Augmented Analytics in 2020: Democratization of Analytics

 AI capabilities such as machine learning, natural language processing and computer vision and to some degree other technologies like AR and VR are poised to augment analytics activities like preparing data and identifying insights. Augmented analytics will enable companies to run more efficient and effective analytics departments and internalize data-driven decision making and enable employees to become citizen data scientists.

What is augmented analytics?

Cognitive or AI-driven or augmented analytics all mean modern analytics: analytics that leverages the latest advances in AI algorithms such as deep learning. When you google these terms, you may find slightly different explanations  Read more

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Data labeling/ annotation/ classification in 2020: In-depth Guide

Since 2010s, companies have been heavily investing in machine learning. Supervised learning is the most common form of machine learning today. Supervised learning algorithms need to be fed with labeled instances. This increases the importance of data labeling solutions.

Therefore, data labeling tools (open source vs proprietary), service providers and alternatives to data labeling are important aspects of a company’s data labeling strategy:

What is data labeling?

Supervised machine learning algorithms learn from labeled data, data that has been tagged with labels. Programmers do not explicitly program machine learning algorithms on how to make decisions, they program the models that learn from labeled data. Read more

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22 AutoML Case Studies / Examples: In-Depth Guide [2020]

Though there is a lot of buzz around autoML, we haven’t found a good compilation of case studies. So we built our comprehensive list of automated machine learning case studies so you can see how autoML could be used in your function/industry.

This AutoML case study list will help us to understand what AutoML is and how you can use it in your business function. The most common application areas of autoML are decision-making and forecasting. Read on to discover how AutoML can support your business function. Read more

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AutoML Software / Tools in 2020: In-depth Guide

We explained autoML in detail. Now it is time to figure out the right software for auto ML for your business.

3 Types of AutoML Solution Providers

Open Source

AI is one of the few scientific areas were despite significant corporate investment, even secretive tech giants like Apple publish their research findings. Therefore it should not be surprising that there are competitive open source autoML tools.

All open source tools we came across, need an active development environment in Python or R and require the user to write at least a few lines of code to initiate the automated machine learning process. Read more

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AutoML: In depth Guide to Automated Machine Learning [2020]

Automated machine learning has the potential to greatly increase the productivity of data scientist and democratize machine learning tools. It can be a powerful solution to the well documented scarcity of data scientists.

What is automated machine learning?

According to Wikipedia:

Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems.

Automated ML solutions aim to automate some or all steps of the machine learning process which includes:

  • Data pre-processing
  • Feature engineering
  • Feature extraction
  • Feature selection
  • Algorithm selection & hyperparameter optimization

Since accuracy of machine learning solutions can be measured, automated systems can fine-tune data, features, algorithms and hyperparameters of algorithms to generate accurate models relying on established machine learning knowledge and trial&error. Read more

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