Machine Learning and Artificial intelligence are driving breakthrough innovations in the IT and corporate environment at a tremendous pace, enabling executives to accelerate decision-making and bolster productivity through automating the core business processes.
While in 2019, the global ML market size was valued at $8.43 billion, it’s predicted to reach a whopping $117.19 billion by 2027.
The result is that more data-driven businesses are speeding up their investment rate in the Data Science, Machine Learning and Big Data initiatives.
Unfold our comprehensive guide to understand everything essential you need to know about the best machine learning platform and drive digital transformation through augmented intelligence.
What is Machine Learning?
Machine learning is a subfield of computer engineering and artificial intelligence (AI) that leverages data to boost the system capacity and performance for improved accuracy in predicting results without being explicitly programmed. It analyses the input data to generate algorithms and identify data characterisation — trends and patterns.
Types of Machine Learning
- Supervised Machine Learning: Applies labelled datasets past data to make algorithms predict results
- Unsupervised Machine Learning: The process of applying ML algorithms to investigate and cluster unlabeled datasets and uncover hidden patterns in data
- Reinforcement Machine Learning: Boosts decision efficacy
Machine Learning Platforms
Built on top of Machine Learning technology, an ML platform is a module with well-organised tools for efficiently managing the modelling lifecycle by emphasising model experimentation, reproducibility, and deployment. It focuses on helping data scientists automate workflows, speed up the processing of voluminous data, and optimise corresponding business functionalities.
Machine learning platforms allow IT and corporate executives to get in-depth insights into the entire customer lifecycle and accelerate core business processes. Thus they can make data-driven and faster decisions and improve their products and services through the powerful processing of large-scale data sets.
Despite being a domain specialised for expert data scientists, ML platforms are getting increasingly popular for their intuitive and easy-to-navigate approach. The prebuilt algorithms and drag-and-drop feature coming with some high-end ML tools allow people with no technical flair to understand, build and deploy predictive models and extract insights to turn them into business value in a breeze!
Most ML platforms are cloud-based centralised systems that have support for all teams across the data science lifecycle. They can share the extracted insights, analysed models, and more in a single collaborative environment.
These solutions are unified systems with out-of-the-box data preparation, exploration, augmentation, and visualisation systems for optimised experimentation.
Best Machine Learning Platform for Beginners
With the market bustling with tons of ML products, choosing the best one may seem overwhelming. We have rounded up a concise list of the best machine learning platform for beginners:
BigML
BigML, a cloud-based GUI environment with a suite of robustly-engineered power-packed ML algorithms, is constantly pumping out premium ML platforms with hefty price tags by enabling executives to leverage a standardised, single framework across their business to tackle real-world issues.
From product ideation to risk management and sales forecasting, BigML can cater to all your business needs with ease!
Key Features
- Supports REST APIs to help build user-friendly, responsive, point-and-click, and speedier web-interface
- Allows model exporting via JSON PML and plugging them into external software like Echo, Amazon, Google Sheets, and Zapier, even your mobile device, web, or IoT systems
- With BigML Organisation, users can share their dashboards as a workspace and collaborate in real-time.
- Single-tenant and multi-tenant editions for on-prem or cloud deployment
KNIME Analytics Platform
KNIME is a Java-based open-source Machine Learning platform that brings end-to-end data preprocessing, modelling, analysis, visualisation, and reporting into one workflow.
Running based on the modular data pipeline concept (Lego of Analytics), KNIME aims to enable data scientists to integrate various elements for effortless data mining and machine learning and efficiently mine data within an intuitive and interactive user interface.
Though the exporting capabilities may seem limited, you can work on large-scale data sets and customise this ML-based platform with simple drag-and-drop functionality using visual programming – no coding skill is required. By supporting hundreds of built-in modules, KNIME’s core version has made data management right out of the box.
Key Features
- Supports image mining, text mining, network mining, and through plugins
- Allows integrating codes written in R, JavaScript, Python, C++, C, and more.
- Integrates with a spectrum of data warehousing systems and databases, for instance, Microsoft SQL, Oracle, and Hive for seamless data blending.
- Supports parallel processing on multi-core systems
TIBCO Software
TIBCO is a highly flexible ML solution that allows executives to automate, democratise, and operationalise data science across their entire business and help build ML algorithm-powered enterprise-grade AI applications.
From data preparation to model construction, deployment, and retraining, TIBCO has end-to-end tools for the complete analytics lifecycle to streamline the analysis process, turn insights into optimal results and drive breakthrough innovations.
Key Features
- Inbuilt audit trail, version control, and data access authorisation strategies for high-level enterprise governance.
- Seamless integration with R, Scala, C#, Jupyter Notebooks, Microsoft Azure, Amazon, and Google ecosystems for top-notch extensibility of the analytics pipeline
- Allows to produce and run reusable custom operators at the edge, in-cluster, and in-database.
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