At the turn of this decade, there is a surge of no-code AI platforms. More and more businesses are looking to leverage the power of artificial intelligence to build smarter software-based products.
But execution becomes an obstacle for many. It’s a challenge for startups to find people with relevant machine learning expertise as the field is always a work in progress.
A lot of firms that invest fortunes in hiring engineers with PhDs and academic research background in machine learning fail to launch their products.
This brings no-code visual drag-and-drop tools to the picture which helps fill the data scientist’s void and makes artificial intelligence less intimidating for the non-technical people.
Businesses can now generate datasets, train, and deploy models with minimal to no coding knowledge in significantly less time while staying economical.
For mobile application developers, this certainly is a boon in disguise as on-device machine learning is in high demand right now. They don’t need to have a Ph.D. in machine learning and can be more creative with the data and models they wish to train.
In the next few sections, we’ll walk through some of the best no-code machine learning tools available right now. Some of these are totally free while others might charge you beyond their free trials. Nevertheless, each of them will help you to bring your AI application ideas to reality.
Being an iOS developer, I had to start with Apple’s no-code drag and drop tool, CreateML. After initially launching with Xcode, today CreateML is an independent macOS application that comes with a bunch of pre-trained model templates.
By using transfer learning lets you build your own custom models. From image classifiers to style transfers to natural language processing to recommendation systems it has almost every suite covered. All you need to do is pass the training and validation data in the required formats.
Moreover, you can fine-tune the metrics and set your own iteration count before starting the training. Create ML provides realtime results on the validation data for models such as style transfer. In the end, it’ll generate a CoreML model that you can test and deploy in your iOS applications.
While Apple is leading the way with Create ML, Google couldn’t afford to be left behind. There AutoML tool works much the same way as CreateML albeit on the cloud.
Google’s Cloud AutoML currently includes Vision(image classification), Natural Language, AutoML Translation, Video Intelligence, Tables in its suite of machine learning products.
This enables developers with limited machine learning expertise to train models specific to their use cases. AutoML on the cloud removes the need to know transfer learning or how to create a neural network by providing out of the box support for thoroughly tested deep learning models.
Once the model training is finished you can test and export the model in .pb ,.tflite , CoreML etc formats.
MakeML is a developer tool used for creating object detection and semantic segmentation models without code.
It provides a macOS app for iOS developers to create and manage datasets(such as performing object annotations in images). Interestingly, they also have a dataset-store with some free computer vision datasets to train a neural network in just a few clicks.
MakeML have shown their potential in sports-based applications wherein you could do ball tracking. Also, they have an end to end tutorials for training nail and potato segmentation models which should give any non-machine learning developer a good headstart.
Using their built-in annotation tool that works in videos you can build a hawkeye detector that’s used in cricket and tennis games.
Fritz AI is a growing machine learning platform that helps bridge the gap between mobile developers and data scientists.
iOS and Android developers can quickly train and deploy models or use their pre-trained SDK which provides out of the box support for style transfer, image segmentation, pose estimation like models.
Their Fritz AI Studio lets you quickly turn ideas into production-ready apps by providing data annotation tools and synthetic data to generate datasets in a seamless fashion.
Besides introducing support for Style Transfer before Apple, Fritz AI’s machine learning platform also provides solutions for model retraining, analytics, easy deployment, and protection from attackers.
Here’s another great machine learning platform designed specifically for creators and makers. It provides a delightful visual interface to quickly train models ranging from text and image generation(GANs) to motion capture, object detection, etc without the need to write or think in code.
RunwayML lets you browse a range of models ranging from super-resolution images to background removal and style transfer.
While exporting models from their application isn’t free of cost, a designer can always leverage the power of their pre-trained generative adversarial networks to synthesize new images from their prototypes.
Their Generative Engine that synthesizes images as you type sentences is one of the highlights. You can download their application on macOS, windows or use it on the browser directly(currently in beta).
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Obviously AI uses state of the art natural language processing to perform complex tasks on user-defined CSV data. The idea is to upload the dataset, pick the prediction column, and enter questions in natural language and evaluate results.
The platform trains the machine learning model by choosing the right algorithm for you. So, just with a few clicks, you can get a prediction report be it for forecast revenue or predicting the inventory demand. This is incredibly useful for small and medium-sized businesses looking to get a foot into the field of artificial intelligence without having an in-house data science team.
Obviously AI lets you integrate data from other sources as well such as MySQL, Salesforce, RedShift, etc. So, without knowing how linear regression and text classification you can leverage their platform to run predictive analysis on your data.
Beyond model training, data processing eats up a major chunk of time in developing machine learning projects. Cleaning and labeling data can certainly consume lots of hours especially when you’re dealing with thousands of images.
SuperAnnotate is an AI-powered annotation platform that uses machine learning capabilities(specifically transfer learning) to boost your data annotation process. By using their image and video annotation tools you can quickly annotate data with the help of built-in predictive models.
So, generating datasets for object detection, image segmentation will get a whole lot easier and faster. SuperAnnotate also handles duplicate data annotation which is common in video frames.
Last but not the least, we have another Google no-code machine learning platform. Unlike, AutoML which is a little developer-friendly, Teachable Machines let you quickly train models to recognize images, sounds, and poses right from your browser.
You can simply drag and drop files to teach your model or use the webcam to create a quick and dirty dataset of images or sounds. Teachable Machine uses the Tensorflow.js library in your browser and ensures that your training data stays on the device.
This is certainly a big step by Google for people who wanted to practice machine learning without any coding knowledge. The final model can be exported in Tensorflow.js or tflite formats which can then be used in your websites or app. You can also convert the model into different formats using Onyx.
Here’s a simple image classification model I managed to train in less than a minute.
We saw how no code machine learning platforms bridge the gap between data scientists and non-ML practitioners. While there’s no one size fits all solution, you can always pick a platform to build models or generate datasets at express speed.
Moreover, such tools make machine learning a lot more fun to work with. SnapML is another great no code machine learning tool that lets you train or upload your own custom models and use in Snap Lenses. This certainly helps indie developers and creators to put forth their creativity in front of millions of people.
That’s it for this one. Thanks for reading.