MLOPs as a Service

About MLOPs as a Service

Machine learning (ML) is the catalyst to a strong, flexible, and resilient business in today's market. Smart companies leverage ML to create top-to-bottom growth, employee productivity, and customer satisfaction. With AI and Machine Learning are being implemented in the core of the operations, there is an increasing need for sound operations and risk management in the ML Model Lifecycle. These enterprises have tasted early success with a few ML use cases, but they cannot afford to stop there. While experimenting with ML is easy, most firms lack the skills, processes, and tools required to reliably integrate ML models into business applications, processes, and drive adoption across the enterprise

NxtGen’s datacentre infrastructure encapsulates compute, storage and network infrastructure in a managed, secure and intelligent delivery, hosted in datacentres across the country.

Our MLOps eco-system is a Collaborative Cloud native with a unified UI to manage all data science in one place to build and deploy Machine Learning Models into production safely, quickly and in a scalable fashion.

Why most companies fail to derive full value from AI & ML?

  • SCALABILITY – Absence of Scalable Cloud Infrastructure for handling peaks during data churning and processing, To assure trust & confidence, Expandability & Interpretability of ML models is needed but lacking in the industry. ML Models are subject to bias and overfitting resulting in AI Failure.
  • OPERATIONS - Operationalizing AI Requires integrating many different components, Challenges in operationalizing AI models in production and enabling real time scoring due to complex deployment processes in legacy IT architecture.
  • TALENT COLLABORATION - Traditional DSML approaches require a team of Domain Experts, Data Scientists, DevOps Engineers, Developers & Decision Scientists – which is prohibitively expensive & hard. Operationalizing AI Requires Collaboration between roles.
  • VALUE GOVERNANCE - AI failure and decision inaccuracy due to immature ML governance, and lack of best practices in performance tracking. Organizations struggle to measure outcomes and benefits of ML Models and track the true value of AI.

NXTGEN Advantages

  • Fully Managed Data Center and Cloud Services
  • Secured and Adaptive Scalability for Computing Resources
  • Unified Distributed Exa-Scale Storage & Software Defined Networks
  • Leading Hypervisors for DevOps and Application Workloads
  • Robust Platform for Distributed Micro-services as a Pre-integrated solution
  • AI/MLOps Engines to deliver outcomes
  • Pre-Integrated, Native AI/IoT/ Data Analytics Enabled Outcomes / Insights
  • A digital platform to transact and a Vault for the digital assets
  • Pre-Integrated Analytics for outcome based specific use-cases, co-developed for usecases

Operationalize AI at Scale - Visualization Explore your data using the array of data visualizations and use your existing BI tools

The NxtGen MLOps as a Service will give data teams the ability to efficiently build, train, deploy and monitor machine learning model pipelines in a platform-agnostic manner. The solution is designed to simplify machine learning deployment across verticals like financial services, insurance, health care, telecommunications, retail, pharmaceutical, and marketing. The offering comes with a marketplace that would offer over 200 + prebuilt models, notebooks and apps for use cases like customer churn prediction, sales targeting, spend optimisation, credit risk scoring, anti-money laundering, predictive maintenance, customer monitoring, malicious domain detection, and more.

Connect data from any source

Make disparate data assets accessible and available for Model Development. Connect data from any cloud, on-premises, or proprietary system using collection of over 75 Connectors

Scheduled Updates

Automate replications with recurring incremental updates.​

Extensible with your own connectors​​

Build connectors in the language of your choice​

Image Description

Transform your data into features to train models and make predictions.​

Transform raw data into feature values, store the values, and serve them for model training and online predictions using katonic’s Integrated Feature Store. ​

Ensure consistency across training and serving​

Guarantees you’re serving the same data to models during training and inference, eliminating training-serving skew.​

Automate feature transformations​

Orchestrate data pipelines to generate backfills and continuously compute fresh feature values. ​

Image Description

Quality models built fast at scale​

Build high-quality models and release them to production faster, with self-serve access to the latest tools and scalable compute.

Katonic Supports all of these popular platforms and frameworks—plus many, many more.

Image Description

Deploy Models to Production in Seconds​

Deploys the model’s native runtime image (e.g. Python, R, H2O, etc) into a containerized endpoint in just a few click with elastic scaling, high availability, and reliability.

Integration with the model registry allows data scientists to track model versions and seamlessly update models when needed.​

Generate predictions for both Batch and real time processing.​

Configure canary deployment for incremental model rollouts.​​

Image Description
Image Description

Continuous Model monitoring​

Monitor the effectiveness and efficiency of a deployed model with a simple dashboard integrated with Model Registry and Feature Store.​

Get real-time insights and alerts on model performance and data characteristics​.​

debug anomalies and initiate trigger to execute ML production pipelines to retrain the models with new data, depending on your use case​​

Security and Control

Katonic ML Ops Platform provides multi-tenancy and data isolation to ensure logical separation between each project, group, or department within the organization. The platform integrates with enterprise security and authentication mechanisms such as LDAP, Active Directory . Different project teams, groups, or departments across the enterprise can share the same infrastructure and access the same data sources for their AI / ML and Big Data analytics workloads

Seamless integrates with your identity infrastructure using existing LDAP or SSO.​.​

Katonic is ISO 27001 certified, and a copy of our report is available upon request.​​

Image Description
Sign Up