Let‘s briefly wind back the clock on the evolution of artificial intelligence…
In the early 2010‘s, machine learning advanced from niche academic discipline to one of the hottest buzzwords in tech. Data scientists intrigued by AI‘s immense potential faced immense barriers however. Sophisticated coding skills, expensive computing infrastructure, scarce training data and immature libraries made exploring machine learning exclusionary for all but the most determined.
A decade later, AI and ML now rank among the top strategic priorities for enterprises globally. Yet serious roadblocks remain for innovators and teams hoping to tap into the predictive power of machine learning. Just some of the bottlenecks:
- Growing talent war for increasingly scarce and expensive data scientists
-Months long development cycles plagued by complex programming dependencies
-Lack of transparency into how "black box" models actually function
-Difficulty integrating and scaling models across business systems
-Struggles operationalizing ML predictions responsibly
Fortunately, an exciting new category of technologies promises to smash these barriers down – low code and no code machine learning platforms.
Democratizing Machine Learning One Interface at a Time
Low code ML solutions aim to make the technology radically more accessible by minimizing or eliminating manual coding through interactive visual interfaces and automation. Meanwhile, no code ML products take this concept even further by allowing anyone – with zero coding skills – to develop and deploy performant models intuitively through simple drag-and-drop flows.
Beyond opening ML model building to non-programmers, low code and no code platforms also shine by improving collaboration between technical and non-technical teams. Instead of siloed hand offs between data scientists, engineers and business analysts, these tools allow seamless cross-functional iteration informed by diverse perspectives.
Additional benefits unlocked by low code and no code ML platforms include:
- Lower costs – Less reliance on expensive and scarce ML engineering talent
- Agility – Faster experimentation for proofs of concept and rapid iteration
- Productivity – 10x gains for development life cycles from prototype to production
- Transparency – No code interfaces increase visibility into how models function
- Portability – Export and integrate models flexibly across apps and systems
This guide will explore the top 8 low code and no code platforms allowing virtually anyone to start building and leveraging ML models today. We examine capabilities, ideal use cases and limitations for a spectrum of tools – from code-free drag and drop editors requiring no technical skills to developer-friendly libraries accelerating experimentation through automation.
Let‘s examine how these trailblazing platforms are revolutionizing access to the transformative capabilities of ML.
1. MakeML – Frictionless Mobile Machine Learning
MakeML lowers the barriers to entry for mobile developers of all skill levels to start training custom Core ML models. The open source tool lets you avoid intensive iOS model programming by leveraging an intuitive drag-and-drop interface even as a total beginner.
Consider MakeML ideal if you want to:
- Quickly build image classification, object detection or text prediction prototypes for mobile apps
- Experiment with creating ML models without coding or cloud dependencies
- Learn ML concepts hands-on with an engaging, visual interface
Key Capabilities
- Upload local datasets and assemble ML models visually through drag-and-drop
- Generate Swift and Objective-C code automatically to export finished models
- Train sophisticated models like detections and segmentations directly on device leveraging Apple hardware acceleration
MakeML accelerates the path from imagining an ML model to implementation for mobile products by drastically reducing programming prerequisites. While MakeML focuses specifically on Apple‘s ecosystem currently, the project pioneers frictionless ML innovation for developers and non-technical creators alike.
Pricing: Free and open source
2. Obviously AI – Code-Free ML with Instant Business Value
Obviously AI eliminates coding requirements through an intuitive drag-and-drop interface allowing anyone to upload datasets and generate production-ready predictive models visually.
Consider Obviously AI for:
- Empowering business analysts and experts to create ML models tailored to domain needs
- Accelerating pilot projects to surface actionable insights from company data
- Building and iterating niche ML apps without recruiting data science talent
Key Capabilities
- Upload data from CSVs/databases and configure ML experiments visually step-by-step
- Automatically selects optimal algorithms, processes data, trains dozens of models
- Deploy finished models via REST API, download packages or integrate live
- BYO Model – Upload existing models to compare performance
With intuitive experiences that abstract away coding complexity, non-technical users can leverage Obviously AI to unlock predictive insights from company data faster than ever.
Pricing: $250/mo starter plan
3. SuperAnnotate – Scalable Data Labeling, Augmented
SuperAnnotate focuses specifically on a crucial yet time-intensive component of the machine learning pipeline – data labeling and annotation. Their no code platform empowers teams to quickly annotate varied datasets critically needed to train ML models by hand, at scale.
Consider SuperAnnotate if aiming to:
- Annotate images, documents, audio and video frames
- Improve model accuracy by sourcing additional labeled training data
- Scale annotation teams while ensuring precision and governance
Key Capabilities
- Project management with progress dashboards, tasks, collaboration
- Multi-format annotation interfaces for text, images, 3D and video
- Active learning to recommend most useful data for labeling QA
- Rules engine to auto-annotate metadata and streamline workflows
For enterprise teams hungry for pristine labeled datasets to increase model precision, SuperAnnotate delivers transformation capabilities previously out of reach.
Pricing: Custom enterprise packages
4. Teachable Machine – Delightfully Accessible ML
Teachable Machine offers an uniquely engaging and delightful way to start experimenting with machine learning models through sound, pose and image classification. The web tool aims not to match enterprise functionality, but instead foster fun education.
Consider Teachable Machine if you want to:
- Introduce beginners to core ML concepts interactively
- Encourage students to explore modeling ideas without math/coding
- Spark creative experimentation with audio and visual data
Key Capabilities
- Record custom audio and image samples to create classifiers
- Leverage camera and mic with student privacy safeguards
- Export TensorFlow.js models for web applications
- Lightweight, launcher quickly even on slow connections
While not positioned as an enterprise solution, Teachable Machine delivers uniquely engaging and pedagogical ML explorations for schools and individuals alike completely free.
Pricing: Free
5. Apple Create ML – Integrated Mac ML
Create ML allows Mac, iOS and macOS developers to train custom Core ML models directly within Xcode without programming data science frameworks.
Consider Create ML ideal if aiming to:
- Prototype and iterate ML models on Mac leveraging Apple hardware
- Target mobile apps, websites and backend processes with iOS in mind
- Accelerate development cycles by eliminating cloud dependencies
Key Capabilities
- Guided workflows for configuring ML training experiments in Xcode GUI
- Model builder supports vision, NLP, audio, tabular algorithms
- Analyze model quality interactively through built-in visual reporting
- Export trained models seamlessly into Xcode projects
For Apple developers specifically, Create ML delivers frictionless integration tailor made to leverage Mac capabilities and iOS endpoints.
Pricing: Free (Requires Xcode)
6. PyCaret – Open Source Python AutoML
While the other featured platforms eliminate coding completely, PyCaret offers a hybrid approach – the Python library allows developers to construct end-to-end ML workflows rapidly by abstracting implementation complexity into reusable components.
Consider PyCaret if your goal is:
- Leverage automation and templates to accelerate Python model building
- Smooth experimentation process from EDA to deployment
- Get running start for developers new to ML coding projects
Key Capabilities
- Automated machine learning for instant model training, tuning and ensembling
- BYO Model integration to benchmark custom models
- MLOps enablement through Docker, CI/CD pipeline integration
- Templates and helpers to reduce Python configuration needs
While demanding some Python fluency, PyCaret unlocks orders of magnitude productivity gains through AutoML to streamline development.
Pricing: Free and open source
7. Lobe – No Code Custom Image Recognition
Lobe enables anyone to build performant computer vision ML models like image classification through an intuitive drag-and-drop interface requiring no prior coding or data science expertise.
Consider Lobe for:
- Empowering non-technical users to create custom visual classifiers
- Rapid prototyping of proof of concept computer vision apps
- Education on applied ML user flows for academics and students
Key Capabilities
- Upload images via local files or camera to label datasets visually
- Train models locally on Windows and Mac machines without cloud reliance
- Export neuro networks programmatically or operate predictions via API
- Community exchange for public models, images and anonymized stats
Lobe pioneers efficiencies which lower barrier to computer vision innovation for individuals and startups dramatically.
Pricing: Free tier for 1000 monthly predictions
8. MonkeyLearn – Cloud Hosted ML Text Analysis
While services like the aforementioned MakeML and Lobe focus on models running locally on devices, MonkeyLearn provides an alternative server-based approach – their managed cloud platform exposes scores of NLP classifiers via API allowing no code integration into web and mobile apps.
Consider MonkeyLearn for:
- Sentiment analysis, language detection, keyword extraction
- Scaling text processing needs without added infrastructure
- Access to state of the art NLP models without expertise
Key Capabilities
- Pre-trained and custom text analysis via cloud hosted API
- Build classifiers tailored to unique docs like contracts, support tickets etc
- Ongoing model optimization and governance handled by MonkeyLearn
- Embedded dashboards to visualize and filter text data trends
For using sophisticated NLP securely at scale without added complexity, MonkeyLearn delivers.
Pricing: Free up to 10K monthly predictions, paid tiers beyond
Choosing Your Low Code AI Adventure
The innovative platforms and tools highlighted in this guide make AI and ML remarkably more accessible to non-specialists. But with expanded opportunity comes potential for misapplication as well. As low code ML opens complex capabilities to novices, thoughtful governance is critical.
Key evaluation criteria for selecting a responsible platform match for your needs include:
- **Intended use case** – Does the tool actually support your business needs and data types?
- **User skill fit** – Do the interfaces and abstractions appropriately balance simplicity with depth?
- **Output flexibility** – Can you integrate and operationalize resulting models?
- **Privacy and ethical enablement** – How does the platform encourage transparency?
In closing, no code and low code machine learning platforms represent a breakthrough in computing – for the first time, transformative AI capabilities become truly accessible to non-programmers. These tools invite unprecedented creative experimentation by eliminating infrastructure and coding barriers.
The democratization of ML does not diminish the enduring need for engineering, mathematical and ethical rigour however. Rather, low code ML refocuses talent on higher judgement tasks like prioritizing valuable vs trivial use cases, ensuring representative data quality and championing responsible model ops.
Choose the right platform fitting your team‘s readiness, apply thoughtfully to high impact business challenges, feed judiciously chosen training data and let the predictive insights flow freely. The low code ML revolution awaits – who will you empower to innovate?
