers work with tools and frameworks of their choice without lock-in.  

Microsoft uses an interconnected dataset of world knowledge, organizational knowledge, and individual knowledge, called the Microsoft Graph, as the foundation for its ML and AI capabilities.  Comprised of signals across Bing, Office 365, Windows, and many other sources, the graph enables ground-breaking possibilities for organizations of any industry.

Microsoft AI Platform
Microsoft Artificial Intelligence (AI) Platform

Microsoft Intelligent Applications

Microsoft recently unleashed a plethora of applications built on a machine learning platform.  To democratize AI for organizations, Microsoft infused artificial intelligence throughout Microsoft 365. This infusion assists in enhancing the skills of individuals and teams and uncovering hidden insights.  In addition, organizations possess the ability to actively monitor and secure the organization against advanced threats and the risks caused by the proliferation of devices.

Intelligent applications such as Bing Predicts, MyAnalytics, and Power BI greatly enhance decision-making capabilities via research and data gathering methods.  Other ML and AI driven applications such as Microsoft Pix and Zo enhance the mobile device experience of users.  To summarize, integrating ML and AI into applications continues to enhance their performance and utilization.

Machine Learning (ML) and Artificial Intelligence (AI) inquiries

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