Machine Learning, as we know it today, has become a staple feature of almost every application we come in close contact with regularly. From spellcheck and text prediction to automating complex tasks that otherwise required several manhours, AI and its subsets like Machine Learning are taking giant strides in the world of cognitive computing.
A quick brief about Machine Learning
Machine Learning is a subset of Artificial intelligence. It can be defined as the study of computer algorithms that improve automatically through experience and by the use of data. ML algorithms continually improve themselves by observing patterns in the current data sets and the dynamic datasets that they are supplied with. Supervised and unsupervised learning enables them to become smarter by the day and make accurate predictions that drive business results.
Building a Machine Learning Application, irrespective of the purpose and industry, requires a machine learning platform. The choice of a machine learning platform is crucial since the pace and efficiency with which the software lifecycle is completed are dependent on it.
That said, there are countless machine learning platforms that you can choose from. With every passing day, new platforms are being added to the list, making it difficult to make a final choice.
If you are taking the first step towards developing a machine learning application, here are some applications you can consider to get the work done for you without breaking into a sweat.
RapidMiner
RapidMiner aims to bring enterprise-grade AI capabilities to all. It is an end-to-end AI platform that provides Machine Learning, data prep, and model operations all with a positive user experience that focuses on making lives easier for data scientists and other stakeholders.
What is it popular for?
- A visual process flow
- Sliders for scenarios
- What-if analyses
- Free for academic use
Alteryx
Alteryx is another end-to-end AI platform, but with a focus on digital transformation. Its unified business process automation, data science, and machine learning capabilities can help organizations of all sizes to achieve automation at scale.
What do they find it good at?
- Easy-to-learn
- Quick reuse of existing workflows
- Simplifies data extraction
- Integrates Pythos and R programming scripts
Peltarion
If coding doesn’t come to you easily, Peltarion can make life easy for you. It is a no-code deep learning platform that can help create and launch machine learning applications at scale. It makes a difference with its deep learning capability that can help the ML system improve its prediction accuracy with time.
What do they find it good at?
- Easy-to-use even for non-programmers
- A thriving community of users
- Pre-made databases
- Robust user support
Kraken
Kraken is an AutoML system from BigSquid. It can integrate directly with the BI platforms already in use by enterprises and help data analysts derive more value out of it. Like with most present-day ML platforms, Kraken also offers a no-code platform that literally simplifies creating and managing any AI project.
What do they find it good at?
- Offers quick results
- Short learning curve
- Cost-effective pricing
- Robust training and support
IBM Decision Optimization
IBM is a pioneer when it comes to automation and global digital transformation. It is no surprise that IBM’s Decision Optimization is one of the top ML platforms out there. IBM Decision Optimization is a family of prescriptive analytics products that augments enterprise decision-making with the help of Artificial Intelligence.
What do they find it good at?
- Comprehensive design architecture
- Accompanying PoC studies
- Capable of handling large-scale data projects
- Offers both Math Programming and Constraint Programming
RStudio
RStudio stands out from the crowd as an open-source modular data science platform. It also combines commercial products giving data scientists complete liberty to create an ML system that will work the way they want it to work.
What do they find it good at?
- Available in Open source and commercial versions
- Integrates with GitHub for version release control
- An object-oriented platform that manipulates information like objects
- Allow checking data frames and filter them by format
Dataiku DSS
If your organization has plans of upscaling from analytics to enterprise AI, Dataiku DSS is a great platform that can help with the cause. It has a huge repository of best practices, resources that make machine learning easier to approach, a controlled environment for AI deployment/management, and so on.
What do they find it good at?
- Visualized data flows
- Accommodates plugins
- Proactive customer support
- Vivid visual depiction of data
TIMi Suite
Analytics, predictive modeling, and BI dashboards form the pillars of any AI application. TIMi Suite is an AI/ML platform that enables businesses to have all these capabilities bundled together in one solution.
What do they find it good at?
- Reliable data processing and analysis
- Predictive analytics
- Data storage
- Power ETL capabilities
Why does the choice of a Machine Learning platform matter?
From just-launched startups to large-scale enterprises, every business of diverse scales wants to experiment with AI and ML. However, not all of them have the capability and resources to create an AI/ML platform from scratch. It is necessary to go with a platform that is already existing in the market.
The choice of this platform matters because the entire capabilities and data processing functionalities are tied to the capabilities of the platform. A good choice ensures that there is agility, scalability, and flexibility.
While there are umpteen choices one can pick from, these 8 choices of AI and ML platforms are considered to be the best by the IT community.
Do you know of any AI/ML platforms that the world needs to consider? Do let us know in the comments.
