Devops And Modelops
DevOps is a software engineering methodology focused on helping software developers (dev) and operations (ops) teams by facilitating a culture of collaboration and shared responsibility. Using iterative software development and automation, DevOps improves productivity across various teams of your organization - helping teams manage the software development lifecycle and drive business outcomes faster.
With the rise of Machine Learning and Artificial Intelligence, DevOps solutions are missing key capabilities focused on ML/AI workloads and the unique challenges of integrating data science into modern web applications. ModelOps aims to bridge this gap.
ModelOps enables your company to integrate cross-functional stakeholders to make sure that your data science workloads (e.g. ML/AI models) are managed, operationalized, and refined in a continuous manner. ModelOps tools allows you to plan, build, test, deploy, secure and observe ML and AI workloads.
ModelOps itself is not one feature but instead a conglomeration of a wide range of integrated features across traditional devops systems that allows you to streamline workflows, reduce complexity, and speed up development that includes:
Native data connectors - Turnkey connectors for major data warehouse providers to provide easy connectivity between data stores and ML/AI workloads
ELT engine - Ability to extract, load, transform data from disparate data sources and get it into the right shape/form for processing.
Code Review Workflows - Set code review policies to govern how code and models can be reviewed and approved before being deployed
Feature Store/Data Catalog - Reduce repetitive data processing and curation work required to convert raw data into features for training an ML algorithm
Security & Compliance Policies - Manage and enforce policies for code and models to minimize security risks and promote adherence to compliance rules.
Data versioning - Most data is not static, it’s dynamic, versioning is focused on tracking and storing changes to data schemas, lineage and behavior over time.
Enterprise Python notebook experience - Rendering, visual diffing, code review, executable cells, dependency management, environment management, compute connectors as well as discovery and explorability.
Agile Planning - Manage and plan your projects and programs within your team or across your enterprise with integrated Agile support.
Specialized Compute - Powerful cloud machines with great CPU/GPU/Memory allocations appropriate for ML/AI workloads
Workload orchestration - Build, test and deploy ML models based on task sequencing that you specify. Inclusion of advanced CI/CD features can help reduce wasted compute like auto-stops, job caching and checkpoints.
Security Scanning - Run security scans of your code and models to identify, track and resolve security vulnerabilities
Model registry - Version control for machine learning workloads. Store, discover and verify model artifacts and model metadata.
Model observability - Inspection into models and visualizations to understand how and why ML models produce the results they do. Data check, performance analysis, monitoring and explainability.
With the rise of Machine Learning and Artificial Intelligence, DevOps solutions are missing key capabilities focused on ML/AI workloads and the unique challenges of integrating data science into modern web applications. ModelOps aims to bridge this gap.
ModelOps enables your company to integrate cross-functional stakeholders to make sure that your data science workloads (e.g. ML/AI models) are managed, operationalized, and refined in a continuous manner. ModelOps tools allows you to plan, build, test, deploy, secure and observe ML and AI workloads.
ModelOps itself is not one feature but instead a conglomeration of a wide range of integrated features across traditional devops systems that allows you to streamline workflows, reduce complexity, and speed up development that includes:
Native data connectors - Turnkey connectors for major data warehouse providers to provide easy connectivity between data stores and ML/AI workloads
ELT engine - Ability to extract, load, transform data from disparate data sources and get it into the right shape/form for processing.
Code Review Workflows - Set code review policies to govern how code and models can be reviewed and approved before being deployed
Feature Store/Data Catalog - Reduce repetitive data processing and curation work required to convert raw data into features for training an ML algorithm
Security & Compliance Policies - Manage and enforce policies for code and models to minimize security risks and promote adherence to compliance rules.
Data versioning - Most data is not static, it’s dynamic, versioning is focused on tracking and storing changes to data schemas, lineage and behavior over time.
Enterprise Python notebook experience - Rendering, visual diffing, code review, executable cells, dependency management, environment management, compute connectors as well as discovery and explorability.
Agile Planning - Manage and plan your projects and programs within your team or across your enterprise with integrated Agile support.
Specialized Compute - Powerful cloud machines with great CPU/GPU/Memory allocations appropriate for ML/AI workloads
Workload orchestration - Build, test and deploy ML models based on task sequencing that you specify. Inclusion of advanced CI/CD features can help reduce wasted compute like auto-stops, job caching and checkpoints.
Security Scanning - Run security scans of your code and models to identify, track and resolve security vulnerabilities
Model registry - Version control for machine learning workloads. Store, discover and verify model artifacts and model metadata.
Model observability - Inspection into models and visualizations to understand how and why ML models produce the results they do. Data check, performance analysis, monitoring and explainability.
Devops And Modelops
DevOps is a software engineering methodology focused on helping software developers (dev) and operations (ops) teams by facilitating a culture of collaboration and shared responsibility
