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.

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SwissCognitiveHowever, 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. The list is in no particular order.

Create ML By 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

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

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

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

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.  […]

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