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Top 10 best tools to use AI without knowing how to code

Top 10 best tools to use AI without knowing how to code

Do you want to use Machine Learning and AI in your company or for a project, but you do not have the expertise of a Data Scientist or an engineer? Check out the top 10 best Ai tools for building machine learning and artificial intelligence applications without computer code. Artificial intelligence and machine learning are

Do you want to use Machine Learning and AI in your company or for a project, but you do not have the expertise of a Data Scientist or an engineer? Check out the top 10 best Ai tools for building machine learning and artificial intelligence applications without computer code.

Artificial intelligence and machine learning are opening up new possibilities for companies in all industries. However, these technologies require the expertise of Data Scientists and other specialists with programming knowledge.

However, on a global scale, there is a real shortage of such experts. Fortunately, to facilitate the adoption of AI in business, several tech giants offer open-source platforms and tools to exploit this innovation without expertise in computer code. Here is a selection of ten such Ai tools.

1. Teachable Machine

The TTeachable Machine (Ai tools)eachable Machine (Best Ai Tools) is a web-based Ai tool. From a simple browser, the user can create machine learning models accessible to everyone.

It suffices to feed the computer with examples to allow it to learn. These examples can be files or live captures. Once downloaded, the examples are categorized into images or audio.

The models can then be tested immediately to see if the new samples were correctly classified. Thus, it is possible to teach models to classify images, sounds, or even body postures.

2. What-If Tool

What-If Tool

The What-If Tool is distinguished above all by its ease of use. Users can easily run two models on the same dataset to compare the differences through visualization features.

The data points can be edited at will by adding or removing features. Finally, it is possible to perform a test before putting the model into production.

Another strong point of this tool is the use of confusion matrices and ROC curves to determine the accuracy of models.

3. Google AI Platform

The Google AI Platform is Google’s artificial intelligence platform. It stands out for its affordable price and ease of use.

With this solution, Data Scientists can easily bring their ideas to life through an integrated toolchain to run a machine learning application.

This platform is compatible with Kubeflow, Google’s open-source platform. This allows the user to design portable pipelines that can be run on Google Cloud or on-premise.

The data is first stored in the Cloud or on BigQuery, then labeled to classify it in different categories: images, videos, audio, text, etc.

The data can then be imported to train a model. A machine learning application can then be created on the Google Cloud Platform (GCP). This supports different machine learning frameworks using a virtual machine deep learning image. The AI ​​platform and the GCP Console make it easy to manage models.

4. Data Robot

With Data Robots, you can build AI projects in minutes instead of months. This solution allows you to exploit predictive models without needing the expertise of a Data Scientist.

The tool provides access to different open-source machine learning models. You will thus be able to obtain models whose accuracy will depend on the data available.

Thus, a company is able to take advantage of predictive models without needing to develop its own set of proprietary models. In addition, this platform offers a balance between machine learning and human experience to solve predictive modeling problems.

5. RapidMiner Studio

The RapidMiner Studio offers you a very intuitive “drag and drop” interface. After collecting all the available data, the platform will choose from a collection of over 1,500 algorithms to determine the best model.

The company’s databases, data warehouses, and social networks are quickly connected to allow the user to share the data with anyone who needs it.

When the model is ready, the tool explains why it is the best choice and what benefits it will bring to the business. In addition, visualizations make it easy to explain the workflow to anyone inside or outside the company.

6. Accelerite ShareInsights by Amazon Web Services

Powered by AWS, ShareInsights is a tool for designing ETL (extract, transform, load) pipelines of data without the need for computer code.

A drag and drop interface makes it easy to create the pipeline by relying on the various Amazon cloud services. Cloud-native technologies like Glue and Arena can be leveraged to create interactive dashboards in minutes.

A data analysis platform for S3 and Redshift is also offered, as well as automated service selection and hit management features for AWS serverless services. There is also data preparation, OLAP, and Machine Learning functions.

7. Create ML By Apple

Create ML is a very easy to use application. It allows the user to deploy Machine Learning models, without the need for technical machine learning knowledge.

The user can view the model building workflow in real-time. He can also develop his own models for object detection, activity, or sound classification.

Many models can be trained using different sets of data simultaneously. Models can then be tested before being deployed. The application is designed to work without a dedicated server. It is possible to improve performance by using an external GPU.

8. Microsoft Azure Automated Machine Learning

The Microsoft Azure Cloud Automated Machine Learning Tool enables organizations to deploy machine learning models much faster.

Its user interface does not require coding, and the tool automatically deploys predictive models using existing data filtered by algorithms and hyperparameters.

Inconsistencies and errors in the data are automatically detected and rectified. This saves time and avoids erroneous results. Thanks to a detailed visualization, it is possible to make a comparison between the two models and their respective performances.

9. BigML

The main advantage of BigML is that it can be exported to any local server or can be instantly deployed as a real-time application.

Thanks to the “Partial Dependence Plots”, users benefit from a clear and intuitive interface allowing them to fully understand the predictive models.

Its easy-to-use web interface and REST API provide immediate access to users. In addition, the tool is available in a multi-user version, and in a Cloud or on-site version.

10. Google ML Kit

It is possible to create machine learning applications on mobile. Google ML Kit is a software development kit available on iOS and Android operating systems for smartphones and tablets.

In a word, This tool makes it easy to add machine learning functions to an application, without the need for machine learning or programming expertise.

Among the features offered are, for example, text recognition, face detection, landscape identification, or barcode scanning. APIs also allow the use of TensorFlow Lite models on the smartphone application.

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