Artificial intelligence

Eggs, baselines and the Darts library for time series


I have some chickens: 6 hens and 2 roosters. So I get a lot of eggs. I have written down how many eggs I get every day for some time now to see if there are any patterns in the production. In this article I will look at the value of a baseline for your AI problem as well as taking a quick look at the Darts library.

Follow the link below to read more:

Eggs, baselines and the Darts library for time series

Artificial intelligence

Building and deploying a serverless AI model


In this article I will demonstrate the workflow for creating an AI model from scratch and then deploy it serverless in the cloud. It will be slightly technical but the amount of code is quite small. I will show you all the steps required to go from an EDA (Exploratory Data Analysis) on Google Colab to serving a deployed AI model with AWS Lambda and Google Cloud Functions.

Follow the link below to read more:

Building and deploying a serverless AI model

Artificial intelligence

AI use cases for images and automation


In this article I will discuss AI solutions based on images, automation and the value of non-perfect models. I will include some important considerations for your AI projects, how to think and what to focus on both regarding effort and ethics. I will use a few different use cases to illustrate my points and hopefully this will get your inspirational juices flowing.

Follow the link below to read more:

AI use cases for images and automation

Artificial intelligence

Anomaly detection overview


Anomaly detection is the notion of automatically looking at data and finding outliers, i.e. data points that do not fit in well with the rest of the dataset. Finding an outlier is usually a sign that something exceptional has happened that should trigger further investigations or actions. Anomaly detection can be performed on existing historical datasets e.g. when you are pre-processing your AI training data. It can also be used on a real time data stream from your business transactions or sensor readings to almost instantly flag a data point as anomalous. This is a great example of AI automating things that a human can do and with a glance at a graph see a problem.
In this article Torbjörn will discuss anomaly detection from a few different perspectives and with this new tool under your belt you will start to look at the world with different eyes!

Follow the link below to read more about Torbjörn’s blog

Anomaly detection overview

Artificial intelligence

AI shows what an avocado armchair looks like


OpenAI is an AI research company. They are probably most famous for their language model GPT-2 that allegedly was “too dangerous to be shared”. They soon released an even better model, GPT-3. This latest model was then used as the basis for training an AI called DALL-E to generate images from textual descriptions.
Using text-image pairs as a training dataset this model is able to combine unrelated concepts into plausible images of things that never actually existed.

Follow the link below to read more about Torbjörn’s blog

AI shows what an avocado armchair looks like

Artificial intelligence

Time series classification use cases


A time series is an ordered sequence of numbers over time. For certain problems you want to look at a specific time series and from its characteristics be able to say something clever about it. Perhaps its origin or about some feature of the source.
This is a class of problems called time series classification that Torbjörn will discuss in this article, and when you start looking for them they pop up absolutely everywhere!

Follow the link below to read more about Torbjön’s blog

Time series classification use cases

Artificial intelligence

AWS Lookout for Vision goes GA


A few days ago AWS announced General Availability for their service Lookout for Vision. This is an AI service that provides image based anomaly detection in manufacturing using computer vision. With basically no need for AI knowledge this service can be quickly implemented in your manufacturing process to detect product anomalies and defects at a low cost.
Torbjörn will also describe a cool real-world use case for this service!

Follow the link below to read more about Torbjön’s blog

AWS Lookout for Vision goes GA

Artificial intelligence

Simple AI with Google BigQuery


In this post Torbjörn will discuss two different AI use cases built around the AI capabilities provided by Google BigQuery ML.
This is a quick and easy way to get started with your AI projects, especially if you are already using Google Cloud. The first use case is a time series forecasting problem where we want to predict some monthly tracked cost 12 months ahead. The second use case is a variation of the first one where we basically want to predict the same cost for just the next month but based on historical events.

Follow the link below to read more about Torbjön’s blog

Simple AI with Google BigQuery

Artificial intelligence

How can AI help you with Product recommendations


Product recommendations is something most people have seen. Some of the most known examples are Netflix movie recommendations and Amazon product recommendations. Entire companies have this as their core offering – helping other companies do it better. But with a little help from AI, doing this well has now become much easier than before. Many companies also have or are planning to incorporate this into their marketing or selling strategies. And for obvious reasons. Showing attractive additional products or packages is often a good way to increase sales.

Traditionally this has been a purely statistical or analytical operation: “on the web page for product X, recommend things that product X buyers also bought”. This is a nice and simple way to start if you have never done recommendations before. But if you wanted to apply AI to this problem – could you? The not so surprising answer is “you could and you should”! As always you want to collect lots of examples to train your AI including things like this:

  • Current product name (on the current web page)
  • Current product price
  • Visitor total time on website
  • Pages visited before current page
  • Geographical location of visitor
  • Total visitor purchase history

With this information the AI will be able to learn the best additional product suggestions for each main product.

Why do this

When your customers have found something that they want, why not help them to find valuable and useful extra products? This could be a great service for your customers that set you apart from your competition. Of course this is by no means an act of altruism – since you will sell more and make more money. So it is a win-win situation!

How do I get started

A good way to get started with this is to pick out some high-volume products. Because the volumes are high you can set up A/B-testing which means that for some customers you show recommendations and for the others you don’t. You now have a way to compare the impact of the recommendations. You also limit the effort required to collect data and train the AI, and you will only affect a part of your business (“small blast radius”). Due to the law of large numbers you can have higher confidence in the results than if your product sells only a small number of items, since as the number of purchases grows the observed effect gets closer and closer to the “truth”.

This could be a rather big project depending on your internal-IT and general IT-maturity. But if you just want to “get it out there” there is a shortcut.

The fast way to get started is just a few steps away:

  • Find the data
  • Train and deploy your model
  • Insert some code in your product webpage
  • Done.

This is such a standardized service that almost all major cloud providers offers this via API integrations, here are a few:

  • AWS Personalize
  • Google Recommendations AI

If you want to learn more about AI please visit: praktisk.ai