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As artificial intelligence (AI) and machine learning (ML) reshape industries and redefine possibilities, mastering the art of AI and ML model development and evaluation has never been more crucial. Behind every powerful AI or ML model lies strategic decisions, intricate algorithms and critical evaluations. Within the Model and Evaluate phases of the Columbus AI Innovation Lab, we delve into these aspects and how to use diverse metrics to evaluate a machine learning model’s performance, along with its strengths and weaknesses.
Medius AP Automation is a best-of-breed solution for automated processing of vendor invoices. If you work in Finance, you know that invoice processing does not happen in isolation. There’s a lot of master data in your ERP involved in the process. That’s why it is critical that your Dynamics 365 ERP and Medius are tightly connected. Once this connection is set up the fun begins. This is when you’ll configure all the automation capabilities of Medius to enjoy a fully touchless invoice process. But with endless automation opportunities it might be hard to know where to start. We sat down with Anna Moore, one of Columbus’ experienced business advisors for Dynamics 365 and Medius, to get the expert tips. Before we dive into the juicy tips and hands-on advice from Anna, it’s worth mentioning some of the basics to help set the scene. Dynamics 365 is the ERP (Enterprise Resource Planning) system, where master data is hosted and maintained. This includes procurement information such as vendor records, purchase orders, price lists etc. Medius Accounts Payable Automation is where the supplier invoice processing happens including data capture, coding, matching invoices to purchase orders and approval, often in a highly automated workflow. When the integration between D365 and Medius has been set up master data is automatically synchronized between the two systems. As a result, purchase order (PO) data from D365 can be used by Medius to match invoice information in an automated way. And this is just one example of how Medius enables efficiency gains in accounts payable. While Medius enables high automation levels right from the start thanks to default best practice workflows, there’s often a lot more to do after go-live, says Anna. Many of my customers just don’t know about all the good functionality that exists in Medius and they struggle to find the time for research in their busy workday. Continue reading to get Anna’s five hands-on tips on how to improve your invoice processing in D365 and Medius, or click on the topic that is most relevant to you: Validate vendor bank details to identify potential fraud Set up VAT indicators in Medius to remove manual work Create accruals quickly and easily Automate acquisition journals for fixed assets Leverage authorization groups for automated approval flows and improved audit trail
It doesn’t matter how good your artificial intelligence technology is. If you have bad data, you’ll get bad results. While AI is one of the most innovative and impactful innovations for businesses today, bad data can prevent AI projects from getting off the ground. Having high-quality data and following data preparation best practices are crucial for a successful AI project. In the Transform phase of the Columbus AI Innovation Lab, the one we detail here, data is collected and prepared for use by AI and machine learning (ML) algorithms. Major phases in Machine Learning Machine learning, frequently used synonymously with “artificial intelligence,” is about using predictive methods to simplify tasks. There are several essential steps to building ML models, and these steps can change based on the issue you are trying to solve and the kind of ML model you are building. However, in general, the steps involved in creating and implementing an ML model are as follows: Define: Different approaches for finding AI use cases Discover: Scope and prioritize ideas before developing an AI solution and implementation strategy Transform: Transforming the business case including data acquisition and preparation with AI/ML (current blog) Model and evaluate: Analyze and enhance the business use cases using AI/ML algorithms Deploy and support: Deployment of AI/ML models and support/monitoring to guarantee quality and effectiveness

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