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Category: Data Science

All posts about Data Science.

4 Tips for development of Alexa Skills

In the last weeks, I have developed some Alexa Skills for different purposes. It is really cool to develop the skills with the Alexa developer console. Building and testing the dialogue model is fairly easy. But at some points, you may encounter some problems like me. Therefore, I would like to share some tips with you to improve the user experience of a skill significantly.

Tip 1: Use Default Slot Types

Let’s start with a simple topic. If possible, use the provided slot types from Amazon, like Amazon.FOOD or AMAZON.NUMBER. These Slots have a huge set of data in the background. They are already optimized for a good NLP understanding. Doing this on your own is a lot of work and fine-tuning the model. Save yourself many hours and use what Amazon provides you.

Tip 2: Use a proxy for local development

There are different ways to implement the logic for the service: AWS Lambda or (self-hosted) endpoint services. If you develop endpoints services, you need to redirect the requests from the Alexa skill to the development instance, usually running on the local machine. An important thing is, the service needs to provide a valid TLS certificate. The easiest way to get it running is a web-proxy system like ngrok. Ngrok routes requests via a public web URL to your local development instance. And the best thing is, it has an option to provide a valid Wildcard-TLS-Endpoint which will be accepted by Alexa. This saves you a heck of time to set up anything equivalent with DynDNS and creating certificates. ngrok - a good tool for developing Alexa Skills

Tip 3: Answer not only use-case questions

During the development of Alexa skills, you work a lot through the questions (utterances) you have in mind with regard to the use case. But, think about your users. They can just interact with your app by asking questions. They can not click through a mobile app or website to search and find things they need. It’s important to be prepared for simple and general questions such as:

  • “What are the opening hours?”
  • “What is the address of a store?”
  • “What is the maximum of items I can order?”

Think about how your customer will ask questions. Ask your friends to try the skill and listen to their natural type of questions and commands. You can also log questions in the FallbackIntent to find out what real people say.

Tip 4: Test Alexa Skill dialogue with many people

This tip continues the thoughts of the previous. Many people will formulate questions and commands differently. Since the skill is usually used by many people, you need to be prepared for different types of utterances. Add as many sample utterances as you can to improve the user experience for the skill.

These 4 tips will improve the user experience of your Alexa skill. Do you have any further tips? Let me know in the comments.


6 Tipps zu IoT Analytics mit der CumulocityIoT Plattform

IoT AnalyticsEigentlich hätte ich gestern auf der buildingIoT Konferenz meinen Talk zu “IoT Analytics – Stream und Batch-Processing” gehalten. Nun ja, es sollte nicht sein. Daher habe ich meine Takeaways hier zusammengefasst.

In IoT Use Cases werden oft Daten verarbeitet. Ab einer gewissen Menge an Daten gibt es einen nicht mehr zu erfüllenden Zielkonflikt zwischen Real-Time-Anforderungen und der Genauigkeit. Dieser lässt sich durch die Lambda-Architektur auflösen und in zwei Layern getrennt erfüllen. In SaaS Plattformen, wie der CumulocityIoT, stehen dazu oft Mittel wie Complex Event Processing (CEP) Engines und REST-Schnittstellen zur Verfügung. Im Falle der CumulocityIoT Plattform läuft die Stream Verarbeitung über die CEP Engine Apama. Es gibt jedoch ein paar Dinge für eine stabile und effektive Verarbeitung zu beachten. Daher hier meine 6 Tipps zu IoT Analytics.

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Review of the Predictive Analytics World Business Conference

Estrel Hotel Berlin
Event Location of Predictive Analytics World Business

The last two days I was at the Predictive Analytics World Business Conference in Berlin. The event happened inside the Estrel Hotel, a nice and good managed location. In the talks of day one, little was in for me. The deep dive tracks were too deep for me. The use case tracks too superficial. At least it looks like presenting companies are using AI/ML in production. This is in contrast to the Industrial Data Science Days in Dortmund earlier this year, where Companies are using AI/ML in scientific PoCs, far from production.

At day two, the talks were much more interesting. My personal highlight was the talk (with the very long title) “Data Science Development Lifecycle – Everyone Talks About It, Nobody Really Knows How to Do It and Everyone Thinks Everyone Else Is Doing It” by Christian Lindenlaub und René Traue. They summarized their learnings from using Scrum and other methods in Machine Learning projects. They showed how to combine different agile methodologies to run successful machine learning + production software projects. Very inspiring for our own projects too.

The following talk “How to Integrate Machine Learning into Serverless Workflows” delivered also some helpful insights for some of Tarent’s current projects.

In the end, a good conference with some points I took home. See you next year? I don’t know yet. We will see.

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The AirQuality Lab: How To Work With IoT Sensors

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