AutoML Creates Scalability

“By 2025, 50% of data scientist activities will be automated by AI, easing the acute talent shortage.” 

– Gartner, How Augmented Machine Learning Is Democratizing Data Science; Jim Hare, Carlie Idoine, Peter Krensky, 29 August 2019 (Report available to Gartner subscribers)

With the automation of complex and previously manual processes, now any stakeholder is able to take part in data analytics efforts. Tools like Dataiku have made it easier for non-trained professionals in data analytics to help in the data pipeline. This means your enterprise will benefit from more efficient data analytics by requiring less professionals to do simple tasks. 

Early on, AutoML was almost exclusively used for the automatic selection of the best-performing algorithms for a given task and for tuning the hyperparameters of said algorithms.  Its development has spurred the application of automation to the whole data-to-insights pipeline, from cleaning the data to tuning algorithms through feature selection and feature creation, even operationalization. 

AutoML's rapid acceleration has facilitated the adoption of automation across the entire data pipeline, from data cleansing to selecting and building features and optimizing algorithms to bring them to running in production environments. At this scale, coupled with increasing amounts of data, AutoML generates more information in less time. These developments help enterprises take their AI efforts to the next level. More features of these systems explained further. 

Benefits of AutoML Tools

Anyone outside the data analytics team will be able to help with the deployment of machine learning models using the right AutoML tools. The concern is, what can a non-technical user actually bring to machine learning models and data analytics efforts? Previously, there has been a disconnect between business savvy users and technical analytics teams. Technical users needed to translate business rules into meaningful machine learning models. As always, things were lost in translation. This is a bottleneck for most enterprises looking to scale their AI and analytics efforts. 

With the introduction of AutoML (using machine learning to automate machine learning) the process is streamlined. Now everyone that can contribute to data analytics in an enterprise, can. Tools like Dataiku make use of the core pillars of DataOps, which is collaboration. With the additional benefit of automating the complex tasks, a business analyst can tweak model parameters to their level of understanding. Later, a technical analyst can enhance the parameters for a working model.  

In the case of Dataiku’s AutoML, the interface can be reduced to a single button option, simply called “Train”. This feature automatically auto-configures all required parameters for a successful model. It’s that easy. For customizing the parameters manually, a technical user can later use the same interface and tweak any necessary value. Dataiku’s features can also be tried for free and you can then decide if this is all that’s necessary for your enterprise’s data efforts. Don’t get me wrong, you can write the most technical code in Jupyter Notebooks if you want inside of Dataiku. However, even our most experienced data scientists find AutoML extremely convenient.

To summarize, stakeholders and data analysts can collaborate with data scientists by doing less advanced work with AutoML, including data preparation, and leaving it to data scientists to refine and fine-tune projects before pushing them to production.

What To Look For In AutoML Tools

Your stakeholders will rely not only on machine learning tools but also on automated and comprehensive tools. With these tools, all system users can work together in developing models without worrying about breaking anything. Additionally, technical knowledge should not be required if the user does not need it.

With this considered, there are several functionalities that are necessary for a full end-to-end AutoML system.

  • Transparency: It is difficult to trust something that is not understood. Hence, the best tool is one that gives an accurate description of the algorithms used, explains why they were chosen, and gives users the knowledge necessary for data scientists to trust the results and decide if they are suitable for the project. This is one of the most important features of a modern AutoML system.
  • Ease of use: The system should be easy to use by non-developers with minimal technical skills. Look for a system that supports augmented analytics by providing contextual help and explanations for various parts of computing and a visual, code-free user interface. 
  • Repeatable: Users without a deep understanding of data warehousing technologies should be able to run augmented analytics with a system that can be reliably used from one step in the data pipeline to another. 
  • Adaptability: Since data projects are often edited by several people or by several roles, the selected tool should have adaptability options. For example, the results must be translatable into Python code for complete learning.

All of these functionalities are available in professional systems like Dataiku.

For more information on how to leverage Dataiku’s ease of use and how it can be integrated with your already working systems, please follow our other articles or contact us here at Excelion.