I want to develop a chatbot like application which gives response to input questions using Google Cloud Platform.
Naturally, Dialogflow is suited for this such applications. But due to business conditions, I cannot use Dialogflow.
An alternative could be AutoML Natural Language, where I do not need much machine learning expertise.
AutoML Natural Language requires documents which are labelled. These documents can be used for training a model.
My example document:
What is cost of Swiss tour?
Estimate of Switzerland tour?
I would use a label such as Switzerland_Cost for this document.
Now, in my application I would have a mapping between Labels and Responses.
During Prediction, when I give an input question to the trained model, I would get a predicted label. I can then use this label to return the mapped response.
Is there a better approach to my scenario?
I'm from Automl team. This seems like a good approach to me. People use Automl NL for intent detection, which is pretty aligned with what you try to do here.
Related
So I have been collecting data of numerous text-descriptions about articles, where as each description was structred differently. Now, I would have to "create" an algorithm, which sorts out the title of that article for me what is a hard task. I have come around Google ML natural language and it seems to be able to create one for me.
Unfortunately, I am not really able to exactly find out how I can use it,
so my question is... How precisely can I set it up ? And additionally, it would be helpful to know if firebase has such a service, since I am planning to build a firebase project.
Thanks in advance for any help !
Unfortunately models created using Google AutoML Natural Language are not exportable to Tensorflow lite (mobile models). Based from your use case you will need a model for text classification, the provided link has a sample of how this model work. You can follow this tutorial to train a custom model using the data that you have so it can identify if a title of a article is a hard task or not.
Once training is done you can now:
Deploy it in Firebase
Download the model in your device and perform testing.
You can find detailed instructions from training the model to testing it on your device for either iOS or android.
We are automating the process of our deep learning project. Images are automatically uploaded to a dataset in AutoML Vision (Object detection) in the Google Cloud Platform. We have a couple of team members who regularly annotate the uploaded images by using the provided Annotation Tool in Web UI. We need to measure the productivity of our team members by counting the annotations they make for each of them. I haven't found an efficient solution yet. I would appreciate it if you could share your ideas.
There is not a feature to identify who annotated which images; however, the approach I can think of is that you can split the work between your team members and distribute the labels that each one should annotate. Then you can simply count the number annotations for each label. For instance, in from this guide you can give Baked Goods and Cheese to one collaborator and Salad and Seafood to another one, and so on, so that you can check the totals in the UI. Even, the label statistics can give you more details of annotations for each label (hence for each team member), note that statistics are only available in AutoML Vision Object Detection UI.
An automated approach, in case you are interested in, is Human Labeling Service; according to documentation, currently, it is only available by email because of the Coronavirus (COVID-19) measures
If recommendations above don't fit your needs, you could always file a Feature Request for asking the desired functionality and add the required details.
I'm using AutoML Video Intelligence and it's very tedious and I was wondering if there was an easier way to create Datasets for the object tracking. An easy way to get the time and position of the box?
I'm pretty sure that you can find the answers on the mentioned questions reading GCP knowledge base documentation in particular about AutoML Video Intelligence product.
At least Object tracking process is nicely explained in terms of implementation with either GCP console UI or constructing HTTP calls to Cloud REST AutoML API.
Furthermore, you can find example tutoring the way how to handle video segments positioning for the relevant prediction requests.
You can adjust initial question, extending it with a certain details about your use case in order to preciously address the solution.
I want to use google cloud vision API in my android app to detect whether the uploaded picture is mainly food or not. the problem is that the response JSON is rather big and confusing. it says a lot about the picture but doesn't say what the whole picture is of (food or something like that). I contacted the support team but didn't get an answer.
What you really want is a custom classification, not specifically raw Cloud Vision annotation.
Either use the https://cloud.google.com/automl/ or invent an own wheel like I did: https://stackoverflow.com/a/55880316/322020
Im trying to implement an anomaly detection machine learning solution on GCP but finding it hard to find a specific solution using Google Cloud ML as with AWS' Random Cut Forest solution in Kinesis. Im streaming IoT temperature sensor data for water heaters.
Anyone know a tensorflow/google solution for this as my company only uses google stack?
Ive tried using sklearn models but none of them are implementable on producton for streaming data so have to use tensorflow but am novice. Any suggestions on a good flow to get this done?
I would suggest using Esper complex event processing engine if primary concern is the analysis of data stream and catching patterns in real time. It provides SQL like event processing language which runs as continuous query on floating data. Esper offers abstractions for correlation, aggregation and pattern detection. It is open source project and license is required if you want to run engine on multiple servers to achieve high availability.