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Aboard the analytics train? Machine Learning is Your Next Stop

 If your organization is data-driven, business intelligence-savvy and/or into big data analytics, you know the amount of work that goes into setting up the infrastructure, acquiring the skills and building the culture necessary to sustain it. But did you know that investment puts you on the cusp of being an AI-driven organization too?

While most data and analytics professionals may self-select as specialized in conventional analytics only, the reality is that the groundwork, skills, discipline and initiatives that established their competency are directly applicable to machine learning and AI. And that applicability doesn’t just help them meet prerequisites; in fact, it gets those organizations a significant way toward the AI finish line. 

With advances in machine learning (ML) ease of use, breakthroughs like AutoML and integration of ML technology in more conventional analytics platforms, AI and ML are more accessible than ever to analytics professionals and even to intrepid business users. The truth is, you don’t have to be an AI expert to take advantage of AI because, in many cases, AI can fit into familiar analytics paradigms. 

Most analytics pros will not identify themselves as eligible to onboard machine learning expertise; the two are viewed not only as distinct but, to some, as mutually exclusive. The paradox that faces the industry, meanwhile, is that the AI/ML skill set sits quite adjacent to the conventional analytics competency and the two have quite a lot of overlap. For this reason, the discipline of building machine learning models, and certainly that of conducting predictive analytics by scoring data against existing models, is absolutely in the analytics professional’s wheelhouse. 

This paper will investigate and illuminate just how the conventional analytics and AI/ML disciplines align and connect. It will provide technical decision makers with approaches for enabling Machine Learning (ML) culture and proficiency in organizations that are mature in their use of conventional data & analytics but not yet fully invested in ML. The paper will also explain why the success of such additive adoption of ML cloud initiatives can be greatly accelerated by applying a staged maturity model with defined processes and gates. 

We will start by: 

  • Illuminating the adjacency of AI/ML for, and benefits of, ML for analytics-savvy organizations 
  • Describing key business problems and use cases that ML can help with 
  • Outlining why a cloud-native solution is perfect for ML implementations and how to get there 

For those who wish more detail, with an eye toward due diligence, we will go further, by: 

  • Identifying major ML personas, their needs, and the workflow for collaborating with analytics teams 
  • Discussing ways an organization can architect its ML solution in the cloud 
  • Defining Machine Learning Operations (MLOps) and why it’s imperative for mature organizations 
  • Detailing a multi-staged maturity model for analytics-mature organizations adopting a cloud-native ML solution 
  • Outlining the steps by which organizations can move through the maturity model 

We will finish up by: 

  • Discussing Responsible AI 
  • Providing coverage of the Azure Machine Learning (Azure ML) platform, describing its main features and capabilities 
  • Exploring two Azure ML customer use cases leveraging Azure ML and its capabilities to solve real-world problems 

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