![]() Copy that model to your botframework SDK 4 project, add the nuget package of Microsoft.ML to your project along with the Carclass you wrote for your model (you can refer to the code at github repository for the reference). So, after you’re done with creation of the model, you can navigate to the bin folder of project where you will see your model packed in as. The approach I have chosen for the demo (or blog post) may not be ideal but the reason of my choice was just to show you that once the model is created, it can be exported anywhere and interestingly, with the ML.NET 0.3, you can actually do wonders with ONNX support. However, I will talk in detail about the integration part after the creation of model till its consumption in our bot. Since this blog post talks about the integration of your ML models with bot, therefore, I will not be going in too much of detail for ML.NET as you can find one of the best explanations of this framework, here. ![]() In order to achieve your goal, you will use ML.NET framework to build your model. By this technique, you would be able to get the predicted value of car evaluation in faster with no additional cost, in fact you will reduce the cost by empowering your staff to do something more productive. Traditionally, it is time and cost consuming therefore, you, being a smart chap, thought to automate this task using Machine Learning. Either way, he/she has to evaluate the car by considering several parameters such as buying price, maintenance cost, doors, safety and so on. In a traditional way, when a customer calls on your help-line, one of your staff either invites him/her in-person to your workshop or assists on call. ![]() One of them is to evaluate the car whether it’s acceptable, good or unacceptable for trading purpose. Let’s assume you’re an owner of AMA Auto Services where you provide multiple automobile services to your customers. This integration brings an achievement of business goals at a higher success rate. Do you want to recommend your latest products or services based upon your customer’s choice of shopping? Do you want to notify your customers with an estimated time of order delivery with respect to their location? Do you want to classify the complaints automatically as they’re reported?Īll of above is now possible by just an integration of your customized machine learning models with your bots. ![]()
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