DataRobot competitors are gaining popularity and offering a competitive edge to Data Robot
DataRobot is a popular AI company as an AI cloud leader for delivering a unified platform for all data types — gaining tough DataRobot competitors. It needs to collaborate with all users through augmented intelligence, data analysis, data engineering, decision intelligence, and many more with enterprise-grade stability and scalability. DataRobot alternatives are becoming highly successful by leveraging data analysis and AI. The market share of data analytics is expected to hit US$74.99 billion in 2028 at a CAGR of 25.7%. Let’s explore the top DataRobot alternatives to look out for in 2022 to drive meaningful outcomes
Top ten DataRobot competitors in 2022
H2O.ai
H2O.ai is one of the top DataRobot competitors to boost AI results by solving complex business problems and discovering new ideas. The comprehensive automated machine learning capabilities of how AI is created and consumed. AI and data analysis are transforming multiple industries such as financial services, healthcare, marketing, manufacturing, retail, telecom, and insurance.
RapidMiner
RapidMiner boosts the pace of transformation from data into meaningful insights as to the enterprise-ready data science platform. The DataRobot alternative supports the full data analysis lifecycle. It offers data engineering, model building, model Ops, AI app building, and many more.
Alteryx
Alteryx is one of the top DataRobot competitors to solve problems by transforming data into meaningful insights with AI and data analysis. It offers the Alteryx Analytics Automation Platform for delivering end-to-end automation of analytics, and data analysis to boost the digital transformation.
Google AutoML
Google AutoML is gaining popularity in the tech market for training high-quality custom machine learning models. It allows developers with machine learning expertise to train high-quality models for business needs. The data analysis through AutoML Tabular helps to automatically build and deploy the state-of-the-art machine learning models on structured data.
Dataiku DSS
Dataiku DSS is one of the top DataRobot competitors to succeed in the world’s rapidly evolving data ecosystem for data analysis. It can leverage data science, AI, machine learning, and many more for effective data analysis. This DataRobot alternative helps in data preparation, data analysis, data visualization, data modelling, and many more.
SAS Predictive Analytics
SAS Predictive Analytics is a popular DataRobot competitor focused on offering a variety of predictive analytics solutions. It is designed to meet the needs of all types of users ranging from data scientists, business analysts, and many more. The SAS solutions can be easily configured for any line of business including discovering meaningful insights, analyzing data, monitoring predictions, and growing a portfolio.
Apache Spark
Apache Spark is one of the popular DataRobot alternatives with a unified engine for large-scale data analysis. It is a multi-engine for executing data science, AI, machine learning, and data engineering on multiple single-node machines. It helps to unify the data processing with real-time streaming.
IBM Watson
IBM Watson is one of the top DataRobot competitors for enabling data privacy, compliance, and security in multiple industries. It offers a diverse ecosystem to drive the responsible use of AI. It helps to optimize decisions on IBM CloudPak for Data to automate AI lifecycles through data analysis. Users can run Watson Studio to own a hybrid cloud with a unified data and AI platform.
Kubeflow
Kubeflow is one of the leading DataRobot alternatives with the machine learning and AI toolkit. The main aim of the Kubeflow project is to deploy best-of-breed open-source systems for AI/ML to diverse infrastructures.
Qlik AutoML
Qlik AutoML is helping the no-code and automated machine learning and artificial intelligence for data analysis teams. It can easily generate models, make predictions, as well as test business scenarios with a code-free experience. It helps to publish the data and directly integrate models for fully interactive data analysis.
Source: analyticsinsight.net
