Popular Machine Learning Platforms

AI platforms are the product that information researchers and AI experts use to convey AI models and calculations. With the advancement of information, AI has been dramatically expanded. By automating the business and predicting the results based on experience or past trends, machine learning has solved a number of issues.

Have you at any point contemplated why you get item proposals from different internet-based platforms like Amazon, Netflix, Flipkart, and so forth.? The short response is AI. It turned into the most famous trendy expression today in all advancements, and the whole 21st 100 years, as well as the impending age, will utilize AI innovation for their organizations. All little and enormous organizations, including Facebook, Google, Amazon, IBM, Prophet, and so on., utilize AI advancements to run and develop their business. Thus, sit back and relax! You have arrived at the ideal location. In spite of the fact that AI is utilized all over the place, the fundamental issue is the platforms that help AI administrations. This article will examine probably the most well-known AI platforms that will assist you with dealing with your trials at each stage, for example, planning information for organization, checking, and overseeing AI models. Therefore, let's begin by providing a brief overview of machine learning.

What is Machine Learning?

AI is characterized as the cutting-edge utilization of man-made brainpower that helps machines/PCs to gain and improve for a fact and predicts results for the future utilizing different algorithms.

ML utilizes different technologies such as Supervised, unsupervised, Semi-supervised, and reinforcement learning to teach machines. It has a large number of utilizations like 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 AI platform is utilized to robotize and revive the conveyance lifecycle of prescient applications that have the capacities to deal with large information.

It helps assemble blocks to tackle the different ML and information science issues. It gives a reasonable climate to clients to get total opportunity to deploy their items.

We will examine a couple of most famous AI 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.

• Amazon Sagemaker
• TIBCO Software
• Alteryx Analytics
• SAS
• H2O.ai
• DataRobot
• RapidMiner

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.

Top Features:

• Build highly accurate training datasets
• It helps to extract and analyze data automatically for better accuracy and faster decision-making.
• It helps detect frauds such as suspicious transactions and trigger alerts on customer accounts.
• Churn prediction
• It helps deliver customized and personal recommendations to the customer to improve and grow their business process.
• It allows you not to break down data sets into multiple chunks.

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:

• Automate manual data tasks into repeatable analytics workflows
• It provides the flexibility of deploying and managing analytical models and helps analysts prepare, organize and analyze data faster with zero coding skills.
• It helps you with the flexibility of using all data sources and visualization tools.
• It does not require complex coding skills to perform statistical problems in building predictive models.

3. TIBCO

TIBCO is a data science platform that integrates with numerous open source libraries and supports the entire lifecycle of analytics, including cloud-based analytics.

A cloud stage runs and adjusts your associated business.

TIBCO information science permits the client to plan information and fabricate, send, and screen the model. It has numerous use cases, including product improvement and business investigation.

Features of TIBCO:

• It enables users to easily and quickly connect applications and APIs using the browser.
• It provides the services like metadata management, data catalog, data governance, etc.
• It facilitates users' actionable intelligence in real-time.
• It helps to build smart apps with a single click.
• It supports cloud messaging for reliable and secure data distribution.
• It reduces decision latency to a greater extent and acts in real-time.

4. SAS

SAS offers cutting-edge software for data science and analytics that facilitates data facility accessibility regardless of data format or source.

It works on regular language handling to chip away at continuous situations. Additionally, it automatically creates a pipeline that improves data organization. It offers all clients to work with open-source models for their projects.

Features of SAS:

• It offers a visual interface for data analytics. It allows users to explore data within the model studio.
• You can access training data within the model studio from each node.

5. ai

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:

• Credit risk scoring
• Predicting Hospital Acquired Infections (HAIs)
• Medical testing
• Predictive manufacturing design
• Supply chain optimization
• Pricing Optimization
• Anomaly detection
• Customer churn management
• Product recommendation
• Content Personalization
• AML, lead scoring, fraud detection, KYC, smart segmentation, etc.

Features of H2O.ai

• H2O is the open source leader in AI, which aims to democratize AI.
• It supports the facility of building responsible AI models and applications.
• It also helps build explainable AI models with greater transparency, accountability, and trustworthiness in AI.
• It provides automatic feature recommendation, drift, insights, versioning, metadata, rank and bias identification, etc.

6. DataRobot

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:

• It provides cloud capabilities for enterprise AI visual data preparation and builds and runs sophisticated data pipelines in the desired language.
• It helps to generate the best feature for your models by connecting various data sources and formats.
• It helps to explore and visualize data to find new patterns and insights.

DataRobots in Machine Learning:

• It is used to create advanced ML models automatically.
• It is used to forecast the real world with an automated time-series feature.
• It uses Natural language processing to extract meaning from text data.
• It adds geospatial context to ML models.
• It supports human-readable mathematical formulas that can solve sophisticated machine learning problems.

DataRobot in MLOps:

• It helps deploy, monitor, and manage any ML model in any location.
• It provides portable prediction servers that help in easy-to-use Docker containers to host production models.
• It is used in the model registry, etc.

Features of DataRobot

• Speed: It helps to bring AI into production faster than ever.
• Impact: It helps in transforming data to business results with confidence.
• Scale: It helps to deploy AI anywhere at scale.

7. RapidMiner

One of the most well-liked platforms for multimodal predictive analytics, machine learning, and complete data science solutions is RapidMiner. It is utilized to upgrade navigation. It offers various complex, adaptable methodologies that will transform the information into experiences that can be utilized to defeat difficulties and accomplish extraordinary objectives. It has broad involvement with all significant businesses like assembling, energy, utilities, car, medical care, monetary administrations, protection, life science, correspondence, travel, transport, coordinated operations, and so forth.

Use cases on RapidMiner

• Churn prevention means identifying customers likely to leave take preventative action.
• It is used to make intelligent decisions automatically through AI and ML using cognitive RPA.
• In-text mining, i.e., extract insight from unstructured content.
• It helps predict the next best action, which means the right action at the right time for the right customer.
• It helps in identifying fraudulent activity quickly and resolves it too.
• It gives quality assurance and resolves quality issues before they become a problem.

Features of RapidMiner

• Ubiquitous, portable & extensible
• Easy to Trust, Tune & Explain
• Deliver ROI & results, not just technically sound models
• Increase productivity and performance
• Transformational business impact
• Upskill Your Organization

Conclusion

With data science and big data, AI turned out to be all the more impressive among information researchers and experts. These AI stages assume a critical part in creating and sending ML models. Your company's expansion, as well as customer satisfaction and support, depend on this software. In the event that you need to upskill your association, you can pick both of the above-given AI platforms to smooth the run of your business.