Popular Machine Learning Platforms
Machine Learning platforms are the software that data scientists and machine learning professionals use to deploy machine learning models and algorithms. With the evolution of data, machine learning has been exponentially increased. Machine learning has solved various problems by automating the business and predicting the results using experience or historical trends.
Have you ever thought about why you get product recommendations from various online platforms such as Amazon, Netflix, Flipkart, etc.? The short answer is Machine Learning. It became the most popular buzzword today in all technologies, and the entire 21th century, as well as the upcoming generation, is going to use machine learning technology for their businesses. All small and big companies, including Facebook, Google, Amazon, IBM, Oracle, etc., employ machine learning technologies to run and grow their business. So, don't worry! You are exactly in the right place. Although machine learning is used everywhere, the main problem is the platforms that support machine learning services. This article will discuss some of the most popular machine learning platforms that'll help you manage your experiments at every stage, such as preparing data for deployment, monitoring, and managing machine learning models. So let's start with a quick introduction to Machine learning first.
What is Machine Learning?
Machine Learning is defined as the state-of-the-art application of artificial intelligence that helps machines/computers to learn and improve from experience and predict results for the future using various algorithms.
ML uses various technologies such as Supervised, unsupervised, Semi-supervised, and reinforcement learning to teach machines. It has a wide range of applications such as speech recognition, text recognition, self-driving vehicle, email & spam filtering, healthcare, medicine, banking & finance, virtual personal assistant, chatbots, education, marketing, and many more. So the scope of machine learning is not limited to a few fields; it is employed everywhere around us.
What are Machine Learning Platforms?
The machine learning platform is used to automate and quicken the delivery lifecycle of predictive applications that have the capabilities to process big data.
It helps build blocks to solve the various ML and data science problems. It provides a suitable environment for users to get complete freedom to deploy their products.
We will discuss a few most popular machine learning platforms for deploying ML models.
Most popular Machine Learning Platforms
Machine Learning is the most popular technology in the 21st century that has various capabilities such as text recognition, image recognition, training, tuning, etc. There are some best machine learning platforms or software given below, using which you can effectively deploy machine learning in your business.
1. Amazon SageMaker
Amazon SageMaker is an Amazon Web Services (AWS) entity that helps data scientists and ML experts prepare, build, train, and deploy high-quality ML models. It provides one-click deployment support for various open-source models such as NLP, object detection, image classification, etc.
2. Alteryx Analytics
Alteryx is the best data science platform that accelerates digital transformation. It offers data accessibility and data science processes. It enables you to do complex things with data without having prior experience in coding and data mining techniques.
Features of Alteryx Analytics:
TIBCO is a data science platform that supports the entire analytics lifecycle with capabilities to include cloud-based analytics that integrates with many open source libraries.
It is a cloud platform that runs and adapts your connected business.
TIBCO data science allows the user to prepare data and build, deploy, and monitor the model. It is widely known for use cases, such as product refinement and business exploration.
Features of TIBCO:
SAS provides advanced data science and data analytics software that helps ease-of-access data facility irrespective of source and format of data.
It works on natural language processing to work on real-time scenarios. Further, it automatically generates a pipeline that helps to organize data in a better way. It offers all users to work with open-source models for their projects.
Features of SAS:
H2O.ai offers various facilities and functionalities of Artificial Intelligence and data science. It supports a highly scalable elastic environment for the AI life cycle.
Like SAS, it is also an open-source platform that deals with distributed in-memory ML platforms with linear scalability.
It is a cloud-based AI platform that deals with complex business problems and accelerates the discovery of new ideas with results you can understand and trust. It is a single platform with endless solutions that primarily focuses on the following:
Make: It helps build Ml models and applications with more accuracy, speed, and transparency.
Operate: it supports various Machine learning operations that streamline monitoring performance and rapidly adapt to changing conditions.
Innovate: It includes various AI AppStore that helps in easily deliverable innovative solutions to end-users.
Use cases of H2O.ai:
Features of H2O.ai
DataRobot is an AI cloud platform that helps build, prepare, deploy, predict, monitor, and optimize industry data models.
It offers services to various technologies such as data engineering, machine learning, MLOps, decision intelligence, trusted AI.
DataRobots in Data Engineering:
DataRobots in Machine Learning:
DataRobot in MLOps:
Features of DataRobot
RapidMiner is one of the most popular multimodal predictive analytics, Machine Learning, and end-to-end data science solution platform. It is used to optimize decision-making. It offers a variety of sophisticated, flexible approaches that will turn the data into insights that can be used to overcome challenges and achieve unique goals. It has extensive experience in all major industries such as manufacturing, energy, utilities, automotive, healthcare, financial services, insurance, life science, communication, travel, transport, logistics, etc.
Use cases on RapidMiner
Features of RapidMiner
With data science and big data, machine learning became more powerful among data scientists and professionals. These machine learning platforms play a significant role in developing and deploying ML models. This software is the key player for growing your business and customer satisfaction and support. If you want to upskill your organization, you can choose either of the above-given machine learning platforms to smooth the run of your business.