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Building APIs is not just about technology. APIs enable many new business opportunities, but only if done correctly. Enter API Management platforms to provide the building blocks behind a successful API program. As a result of lucrative opportunities, many Software vendors have emerged or pivoted from their SOA management roots to provide API Management capabilities. In this session, Kent will introduce you to Microsoft’s Azure API Management platform by providing an overview that highlights its capabilities and the opportunities that emerge for organizations. As part of this presentation, Kent will demonstrate how developers can create their first API and discuss strategies for transforming existing services to leverage Azure API Management. This presentation will consist of general guidance on API Management, an Azure API Management portal walk-through and demos that re-enforce the concepts that were introduced.

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As enterprises grow, so too does the number and variety of data sources they use to drive business. The average company is using at least sixteen SaaS applications and has data in at least that many on-premises data stores and internal apps. With such disparate data, each tied to a unique API, integrating, managing, and maintaining integrations for all of a company’s data creates a whole new set of challenges. Thankfully, solutions exist that enable enterprises to rely on data to drive business without causing undue strain. In this session, we’ll explore and compare the different options for solving the data integration problem and explain why you should be using standards-based drivers to abstract your API integrations.

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Amid the increasingly competitive brewing industry, the ability of retailers and brewers to provide optimal product assortments for their consumers has become a key goal for business stakeholders. Consumer trends, regional heterogeneities and massive product portfolios combine to scale the complexity of assortment selection. At AB InBev, we approach this selection problem through a two-step method rooted in statistical learning techniques. First, regression models and collaborative filtering are used to predict product demand in partnering retailers. The second step involves robust optimization techniques to recommend a set of products that enhance business-specified performance indicators, including retailer revenue and product market share. With the ultimate goal of scaling our approach to over 100k brick-and-mortar retailers across the United States and online platforms, we have implemented our algorithms in custom-built Python libraries using Apache Spark. We package and deploy production versions of Python wheels to a hosted repository for installation to production infrastructure. To orchestrate the execution of these processes at scale, we use a combination of the Databricks API, Azure App Configuration, Azure Functions, Azure Event Grid and some custom-built utilities to deploy the production wheels to on-demand and interactive Databricks clusters. From there, we monitor execution with Azure Application Insights and log evaluation metrics to Databricks Delta tables on ADLS. To create a full-fledged product and deliver value to customers, we built a custom web application using React and GraphQL which allows users to request assortment recommendations in a self-service, ad-hoc fashion.

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We recently had a #Webinar on Embracing Rest API through APPSeCONNECT. The key points covered were: 1. What is REST and why it is important? 2. Authentication mechanisms. 3. Adding Schemas and Actions 4. Connecting the dots through Workflows. 5. Defining Integration Strategies. 6. Provitioning and Maintenance. 7. Conclusion #Webinar #RESTAPI #API #iPaaS Check out the Webinar Recap now! Integrate your line of business applications: https://www.appseconnect.com/integrations/

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