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Top 10 Tools For No-Code AI & ML

Top 10 Tools For No-Code AI & ML

Rohit Chatterjee
No-Code AI
W3Schools

Enterprises primarily rely on the two domains — artificial intelligence (AI) and machine learning (ML) in order to build and deploy various kinds of models for the smooth operation of their business. However, it requires programmers or data scientists with adequate knowledge of coding, which enterprises often lack. In a bid to ease such woes of the enterprises, tech giants are now open-sourcing their platforms and providing developer tools to ensure businesses can match the ongoing pace without the need for a coding expert. 

In this article, we list down ten such tools which can be used to develop models without being an expert in coding.

The list is in no particular order.



Create ML By Apple

via Apple

Create ML is an easy to use app that allows a user to deploy machine learning models without any knowledge about machine learning. The app enables the user to view model creation workflows in real-time and also permits to build models for object detection, activity and sound classification. One can train multiple models using different datasets simultaneously, and test the models before deploying them for further productions. The app is designed to operate without the need for a dedicated server. Create ML lets users train their models from Apple with a custom data and is capable of enhancing the performance by using an external graphics processing unit. 

Teachable Machine

Via Teachable

The Teachable Machine is a web-based tool which enables users to create machine learning models that are accessible to everyone easily. Users can feed the examples into different categories from where the computer can learn. Once the inputs are entered, they are categorised into image, audio, and pose models which can be tested instantly to check if the new examples are correctly classified or not. In this manner, one can teach the models to classify images, sound recordings and body postures. The app provides the liberty of using files as well as capturing live examples from the spot.

Accelerite ShareInsights by Amazon Web Services

Via Accelerite ShareInsights

ShareInsights boasts of being a powerful no-code tool which allows users to design ETL pipelines without programming. With the help of AWS services, the tool features a drag and drop console to create the pipeline. It enables anyone to use cloud-native technologies such as Glue and Arena for creating interactive dashboards within minutes. The tool also provides a data analytics platform for on S3 or Redshift along with an automated service selection and cost management for AWS serverless services. Last but not least, it also provides end-to-end data preparation, OLAP, and machine learning as a single integrated process.

What-If Tool

Via What-If

What-If tool has been designed to function in an easy way which can be used by anyone from product managers to students. The tool lets users compare two models simultaneously running on the same datasets by creating visualising features to compare the differences. A user can edit any of the data points by adding or removing features and ultimately running a test before putting it to production. What-If tool provides transparency in the similarity of data point to ensure the comparison is made right between the two models. Another highlighting feature of the tool is the use of confusion matrices and ROC curves to determine the precision of the models.

Google AI Platform

Via Google AI Platform

Cost-effective and quick to use, Google’s AI platform allows data scientists and engineers to turn their idea into reality with an integrated toolchain that helps to run a machine learning application. Google’s open-source platform Kubeflow supports the AI platform, which allows a user to design portable pipelines that can be run on Google Cloud or on-premises. To begin with, one has to store data on cloud storage or BigQuery and can label the data by classifying them into different categories such as images, videos, audios and text. Once done, the data can be imported to train a model. On to the next step, one can create the machine learning application on Google Cloud Platform (GCP) which handles various machine learning frameworks using deep learning VM image. Managing the models is an easy affair which can be done by using the AI platform in the GCP console.

Data Robot

Via Data Robot

Claims to deliver AI projects in minutes instead of months, Data Robot delivers predictive models without the need for a data scientist or a dedicated team of experts in the field. The tool gives access to various open-source machine learning models to get accurate models depending on the existing data in hand. It allows an organisation to enter the zone of forward-looking predictive models without the need of developing an exclusive set. Furthermore, it brings a balance between machine learning and human experience in the quest to solve predictive modelling problems. 

See Also
AWS data exchange

RapidMiner Studio

Via RapidMiner Studio

RapidMiner Studio forms data analytics through the use of images along with dragging and dropping features that will collect all the available data and run them through a library of 1500+ algorithms to determine the best possible model outcome. The tool quickly connects to databases, enterprise warehouse and social media, allowing the user to share the data with anyone who needs access. Once the model is ready, the tool explains the quality of the model, along with a clear understanding of what will work in favour of the business enterprise. In case a model is not sustainable, it can be tweaked with the pre-processing data using a single click of the mouse. Also, visual data science workflows are easy to explain to people inside and outside the organisation.

Microsoft Azure Automated Machine Learning

Via Microsoft Azure

Microsoft Azure is an enterprise-grade tool which is capable of deploying models at a remarkably faster pace. Using its no code UI, the tool automatically deploys predictive models with the help of existing data that are filtered through several algorithms and hyperparameters. The tool understands and automatically detects the inconsistencies and errors in the data and rectifies them, which saves time and results in inaccurate models. The detailed metrics visualisation enables a user to make a comparison between two models and the differences between their performance.

BigML

Via BigML

The highlighting aspect of BigML is the fact that it can be exported to any local server or can be deployed instantly as a part real-time application. With Partial Dependence Plots, the visual aesthetic of tools allows a user to have a clear and crisp understanding of predictive models. The tool provides immediate access to the users due to its easy-to-use web interface and REST API. Further, the tool is available in single as well as multi-user version due to which it can be accessed on the cloud or on-premises. 

Google ML Kit

Via Google ML Kit

For the ones who aspire to create application on-the-go, Google ML Kit is the software development kit that is available on iOS and Android operating system. With little or no knowledge of machine learning, one can smartly add the needed functions to an application with a few words of coding. On the other side, the ML Kit provides APIs that allows a user to use custom TensorFlow Lite models on the smartphone app. For mobile usage, the kit comes with several features such as text recognition, face detection, landmark identification and barcode scanning.

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