Organisations are shifting towards AI-derived businesses to focus on two main perspectives — customer experience and company growth. According to a recent report, in 2021, artificial intelligence augmentation will create $2.9 trillion of business value and 6.2 billion hours of worker productivity globally. In this article, we will list down 7 machine learning tools in the market which are targeted at non-data scientists. 

1| BigML

BigML is a highly scalable, cloud-based machine learning service which can be seamlessly used for integration as well as implement data-driven decision making in the applications. The user of this service does not have to be an expert and it can be used by both data scientists and non-data scientists in an organisation. 

Last year, BigML introduced a new feature known as Organisations in their service to make it easy for companies to adopt Machine Learning across their entire corporate structure. Adding this new feature, the dashboard of BigML became a collaborative workspace where all the users in the organisation can access, work on, and visualise the same projects and resources. 

2| H2O Driverless AI

H2O DriverlessAI is an automatic machine learning platform which delivers unique and advanced functionality for data visualisation, feature engineering, model interpretability and low-latency deployment. There is no mandatory need of a data scientist to use this platform and can be used by everyone including junior data scientists, domain scientists, data engineers, etc. 

This platform works in such a way that the Driverless AI takes a raw dataset and automatically visualises interesting patterns for data exploration. It then applies automatic feature engineering to increase accuracy. Next, it auto-tunes model parameters and provides the user with the model that yields the best results. Lastly, it gives easy explanations of model results.

3| DataRPM

DataRPM is a cognitive data science platform on the cloud or on-premise for enterprises to build data-enabled products for enterprises. This platform uses machine first approach to connect to the customer’s internal data, fuses it with external data sources and runs a series of automatics machine learning algorithms which delivers actionable insights to answer the business challenges in an organisation.

4| DataRobot

DataRobot is an automated machine learning platform which enables a user to quickly and easily build highly accurate predictive models with full transparency. In this platform, coding and machine learning skills are optional, the only things important is data. In this platform, one can build and deploy highly accurate machine learning models in a fraction of time using traditional data science methods. In the development stage of DataRobot, it nearly automates 80% of the tasks of experienced data scientists

5| Google Cloud AutoML

Google Cloud AutoML is a suite of machine learning products which enables developers with limited machine learning expertise to train high-quality models specific to their business needs. This platform is built on machine learning algorithms such as transfer learning and neural architecture search technology. The AutoML products include AutoML Vision which derives insights from images, AutoML Natural Language which helps in gaining insights from text, AutoML Translation which detects and translate between different languages.

6| RapidMiner

RapidMiner is a data mining and data science platform which provides full transparency and governance for machine learning techniques to a non-data scientist. This platform provides a strong GUI, it also provides data mining and machine learning techniques such as data processing, data loading and transformation, data modelling, data visualisation, and much more. With RapidMiner Server, a user can share and re-use predictive models, automate processes as well as deploy models into production. 

7| Xpanse Analytics

Xpanse Analytics is the creator of Xpanse AI, an automated predictive analytics platform automates the entire predictive modelling workflow which reduces the time as well as cost in an enterprise. In the business perspective, Xpanse AI improves time to value for predictive modelling projects, understand the patterns driving customer behaviour, complex factors explained graphically, and much more.