Develop quick experiments on your data. Automate data preparation, feature engineering, algorithm selection, hyper-tuning with a click of a button.
Build custom models using industry-specific recipes, onboard existing recipes, along with pipelines built using AIS Auto.
Build fair, transparent, and reliable models to improve AI adoption. Access AI Studio XAI to understand the results generated by machine learning.
Deploy and operationalize ML models built on AIS Auto or AIS interactive or models built outside using end-to-end model life-cycle management in production.
Build models faster on a large volume of training data leveraging distributed computing environment of AI studio.
Make data analytics and AI/ML accessible to everyone in the organization
Drive innovation with AI-driven decision analytics
Scale-up or down depending on your business goals and usage
Reduce technology investments and infrastructure management costs
Improve data science team collaboration on a single interface
Intuitive drag & drop capabilities for users without coding skills
Faster data to insights and reduced infrastructure management time
Self-serve, DIY capabilities that minimize dependence on the IT team
Seamlessly integrate with your favorite tools and data sources
Pre-built templates, libraries, and workflows to manage use cases
Create complex models and run multiple AI experiments easily
Access 100+ operators built for various operations required in the ML lifecycle
Use in-built visual analytics to create intuitive charts, and graphs
Iterate 100s of models through automated ML operators
Comes with bias detection, de-biasing, explainable AI capabilities
Pre-built accelerators, templates to create faster and efficient ML pipelines
A combination of automated & augmented operators to drive efficiencies
A composable platform with modular and plug-and-play capabilities
Cloud-native containerized solution with separate microservices for agility
Run models on any volume of data; see results in minutes
Integrate with any existing data source or upload your data files in just a few clicks
Build, train, test, and validate AI models with intuitive drag-and-drop UI
Save and launch your models to production and solve complex business problems
Invite and collaborate with relevant stakeholders to monitor and iterate AI models
*Based on our performance benchmarks
Integrated visual analytics to create intuitive charts and graphs during (not after) pipeline creation. Configure new data sources, plot charts, create reports in a click to quickly understand the trends and patterns in complex data. Derive deep, actionable insights in real-time to get better outcomes.
Automate time-consuming and iterative tasks of machine learning model development. Allows data scientists and experts to build ML models with higher scale, productivity, and efficiency while sustaining the model quality. It makes data science accessible to all trained and non-trained resources to build accurate and robust models.
Accelerate adoption and run faster AI experiments with pre-built telco accelerators and use case templates that can be customized to your needs. Build and fail-fast to explore new use cases quickly.
Access a no-code data science environment with AI automation capabilities to build and manage AI models. Intuitive, drag-and-drop UI that enables users without coding skills to apply AI. Make it easy for non-technical business users to turn data into profitable decisions.
Build and manage complex AI models with features such as exploratory data analysis and AutoML. Automate every step of the data science lifecycle including, feature engineering, algorithm selection, hyper-parameter tuning and model validation.
Each studio feature is built, keeping user journey, pain points, and challenges in mind. The features, interactions, and navigations are build to optimize user efforts and enable "Ease of use.
Democratizes AI across the entire data science cycle, accelerating AI adoption and making it accessible to everyone in the organization. Get our AI Studio Brochure to know more about it.
“AI is essential to transforming CSPs’ network and business operations. Omdia’s research for example shows that nearly 80% of CSPs see AI projects as important or very important IT for network operations for 2021. However, access to high quality data and AI skills present challenges regarding the execution of these projects. Capabilities within Subex’s HyperSense platform such as no-code programming, data management and AI studios and pre-packaged AI use cases can help CSPs scale their use of AI across the business to achieve their business objectives.”
“Enterprises and service providers have an accelerating demand for high-speed data tools that enable them to leverage underlying data to unlock greater agility, faster digital transformation, and new innovations,” says Scott St. John, managing editor at Pipeline magazine. “HyperSense represents the future of data analytics and digital trust, built upon Subex’s years of expertise with its ROC platform and its sophisticated application of AI, machine learning, and data analytics.”
“Subex is getting into the platform business and continue to focus on telecoms sector. They are ahead of many other vendors in their approach. It will all be about early traction to help shift their addressable market beyond RA and FM. I congratulate them on their ambition and good luck to them.”
“With HyperSense, Subex aims to revolutionize the way AI is adopted in the telecom world. And since Subex is one of the top telecom analytics platform providers in the market, HyperSense is a true paradigm shift to a more open, AI-based, cloud-friendly, and inter-departmental style of extracting analytics value for telcos. I wish Subex the best in rolling HyperSense out.”
“With the launch of HyperSense, Subex has joined the ranks of leading telco AI providers. By enabling non-technical employees to develop their own analytics, HyperSense can substantially expand the amount of AI analytics deployed at the carrier to improve marketing, care, and operations. HyperSense also builds in up-to-date AI governance capabilities.”