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.
- 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 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:
- 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 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:
- 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
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
- 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, 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.