Dive Deep into 8 Awesome Low Code and No Code ML Platforms

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Hey there! Excited to dive into the world of no code machine learning? I‘m thrilled to be your guide.

In this post, we‘ll explore 8 phenomenal platforms that make ML accessible to everyone – not just PhD data scientists! I‘ll share plenty of insights from my experience as an AI practitioner.

Machine learning is transforming every industry. But many companies hit roadblocks in adoption due to the complexity of traditional ML workflows.

That‘s where low code and no code ML platforms come in! They allow anyone to train and deploy models visually, without coding.

I couldn‘t be more excited about this development. Democratizing AI will unlock innovation and creativity at scale.

Ok, enough preamble – let‘s dig in! Here‘s an overview of the key benefits of no code ML:

The Power of Low Code and No Code ML

  • 10x faster model development – Visual tools let you build in days/weeks instead of months/years
  • Zero ML expertise required – Intuitive UIs are accessible to non-coders
  • Rapid experimentation – Easy to try multiple approaches and learn quickly
  • Privacy and control – Models can be trained locally, no data leaves your device
  • Cost efficiency – No need for expensive data scientists and infrastructure
  • Consistency and compliance – Code-free process improves auditability
  • Augment human intelligence – Let the computer do the repetitive work, so humans can focus on creativity

Research by Gartner shows over 65% of all app development will use low code and no code platforms by 2024. All signs point to this being the future.

Next, let‘s explore some leading platforms driving this no code ML revolution!

MakeML – No Code ML for Mobile Apps

MakeML is the perfect no code solution for iOS developers. You can build, train, and export ML models entirely on your Mac – no cloud required.

I really like that you can integrate the models directly into iOS apps using Apple‘s Core ML framework. MakeML was acquired by Apple and integrates tightly with Xcode.

Some examples of models you can build:

  • Image classification – identify objects, landmarks, text, etc.
  • Audio classification – detect sounds like claps or musical notes
  • Text classification – analyze sentiment, topics, spam, etc.

MakeML uses transfer learning to speed up training. You can leverage pretrained models like MobileNet, SqueezeNet, and ResNet.

According to a 2021 survey by SlashData, over 20 million iOS developers are now active worldwide. MakeML allows them to easily augment apps with the power of ML.

MakeML model training

Obviously AI – Automated ML for Non-Experts

Obviously AI is a fantastic choice if you want automated ML without writing any code whatsoever.

It provides a slick interface where you can upload your dataset and let Obviously AI handle the rest. Some of the key features:

  • Automated data cleaning and preprocessing
  • Feature engineering to extract useful data representations
  • Automated ML model selection – it trains hundreds of models to find the best performer!
  • Tools to understand which factors drive model predictions
  • Customizable deployment – API, apps, etc.

For a clear commercial benefit, Obviously AI says it can increase predictive accuracy by up to 40% compared to hand-coded models. Those are impressive returns for a no code platform!

Obviously AI allows you to build everything from customer churn predictions to dynamic pricing models and sales forecasts. It‘s industry-agnostic – I‘ve seen it applied successfully across retail, healthcare, manufacturing, and more.

Obviously AI dashboard

Lobe – Building Custom Image Recognition Models

Lobe is a fantastic tool for creating custom AI vision models.

It streamlines the entire process – data collection, model building, training, and export. Everything is done through their simple desktop app interface.

Some examples of what you can build with Lobe:

  • Classifier to detect manufacturing defects
  • Scanner to read barcode values
  • Counter to track objects or people
  • Detector for ripe produce
  • Analyzer of sports video to track athletes and ball position

Lobe automatically selects the right neural network architecture for your use case – no ML expertise needed. The app is available for both Windows and Mac.

Once trained, Lobe models can run locally on devices like iPads and iPhones for privacy and speed. No need to route images through the cloud.

Over 50,000 people have already used Lobe to ship vision AI into their apps and products. It‘s a fantastic on-ramp to applied computer vision.

Lobe model training interface

Teachable Machine – Build Browser-Based ML Models

If you want a quick, hands-on ML experiment, try out Teachable Machine.

It lets you train basic image and audio classifiers right inside your web browser, with zero coding required.

The workflow is simple:

  1. Capture examples using your device microphone and webcam

  2. Annotate the examples and group into classes

  3. Train a model directly in the browser

  4. Export the model to use in web apps and sites

You could build things like:

  • Gesture recognition
  • Speech command classifier
  • Noise detection
  • Picture subject identifier

Models won‘t be production-ready, but Teachable Machine is awesome for learning how ML actually works. It‘s a no code sandbox for ML tinkering!

Over 1 million models have been trained on Teachable Machine to date. People are having fun building AI with this no code tool.

Teachable Machine model training demo

MonkeyLearn – ML for Text Analysis

MonkeyLearn provides a suite of no code AI models for extracting insights from text.

You can analyze customer support tickets, survey responses, social media posts, reviews, documents, and more. It‘s a blackbox AI tool applied to text data.

Some common use cases:

  • Classify support tickets by topic
  • Analyze customer sentiment from feedback
  • Extract keywords and entities from documents
  • Clean and structure messy text data

MonkeyLearn offers hundreds of ready-made classifiers and extractors for your text analysis needs. You can also train custom models tailored to your unique data.

Pricing is subscription-based, starting at $49/month. MonkeyLearn provides pay-as-you-go access to enterprise-grade NLP.

For text heavy applications, MonkeyLearn is a phenomenal no code ML solution.

MonkeyLearn analysis

Apple Create ML – ML for Apple Developers

Apple Create ML enables iOS developers to integrate ML models into their apps without coding.

You set up and train models directly inside Xcode with a few clicks. Then export them for use in Swift or Objective-C apps.

Create ML supports building models like:

  • Image classifiers
  • Object detectors
  • Text predictors
  • Audio event detectors
  • Forecasters
  • Recommenders

And more advanced techniques like transfer learning, data augmentation, hyperparameter tuning, and distributed training.

Over 27 million registered iOS developers can now easily augment their apps with ML capabilities. This unlocks new utility and engagement for mobile users.

Apple Create ML model training

PyCaret – Low Code ML Library for Python

If you‘re comfortable with some coding, PyCaret is a fantastic low code ML library for Python.

It simplifies the end-to-end ML process – prep, train, tune, evaluate, explain, and deploy models – with just a few lines of code.

The simple and unified API makes your model development pipelines 10x more efficient.

PyCaret can build models for:

  • Binary classification
  • Multi-class classification
  • Regression
  • Clustering
  • Anomaly detection
  • Natural language processing
  • And more…

It integrates seamlessly with popular Python data science tools like NumPy, Pandas, Scikit-Learn, XGBoost, and Jupyter Notebook.

Over 1.5 million data professionals use Python daily, according to Anaconda. PyCaret brings ML to them in a simple package.

PyCaret Jupyter Notebook example

SuperAnnotate – Building Datasets and Models

SuperAnnotate provides an end-to-end platform for creating image and video training data and models.

It supports annotating objects, regions, cuboids, polygons, lanes, scene categories, optical flow, and more. Advanced tools like auto-annotation accelerate the process.

Once your dataset is ready, you can train custom models on SuperAnnotate‘s GPU cloud infrastructure. They also provide benchmarking and deployment support.

This makes SuperAnnotate a full-service solution tailored to computer vision use cases like:

  • Autonomous vehicles
  • Robotics
  • Industrial automation
  • Medical imaging
  • Satellite imagery
  • And more…

Their team has 50+ years of collective expertise across semantic segmentation, object detection, video analytics, and more. Impressive client list includes Toyota, Airbus, and Walmart.

For custom computer vision applications, SuperAnnotate is worth strong consideration.

SuperAnnotate annotate demo

Make Data-Driven Decisions with No Code ML

I hope you enjoyed this tour of no code ML platforms! We covered a diverse set of options.

It‘s incredible seeing how no code democratizes data science and AI. Empowering more people to ask questions and let data guide decisions.

If you‘re considering ML for your business or apps, I highly recommend starting with a no code platform. Visually build, iterate, and learn.

Once you validate the ML use case and ROI, then consider custom-coded production deployment.

No code ML lowers the barriers to experimentation and innovation with AI. It expands possibilities and helps uncover new opportunities.

Imagine an idea and make it real – quickly – without years of specialized skills. That future is coming faster than many realize.

Now over to you. Give one of these platforms a spin! Build something, learn how the models work, and keep pushing the boundary of what‘s possible.

The no code ML revolution has arrived. Time to be a part of it!

Let me know if you have any other questions.

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