Top 10 Tools Used for Automated Machine Learning

1. Google Cloud AutoML

Google Cloud AutoML is a part of the Google Cloud platform that automates the machine learning workflow steps. We can create our custom ML model and train them by giving the dataset and can predict the output. We can also integrate these models with our web pages or applications. This makes the life of data scientists much easier by automating most of the complex tasks of the ML pipeline. It has many components for different concerns like AutoML for natural language processing and AutoML vision for image processing. This tool is paid and follows pay-as-you-go pricing. Different products will have different pricing. They will charge you only for the resources you need.

1.1 Advantages

AutoML models are highly optimized and save time.
You can integrate these models with other Google Cloud products like Big Query, Google Data Studio, etc.
It is highly scalable and can handle large datasets. So When the business grows, it can easily handle a large amount of traffic.

2. H2O.ai

H2O.ai is a Generative AI and machine learning software. Its mission is to make AI accessible to all. Automated machine learning models generally give the highest predictive accuracy than manually trained models. Driverless AI is their most famous tool. It has both free and commercial versions. Its open-source version can be used by small businesses for completely free. For commercial use, H2O.ai fine-tuned its open-source model to give control and customization to enterprises according to their need. The price of commercial solutions depends on the project requirement such as the number of users, computing power, etc.

2.1 Advantages

It contains an explainability tool that explains why a model is making certain predictions.
It can support different programming languages like Java and Python.
We can scale H2O.ai to handle the increase in the amount of datasets.
It also provides real-time data processing with its real-time scoring feature.

3. DataRobot

DataRobot is an AI platform, that helps businesses and enterprises accelerate their growth by providing predictive models. They can analyze their business performance and find insights using historical data. It is generally used to automate the most time-consuming and complex task of machine learning workflow. This platform helps data professionals understand the hidden patterns in the data that may be difficult for a human brain to comprehend. DataRobot is a paid tool with an annual subscription. The price of tools depends on the business requirements and features used.

3.1 Advantages

It has automated feature discovery which finds the best features that contribute most to the output from the dataset provided
It has real-time prediction features which can be used for real-time computation of large datasets.
It has Cloud AI which can be used to deploy ML models without worrying about the infrastructure.
It provides model management after deployment which ensures that the model keeps on working correctly when there is a change in the amount and speed of data

4. Azure Machine Learning

Azure machine learning is a Microsoft product that provides enterprise-grade generative AI development services for the end-to-end machine learning lifecycle. We can quickly build ML models using AutoML capability to automate tasks. It provides the feature of automating workflow with continuous integration and continuous development. It has machine learning operations(MLOps) that streamline the development and deployment of ML models. It applies an automated featurization technique for data exploration and preprocessing to build accurate models. Machine learning engineers and specialists can use it to train & deploy models and operate MLOps. It is a paid tool and It follows pay-as-you-go pricing. Charges are applied only to the computation resources used during model training.

4.1 Advantages

Its drag-and-drop functionality makes the platform user friendly
There is no limit on data importing from Azure storage.
There is no need for complex software and big hardware. We can use ML as a service and pay for what we are using.
It is also compatible with many open-source frameworks like TensorFlow and pytorch.

5. Amazon Sagemaker

Amazon Sagemaker is an ML service to build, train, and deploy models. Data professionals can use this platform for a production-level environment. This platform allows us to deploy the ML model with just a few clicks. Some of its solutions are fraud detection, personalized recommendations, extracting and analyzing data, and more. These solutions are customizable for our business needs. It can fine-tune more than 150+ open-source models such as NLP. This platform is paid and follows a pay-as-you-go service. We have to pay only for the resources we use. Whenever a new user signs up, it provides a 2-month free service as part of its free tier program. There are two choices for payment: On-Demand pricing and the Sagemaker saving plans. We can choose one according to our requirements.

5.1 Advantages

It is a fully managed service so there is no need to worry about running a machine learning platform.
It supports many machine learning frameworks like PyTorch and also provides the user with the ability to create their algorithms
We can easily integrate it with other AWS services like Amazon S3, Amazon EC2, etc.
It has one-click training and deployment functionality which saves us from doing manual configuration and setup steps.
It automatically scales the resources with an increase in data or business needs.

6. BigML

BigML is a machine learning platform that anyone can use to create ML or deep learning models in just 3-4 clicks. We can use your data to find the underlying trends or hidden patterns that can help our business grow. These models can be easily integrated into our applications or software. BigML automates the machine learning process which removes the complexity of training it manually so that you can focus only on decision-making. The platform also has Rest API that can be used in any programming language like Java, Ruby, Python, etc. BigML’s model can be used locally, or remotely or can be embedded into applications to make predictions. It has subscription-based pricing which varies according to the data size you use and the number of parallel tasks. It has different subscription plans, minimum starting from $30 per month which gives a maximum data size of 64MB per task. It also provides a 14-day free trial with all the functionality.

6.1 Advantages

All the models have interactive visualization and explainability features which makes them easy to understand and interpret.
All the models are exportable via JSON PML and can be used in web, mobile, or IoT applications or services like Google Sheets, Zapier, and more.
BigML is a transparent and collaborative platform. You can share your machine learning resources with anyone in the organization.
It uses HTTPS for all connections and keeps the user’s data safe and secure.
It also provides two private deployment options for organizations that have strict data regulatory requirements. You can either choose your preferred cloud provider and ISP or your commodity infrastructure.

7. Altair RapidMiner

RapidMiner is an end-to-end data science platform that provides the facility of building data and machine learning pipelines with code-free and Drag & drop UI functionality. It can be used by people with little to no knowledge of ML to experienced data scientists and engineers. RapidMiner’s solutions can be easily trained, evaluated, explained, and deployed using the latest technologies. It processes the real-time data to find underlying trends, and spot anomalies and outliers in just a few seconds which can be shared across organizations through powerful dashboards. These data visualization dashboards can be used by organizations which required to make fully informed and fast decisions based on huge amounts of rapidly changing data or real-time data. Some of the products of Altair RapidMiner are Altair AI Studio, Altair Graph Studio, Altair Iot Studio, and more. RapidMiner has both free tier and paid plans. Its paid plan starts from $10 per month. We can also get it customized according to our business needs and pay accordingly.

7.1 Advantages

It has an interactive visualizer that can be used by the user to visualize and analyze the result of the prediction.
It can be easily connected with different databases and cloud services.
It can be integrated with different languages like Python and R which makes it a versatile tool.
It has a collaboration feature that can be used to share a project and collaborate among team members.

8. IBM AutoAI

IBM AutoAI is a graphical tool that uses different algorithms, transformations, and parameter settings to create the best predictive model. It is a part of IBM Watson Studio. IBM AutoAI extends the power of AutoML and applies intelligent automation to the machine learning predictive model pipeline. It includes steps of preparing a dataset for training, finding the features that contribute most to the output, looking for the best model for a given dataset, and testing a variety of hyperparameter tuning options to find the best result and then ranking different model pipelines for you to choose the best one. You can use IBM AutoAI to build a machine-learning model with sophisticated features and deploy it on the cloud or use it in your applications. IBM Studio is a paid tool and it offers two payment options. First is IBM Cloud pak for data which has multiple licensing options. Second is IBM Cloud pak for data as a service which follows pay-as-you-go pricing and is fully managed on IBM public cloud.

8.1 Advantages

It tests potential models against a small subset of data and ranks them accordingly. Then it selects the best model for your dataset.
We can visually build models with an intuitive GUI-based flow.
It supports different open-source frameworks like pytorch, tensorflow, and sci-kit learn.
This platform supports programming languages like Python, R, and Scala. It also works well with Jupiter Notebooks, JupyterLabs, and CLI

9. TPOT

TPOT stands for Tree-based Pipeline Optimization Tool. It is a Python library that automates the process of a machine-learning pipeline. It follows a decision tree-based structure to represent a pipeline model. This library uses generic programming and finds the best ML pipeline by exploring thousands of possible pipelines. Once it is done searching, it will return the Python code for the best pipeline for your data. It is an open-source library so we can use it by importing it in our code file. It always tries to maximize the accuracy of models.

9.1 Advantages

It automates most parts of the model creation process without sacrificing quality.
It simplifies the process of choosing the right model for our dataset.
It saves a lot of time and optimizes the result.

10. TransmogrifAI

TranmogrifAI is an open-source AutoML library for heterogeneous structured data built by Salesforce. It is written in Scala and runs on top of ApacheSpark. It automates the flow of producing AI models with just a few lines of code which saves a ton of time for data scientists. It encapsulates five main components of machine learning development services i.e. feature interference, transmogrification (feature engineering), automated feature validation, automated model selection, and hyperparameter optimization. In feature interference, TransmogrifAI let’s user define the schema type so that all data have correct data types and no null values. Transmogrification transforms the data into numeric representation so that the machine learning model can interpret them all on the same basis. Automatic feature validation removes the features that have little to no predictive power means they don’t contribute to the output prediction. Automated model selection runs different algorithms and uses validation errors to select the best model for your data. Hyperparameter optimization tunes the hyperparameters automatically. It is an open-source library and can be used in the code by anyone to produce machine-learning models in less time.

10.1 Advantages

Instead of using an automated transformer, user can define their transformer and estimator to be used in the machine learning pipeline.
In TransmogrifAi, features are strongly typed and it does type-checking on the entire machine-learning pipeline so that errors can be caught early.
It has both batch settings as well as streaming settings with the help of Apache Spark

Conclusion

There are many tools available in the market to automate machine learning workflow. These tools help you save time and energy by doing the most complex tasks and providing you with the output. Deciding which tool is best for you depends on your project and budget. If you want to make a small or personal project and don’t need many resources then you can use a library like TPOT or TransmogrifAI. If you want to make some industry-level projects that can be built and deployed fast and require professional tools then you can use some paid tools like BigML, Google Cloud AutoML, etc.