With all the talk about new AI-powered tools and programs feeding the imagination of the internet, we often forget that data scientists don’t always have to do everything 100% themselves. Often, some other tools and platforms cut out the middleman and allow you to create some intriguing programs and AI-powered applications. So let’s take a look at a couple of low-code and no-code platforms that could supercharge your next project, and get you out of the weeds of hand coding.
PyCaret: Let’s start off with a low-code open-source machine learning library in Python. PyCaret allows data professionals to build and deploy machine learning models easily and efficiently. What makes this the low-code library of choice is the range of functionaries that include data preparation, model training, and evaluation. Other functionalities that are also popular with PyCaret are model-deployment and hyperparameter tuning. All of this is unified under a single interface. This doesn’t even touch on the fact that there are built-in visualization tools that help those creating their models to understand both data and results more efficiently.
So why is this library so popular? Well, one of its main advantages is that PyCaret reduces the amount of code required to build a machine learning model. This frees up the data scientists to work on other aspects of their projects that might require a bit more attention. Another benefit is that it’s a library that is useful for non-experts. Without a deep understanding of underlying algorithms and techniques, novices can dip their toes in the waters of machine learning because PyCaret takes care of much of the heavy lifting for them. This in turns lowers the skill entry point enabling more non-traditional data scientists who are interested in machine learning models to get a start.
H2O AutoML: A powerful tool for automating much of the more tedious and time-consuming aspects of machine learning, H2O AutoML provides the user(s) with a set of algorithms and tools to automate the entirety of the machine learning workflow. This means everything from data preparation to model deployment. The point of this low-code tool is to give data scientists an easy way to build highly accurate machine learning models without the need for significant manual effort.
Like PyCaret, some aspects are automated such as feature engineering, hyperparameter tuning, and model selection. One significant advantage of H2O AutoML is its ability to handle large data sets with relative ease and its ability to scale horizontally across multiple machines, making it a perfect fit for projects working with big data. Finally, H2O AutoML has the ability to support a wide range of machine learning tasks such as regression, time-series forecasting, anomaly detection, and classification.
Auto-ViML: Like PyCaret, Auto-ViML is an open-source machine learning library in Python. It’s designed to simplify the process of building machine learning models while being built on top of scikit-learn, Pandas, and NumPy libraries. One benefit is an easy-to-use interface that allows users to build up highly accurate models quickly. Due to this, Auto-ViML is effective at automating many of the tedious and time-consuming tasks involved in machine learning, allowing users to build highly accurate models quickly and easily.
Like other libraries, some of the main advantages of Auto-ViML are its ease of use and lowering the necessary coding for entry into model building. This is due to it requiring minimal coding and configuration. This makes Auto-ViML an ideal tool for beginners and experts alike. Additionally, it supports a wide range of machine learning tasks, including regression, classification, and time-series forecasting.
TPOT (Tree-based Pipeline Optimization Tool): Like PyCaret and H2O AutoML, TPOT is an open-source automated machine learning library in Python. TPOT works by generating a large number of potential pipelines using a tree-based representation of the pipeline space. From there, it evolves those pipelines over a number of generations using programming techniques to optimize them against a specific problem.
Like other low-code tools, TPOT has an easy-to-use focus, providing users with a simple and intuitive interface that takes much of the tedious and time-consuming work out of machine learning while still quickly proving highly accurate models.
AutoKeras: Another Python library, AutoKeras is an open-source tool that automates many tasks involved in building neural networks. This includes the model section, architecture search, and hyperparameter tuning. The way AutoKeras works is by using a technique called neural architecture search or NAS. This automates the search for the best neural network for any given problem.
One interesting detail of AutoKeras is its ability to handle both structured and unstructured data. It can automatically handle the preprocessing of data, which includes encoding, imputation, and data normalization which makes it easier for users to build models with different types of data.
Google Cloud Auto ML: The first no-code tool we’ll look at is Google Cloud Auto ML. This is a suite of machine learning-focused tools that allow users to build custom machine learning models without the recruitment of significant programming languages. Its main benefit is its ability to build custom machine learning models quickly in a simple-to-use interface that can handle scalability with large amounts of data and a wide range of machine learning tasks.
Also included with Google Could AutoML is its host of visualization tools that give users the ability to see patterns and gain key insights from the visualizations. Finally, Google Cloud AutoML can handle a wide range of machine learning tasks, including image classification, text classification, and language translation. It also supports a wide range of data formats, making it easier for users to work with different types of data.
Google ML Kit: Another no-code solution from google, Google ML Kit is a mobile machine learning software development kit that allows developers to build up custom machine learning models for mobile applications. Part of its benefits is that it provides the user with a set of pre-built machine learning models that can be used out-of-the-box while also providing tools that allow for the creation of custom models for specific use cases.
An interesting feature of Google ML Kit is its ability to run models directly on a device without the need for a network connection. This is a partially beneficial feature as it can provide a reliable machine learning creation platform without the need for network connectivity. Like other tools, Google ML Kit also provides key visualization tools to allow developers to interpret and understand the results of their machine learning models, which is key to finding and fixing any issues that might keep their models from running optimally.
Runway AI: A no-code tool that has artists and designers in mind, Runway AI is a platform specifically for creative spaces so that they may experiment with machine learning and AI. Its interface is a simple drag-and-drop that allows users to jump right into creating through machine learning and AI without the need for any significant programming knowledge.
Like other no-code tools, Runway AI provides a wide range of pre-built machine learning models that can be used out-of-the-box, as well as tools for building custom models for specific use cases. With that said, it allows for the creation of multiple media types such as images, audio, and 3D models which could, in the future, reduce the costs of production and creation.
Lobe: This is a no-code deep learning platform tool that allows users to build up custom machine learning models. Like other no-code tools, there isn’t a need for a great deal of programming knowledge and it also provides a rich interface that allows users to experiment with different deep learning architectures and techniques, while allowing the possibility of interaction into workflows.
Like Runway AI above, Lobe has a drag-and-drop interface that lets users be hands-on with how they experiment with different deep learning models. There are also a variety of pre-built deep learning models that can be used to further reduce the time between planning and deployment. One interesting benefit of Lobe is that there is a strong community behind the platform where new users can find information, guidance, and opportunities for collaboration.
CreateML: If you’re in search of a custom machine learning model with applications for image and text recognition, then the Apple-developed CreateML might be up your alley. This powerful no-code machine learning platform is well-suited for iOS developers and provides a simple and intuitive user interface that has seamless integration with other Apple products and services.
What sets CreateML apart, other than its iOS-suited capabilities is its ability to use transfer learning. This is a technique that allows users to take pre-trained models and fine-tune them for their specific use cases. This can reduce the amount of data required to train a model significantly while also making it easier to create custom models.
RapidMiner: This is a low-code and no-code platform that allows for the creation of both data analytics workflows and machine learning models. There are a large array of tools for data exploration, data preparation, model training, and deployment, which are all held together by a user-friendly drag-and-drop interface. But what sets RapidMiner apart from some of the other tools is its sheer versatility.
It has no problems supporting a wide range of data types, and formats, and it doesn’t have issues with either structured or unstructured data. Like other tools, RapidMiner provides several key visualization and tools that allow users to give vital insights during their projects. Finally, another benefit is the community around RapidMiner where users can share knowledge and expertise in forums filled with tutorials, collaborations, and other resources.
DataRobot: If you’re in search of a flexible and powerful enterprise no-code/low-code machine learning platform, then DataRobot has what you’re searching for. Not only does the platform provide users with a wide range of automated machine learning tools, but there are also scalable deployment options and key visualization tools that allow for the export of information so the results of models can be easily shared.
As mentioned, DataRobot’s key benefits are both its automation capabilities that allow users to streamline their workflow, reducing the time and effort needed to build models, and its scalability. The platform is able to handle large volumes of data and has the ability to be deployed on-premises or in the cloud. This is partially useful for those aiming toward enterprise-level applications.
Finally, its visualization and export tools give users the ability to identify and fix issues within their models while ensuring that there is room to optimize performance for specific use cases.
To fully utilize the power of these tools, there still needs to be a data science foundation. And the best part is there isn’t a better way to develop the foundation you need to get the most out of any low-code or no-code tools than the bootcamps at ODSC East and ODSC Europe. In-person or virtually, learn from some of the leading experts in data science as they help you develop job-ready skills.