Model 360

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Overview: Machine
Learning Operations

MLOps(Machine Learning Operations) is a process that aims to streamline and optimize the production deployment and maintenance of machine learning models

It enables data scientists to build, test and deploy models faster and more efficiently while providing IT and DevOps teams with the necessary tools to manage the models in a scalable and reliable way

This helps businesses to improve the accuracy and reliability of their machine learning models and to gain more value from their data-driven applications

Machine learning Challenges

Inability to track and manage multiple ML models developed by different teams
Difficulty in reproducing and scaling ML experiments to production
Lack of visibility into model performance and behavior in production
Inefficient model deployment processes causing delays and errors
Poor collaboration and communication between data scientists, engineers, and operations teams
Inability to ensure model fairness, ethics, and compliance with regulations
Lack of version control and traceability for ML models
High infrastructure costs for training and deploying ML models
Difficulty in managing data pipelines and ensuring data quality
Security vulnerabilities in ML models and infrastructure

Why Model 360?

Data quality and quantity: The success of an ML project depends heavily on the quality and quantity of data available Data collection, cleaning, and labeling can be time-consuming and costly

Model selection and tuning: Choosing the right algorithm and hyperparameters for the model can be challenging and require a lot of trial and error

Infrastructure and resource management: ML models can be resource-intensive, and managing the infrastructure and resources required to train and deploy the models can be complex

Model interpretability and explainability: ML models can be difficult to interpret, and it can be challenging to understand how a model makes predictions

Collaboration and communication: ML projects often involve multiple stakeholders, including data scientists, engineers, and business stakeholders, and effective collaboration and communication can be critical to project success

Version control and reproducibility: It can be challenging to track changes to models and experiments over time and ensure that results are reproducible

Deployment and monitoring: Once a model is trained, deploying it to production and monitoring its performance can be complex and require ongoing maintenance and updates

Who Are the User

Data Scientists

ML Ops can help data scientists by automating many tasks, such as data preparation, model training, and model deployment, allowing them to focus on more high-level tasks such as feature engineering and model selection

Data Engineers

ML Ops can help data engineers by providing a way to manage data pipelines and ensure data quality, allowing them to focus on building efficient data infrastructures and integrating new data sources

DevOps Engineers

ML Ops can help DevOps engineers by providing a framework for managing machine learning models in production environments, allowing them to ensure that the models are running efficiently and that any issues are quickly identified and resolved

Business Users

ML Ops can help business users by providing access to real-time analytics and insights, enabling them to make data-driven decisions and stay ahead of the competition

IT Managers

ML Ops can help IT managers by providing a way to manage and monitor the entire machine learning process, allowing them to identify areas for improvement and ensure that the process is running smoothly

What is Model 360

Cloud Agnostic
Model 360 supports deployment across multiple cloud providers, giving customers the flexibility to choose the one that best suits their needs
Automated Model Validation
Model 360 includes automated model validation and testing to ensure the quality and accuracy of models before deployment, saving customers time and resources
Explainability and Interpretability
Model 360 prioritizes explainability and interpretability of models, helping customers understand the decisions made by their models and improving trust in the system
User-friendly Interface
Model 360 offers a user-friendly interface, making it easy for non-technical users to interact with and manage models
Integration with Existing Tools
Model 360 integrates seamlessly with other existing tools and platforms, providing customers with a comprehensive solution for managing their ML workflow
Scalability
Model 360 is designed for scalability, allowing customers to easily scale their ML workflows as their business needs grow
Security
Model 360 prioritizes security, ensuring that models and data are protected from external threats and vulnerabilities
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Cloud Agnostic

Model 360 supports deployment across multiple cloud providers, giving customers the flexibility to choose the one that best suits their needs

Automated Model Validation

Model 360 includes automated model validation and testing to ensure the quality and accuracy of models before deployment, saving customers time and resources

Explainability and Interpretability

Model 360 prioritizes explainability and interpretability of models, helping customers understand the decisions made by their models and improving trust in the system

User-friendly Interface

Model 360 offers a user-friendly interface, making it easy for non-technical users to interact with and manage models

Integration with Existing Tools

Model 360 integrates seamlessly with other existing tools and platforms, providing customers with a comprehensive solution for managing their ML workflow

Scalability

Model 360 is designed for scalability, allowing customers to easily scale their ML workflows as their business needs grow

Security

Model 360 prioritizes security, ensuring that models and data are protected from external threats and vulnerabilities

Monitoring KPI’s and KRI’s

  • Time to deployment
  • Models in Production
  • New models deployed
  • API calls
  • Chargebacks vs Showbacks
  • False alerts vs total alerts (drift)
  • Risk Rating Models
  • Win% trend over time period
  • Model Accuracy
  • Model Latency
  • Model Throughput
  • Data Quality
  • Model Training Time
  • Deployment Frequency
  • Error Rates

Value Creation with Model 360

Model 360 offers end-to-end support for managing and operationalizing machine learning models, ensuring scalability, reliability, and governance throughout the machine learning lifecycle

Increased productivity

By automating repetitive tasks, Model 360 can free up valuable time for data scientists, engineers, and other team members to focus on higher-value tasks such as model development and experimentation

Read More

Improved efficiency

Model 360 can streamline the entire machine learning lifecycle, from data collection and preprocessing to model training and deployment This can lead to faster development and deployment times, as well as improved accuracy and performance of models

Read More

Better collaboration

Model 360 can help teams work more effectively together, by enabling better communication and collaboration across different departments and functions

Read More

Enhanced model governance

Model 360 helps to establish governance policies around the development, deployment, and monitoring of machine learning models This can help to ensure regulatory compliance, reduce risk, and improve transparency

Read More

Cost savings

Model 360 can help to reduce costs associated with machine learning projects by optimizing the use of cloud resources, improving model efficiency, and reducing the need for manual intervention

Read More

Increased productivity

Read More
By automating repetitive tasks, Model 360 can free up valuable time for data scientists, engineers, and other team members to focus on higher-value tasks such as model development and experimentation

Improved efficiency

Read More
Model 360 can streamline the entire machine learning lifecycle, from data collection and preprocessing to model training and deployment This can lead to faster development and deployment times, as well as improved accuracy and performance of models

Better collaboration

Read More
Model 360 can help teams work more effectively together, by enabling better communication and collaboration across different departments and functions

Enhanced model governance

Read More
Model 360 helps to establish governance policies around the development, deployment, and monitoring of machine learning models This can help to ensure regulatory compliance, reduce risk, and improve transparency

Cost savings

Read More
Model 360 can help to reduce costs associated with machine learning projects by optimizing the use of cloud resources, improving model efficiency, and reducing the need for manual intervention

Unlocking the Power of AI/ML for a Smarter Future

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Contact Information

Phone

+91 966 5050 222
+91 20 4120 3434

Mail

info@kinsight.ai

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