Today, AI and machine learning are enabling data-driven organisations to accelerate their journey to insights and decisions. With all the latest advancements, AI is no longer limited to only those with deep expertise or a cache of data scientists, and many organisations can now adopt AI and machine learning for better competitive advantage.
Machine learning has many applications across a broad set of industries and scenarios. Using machine learning in addition to traditional analytics, organisations can, for example, predict their sales forecast, identify their next new revenue growth opportunity, gain insights on their most profitable products, and more, resulting in a far greater return on investment (ROI).
Azure Machine Learning is an enterprise-grade machine learning service to build and deploy models with machine learning operations (MLOps) capabilities to streamline the machine learning lifecycle and help automate it. It provides deep integration with Azure Synapse Analytics, a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics. Azure Machine Learning and Azure Synapse Analytics together help enable stronger collaboration between data engineers and data scientists, so they can build models with ease, enrich the enterprise’s data, and create even greater outcomes with their data.
Customers vested in analytics typically already have streamlined data processes, workflows, and teams in place and while there are synergies to be had from adding machine learning to the mix, there are also areas where additional investment will be required. AGL is one such Azure customer and a leader in Australia’s energy sector. AGL set up combined analytics and Machine Learning Center of Excellence (CoE) to create significant scalable and sustainable value through data and analytics for use cases that span optimizing procurement for power plants, forecasting demand, improving customer experiences, and more. They used Azure Synapse Analytics and Azure Databricks for data engineering and analytics, and Azure Machine Learning for quick and cost-effective training, deployment, and lifecycle management for thousands of parallel models. The architecture incorporates on-demand at-scale training, end-to-end model, and code management, automated MLOps deployments, model hosting, and performance monitoring.
“With Azure Machine Learning, we’re increasing speed-to-value while reducing cost-to-value.”—Sarah Dods, Head of Advanced Analytics, AGL
Another Azure customer, BRF, one of the largest food companies in the world, had a strong analytics practice and had already experimented with a successful machine learning use case. They built on this over time and established a CoE for advanced analytics and machine learning. Using Azure Synapse Analytics and Azure Machine Learning with its built-in MLOps capabilities, they solved many more use cases such as forecasting disease among livestock, customer purchase recommendations, traffic risks, and more.
“We already had a strong analytics practice and with the help of Azure Machine Learning we were able to better align to new use cases and quickly develop innovative product recommendation solutions for our customers.”—Wellington Monteiro, Global Data Science Chapter Lead, BRF
The company expects the continued alignment of its analytics and machine learning practices to generate commercial and operating advantages as it moves through the maturity levels of the combined analytics and machine learning lifecycle.
With seamless integrations and advanced capabilities, Azure Machine Learning and Azure Synapse Analytics make it easier for organisations to build a machine learning practice to achieve their business objectives. And organisations can accelerate their success by augmenting analytics with machine learning through a multi-stage maturity approach.
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