Applications that take advantage of machine learning in novel ways are being developed thanks to the rise of Low-Code and No-Code AI tools and platforms. AI can be used to create web services and customer-facing apps to coordinate sales and marketing efforts better. Minimal coding expertise is all that’s needed to make use of Low-Code and No-Code solutions.
Artificial intelligence technologies that require little to no coding reflect a long-sought objective in computer science. No-code is a software design system that implements software without writing a single line of code. At the same time, low-code is a software development technique that promotes faster app deliveries with little to no coding required, and low-code platforms are software tools that allow the visual development of apps using a GUI interface. This AI tool requires no coding and may be used with a simple drag-and-drop interface—code-free or low-code development environments for AI applications.
Top low-code and no-code AI tools include the following:
Use MakeML to generate machine-learning models for object identification and segmentation without hand-coding. It simplifies the process of creating and efficiently managing a large dataset. In addition to preparing your ML models for action, you can also test them. MakeML is an online resource that can teach you all you need to know to build AI software and apply Computer Vision to an in-house problem in only a few hours. Video tutorials are also available on your mobile device to help you master Machine Learning. The skilled professionals at MakeML will assist you in developing a Computer Vision solution and incorporating it into your product. A single GPU cloud training and limited dataset import/export are provided at no cost.
With Obviously AI’s Machine Learning platform, you can make accurate predictions in minutes and don’t even need to know how to code. This entails creating machine learning algorithms and forecasting their results with a single mouse click. Use the data dialog to modify your dataset without additional code, then distribute or showcase your ML models across your organization. The low-code API allows anyone to use the algorithms to make predictions and incorporate those forecasts into their real-world applications. Furthermore, Obviously, AI gives you access to state-of-the-art algorithms and technologies without compromising efficiency. It can be used for revenue forecasting, supply chain planning, and targeted advertising. Lead conversion, dynamic pricing, loan payback, and other outcomes can all be forecast in real-time.
Create AI-Powered SuperData using SuperAnnotate. It’s an end-to-end system for AI-related tasks, including annotating, managing, and versioning “ground truth” data. With its extensive toolkit, top-tier annotation services, and solid data management system, your AI pipeline can be scaled and automated three to five times faster. High-throughput data annotation of video, text, and image to create high-quality datasets using industry-leading services and software. Project management tools and teamwork can help your model succeed in the field. Set up a streamlined annotation workflow, keep tabs on project quality, share updates with the team, and more—all with SuperAnnotate. It can speed up your annotation process because of its active learning and automation features.
Teachable Machine allows you to teach a computer to recognize and respond to your voice, gestures, and photos. Without the need to write any code, it facilitates the rapid creation of robust ML models for integration into applications, websites, and more. Teachable Machine is a web-based low-code machine learning platform that enables the development of widely usable machine learning models. You’ll need to collect and organize examples into relevant classes to teach a computer something new. You may put your computer through its paces as a learning machine and then immediately put it to the test. You can use the model in your online projects. You can also host the model online or distribute it as a downloadable file. And the best part is the model works completely locally on your device, so none of your audio or video has to leave the system at any point. Classifying photos and body orientations is a breeze with the help of files, a camera, and short audio samples.
Discover an innovative approach to teaching and training ML models on your Mac. It facilitates efficient ML model creation and Mac training using Apple’s Create ML. In a single project, you can train numerous models simultaneously, each with a unique dataset. It contains an external graphics processing unit to improve the speed of your models on your Mac. Take charge of your workout with options like pausing and resuming playback. The evaluation set will tell you how well your model performed. Examine pivotal KPIs and interconnections to spot a wide range of model-enhancing use cases, prospects, and investments in the future. Try out the model’s performance with a continuous preview using the camera on your iPhone. Train models more quickly on your Mac by using the hardware accelerators. Models can be of various kinds in Create ML. Model types include images, movies, music, speeches, texts, tables, etc. Afterward, you can train your computer with new information and settings.
You may automate your machine-learning workflows in Python with the help of PyCaret, a low-code machine-learning platform. With this basic, straightforward machine learning library, you may devote more effort to analysis, such as data pretreatment, model training, model explainability, MLOps, and exploratory data analysis, and less to writing code. PyCaret is built modularly so that different models can perform various machine-learning operations. Here, functions are the collections of processes that carry out jobs according to some procedure. Using PyCaret, virtually anyone can create complete, low-code machine-learning solutions. A Quick Start Guide, Blog, Videos, and Online Forums are all available for study. Create a basic ML app, train your model rapidly, and then instantly deploy it as a REST API after analyzing and refining it.
Use Lobe to teach your apps to recognize plants, read gestures, track reps, experience emotions, detect colors, and verify safety. It facilitates the training of ML models, provides accessible and free tools, and supplies everything required to develop such models. Provide examples of the behavior you would like to be learned by your application, and a machine-learning model will be trained automatically and ready to be released as soon as possible. This platform requires no coding experience and may be used by anyone. You can save money and time by skipping online storage and instead training locally on your PC. Lobe may be downloaded on both PCs and Macs. Furthermore, your model is cross-platform and ready for export or distribution. Your project’s ideal machine-learning architecture will be chosen automatically.
MonkeyLearn provides state-of-the-art Artificial Intelligence tools that will make cleaning, visualizing, and labeling client feedback a breeze. It is a data visualization and no-code text analysis studio that comprehensively analyzes your data. MonkeyLearn allows you to quickly and easily generate unique data visualizations and charts, allowing for more in-depth data exploration. You may also merge and filter these findings based on data inputs like date ranges and custom fields. In addition to using pre-made machine learning models, you can create your own with MonkeyLearn. Additionally, various pre-trained classifiers are available for use—emotion analysis, topic classifiers, entity extractors, and so on- and may all be constructed rapidly.
Akkio is a platform for artificial intelligence that doesn’t require users to write any code to build prediction models. It facilitates the easy creation of predictive models from user data for improved in-the-moment decision-making. Key business results, such as enhanced lead scoring, forecasting, text classification, and reduced churn, can be predicted with the help of Akkio’s use of existing data. It can also do advanced tasks for cleaning data, like merging columns, reshaping dates, and filtering out anomalies. Because of its intuitive interface, Akkio may be utilized by non-technical business users without the requirement for coding or machine learning knowledge. It may reduce time and increase output in various settings, from marketing and sales to finance and customer support.
Machine learning (ML) models can be created, trained, and deployed with the help of Amazon SageMaker, a cloud-based ML platform that offers a full suite of ML-related tools and services. SageMaker’s no-code and low-code tools streamline the machine learning (ML) model development and deployment processes for non-technical users and business analysts. Amazon SageMaker Canvas is a visual tool that facilitates ML model development and deployment without writing code. SageMaker Canvas’s intuitive drag-and-drop interface streamlines the processes of data selection, algorithm selection, and model training. SageMaker Canvas may then make predictions and put the trained model into production.
Data Robot is an artificial intelligence platform that streamlines the entire lifecycle of machine learning model development, deployment, and management. It’s a robust resource that serves many users, from data scientists and engineers to businesspeople. Data Robot’s flexible features make it a solid pick for those with little programming experience. Data Robot offers a visual, drag-and-drop interface for non-technical people to create and deploy machine learning models. This paves the way for business users with rudimentary technical skills to experiment with AI. Data Robot’s adaptable interface makes machine learning customization easier for non-programmers. Integration with external systems and the capability to create one’s programs fall under this category.
With Google’s AutoML, programmers and data scientists can create and release machine learning models without using hand-coded solutions. If you have little experience with machine learning, you can still use this platform to construct models because it requires little to no coding. Google AutoML provides a library of pre-trained models that may be used in various scenarios. These models are accurate because they are trained on large datasets. With Google AutoML, creating and deploying models is as straightforward as dragging and dropping components. It may be used without having to learn how to code. Google AutoML takes care of tuning your models’ hyperparameters automatically. Time and energy are both conserved by this method. You may check how well your models are doing with the help of Google’s AutoML tools. This aids in making sure your models are trustworthy and correct.
NanoNets is a machine learning API that allows developers to train a model with only a tenth of the data and no prior experience with machine learning. Upload your data, wait a few minutes, and you will have a model that can be queried via their simple cloud API. Extracting structured or semi-structured data from documents is made faster and more efficient by this AI platform. The OCR technology powered by artificial intelligence can read documents of any size or complexity. The document processing workflow can be streamlined using Nanonets’ AP Automation, Touchless Invoice Processing, Email Parsing, and ERP Integrations, among other services. In addition to PDF to Excel, CSV, JSON, XML, and Text conversion, Nanonets comes with various free OCR converters.
IBM Watson Studio is a service that provides a central hub from which anybody can create, release, and manage AI models in the cloud. It offers features and tools that make AI development accessible to people with little coding skills. Watson Studio’s no- or low-code features are a major selling point. It’s now possible to construct AI models without resorting to custom coding. Instead, you can utilize Watson Studio’s visual tools to assemble your project by dragging and dropping individual components into place. This paves the way for non-technical people, including business users, analysts, and researchers, to construct AI models. You can get up and running quickly with Watson Studio and its many pre-trained models. Uses for these models range from spotting fraudulent activity and client segmentation to predicting the need for repairs. After finishing an AI model in Watson Studio, you can send it into production. Watson Studio allows for both cloud-based and on-premises deployments and hybrid implementations that combine the two.
H2O Driverless AI is an AutoML platform streamlining the machine learning lifecycle, from preprocessing data to releasing models. This is a priceless tool for data scientists and business users since it allows them to build and deploy machine learning models without writing code. H2O Driverless AI uses several methods, including imputation, modification, and selection, to autonomously engineer features from your data. In machine learning, feature engineering is frequently the most time-consuming step, so this might be a huge time saver. Decision trees, random forests, support vector machines, and neural networks are some machine learning models that H2O Driverless AI can automatically construct and analyze. In addition, it optimizes your data by adjusting the hyperparameters of each model. With H2O Driverless AI, your models are instantly deployed to production, where they may be used in making predictions.
Domino Data Lab is a cloud-based service that facilitates creating, deploying, and managing machine learning models for data scientists, engineers, and analysts. It’s a low- or no-code artificial intelligence tool for designing and automating data science operations. Domino Code Assist is a tool that can build Python and R code for frequent data science projects. This can reduce the learning curve for non-technical users and the workload for data scientists. Domino Data Lab facilitates effective teamwork on data science initiatives. Users can collaborate on projects by sharing and analyzing code, data, and models. Data science projects are 100% reproducible in Domino Data Lab. This allows anyone to replicate a project’s outcomes without obtaining the original data or source code. Domino Data Lab has several tools that can be used to manage data science initiatives. Access control, code history, and auditing of the model’s efficacy are all part of this.
Organizations may automate their security operations, threat intelligence, and incident response with the help of CrowdStrike Falcon Fusion, a security orchestration, automation, and response (SOAR) architecture. It is based on the CrowdStrike Falcon® platform and is provided at no extra cost to CrowdStrike subscribers. Falcon Fusion is a low- to no-code tool, making it accessible to organizations of all sizes in the security industry. The software’s drag-and-drop interface simplifies the process of developing and automating workflows. Falcon Fusion also features a library of pre-built connections with various security solutions, allowing easy and rapid integration with an organization’s pre-existing infrastructure. Artificial intelligence (AI) is leveraged by Falcon Fusion to facilitate automation and better judgment. For instance, the program may analyze security telemetry data for patterns, assign priorities to incidents, and suggest courses of action using artificial intelligence. Consequently, security personnel are better able to deal with threats.
Data mining and machine learning models may be created and deployed quickly with RapidMiner, a comprehensive data science platform. Data preprocessing, feature engineering, model training, evaluation, and deployment are just some of its services. RapidMiner’s no/low code methodology is a major selling point. You may now create and release AI models without touching a single line of code. RapidMiner has a graphical user interface (GUI) lets you build your models by dragging and dropping various building blocks. This facilitates the entry of non-technical users into the field of artificial intelligence. RapidMiner has sophisticated scripting features, including a language dubbed RapidMiner R and its no/low code capabilities. You can use this language to modify your models and add new features to RapidMiner.
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Prathamesh Ingle is a Mechanical Engineer and works as a Data Analyst. He is also an AI practitioner and certified Data Scientist with an interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real-life applications