What Is Model Ops? – DZone
The rapid implementation of various new applications, cloud services, and other technologies has complicated IT environments too much for humans to handle, negatively affecting profits. It is difficult for companies to pursue digital transformation in a highly competitive market despite the significant investments they’ve put into artificial intelligence. They cannot streamline all organizational models, making it difficult to gain valuable insights from the models and make informed business decisions.
Scaling all available models from the dev region to the CI/CD pipeline to the deployment region can be a challenging process for DevOps teams within traditional siloed environments. The challenge gets even more difficult when you have to monitor and manage all these models in production for performance, drift, bias, and other risks. All this while adhering to international and local regulations until the model decays or is retired.
The Model Ops solution solves the problem of scalability by operationalizing all AI/ML and decision models in production. It also focuses on model monitoring and governance throughout the ML life cycle. Automating all these processes with Model Ops can increase observability and help businesses focus better on their business goals.
Let us discuss in this blog in detail Model Ops and how they help businesses overcome challenges associated with AI implementation without affecting their ROI.
Model Ops
ML teams in organizations design the business objective and develop, validate, and deploy each model in production with the DevOps team. But there is no proper approach.
- To scale all the models throughout the enterprise and manage them across their ML life cycle.
- Create reliable and automated CI/CD ML deployment pipelines to streamline the model deployment process
- Monitor models in production and ensure that AI model compliance standards are met.
A practical solution to resolve these problems is “Model Ops.” It is a framework for AI organizations to operationalize their AI analytics. Model Ops can help to streamline the AI/ML model lifecycle by automating deployments, monitoring models, providing end-to-end governance, and continuous improvement of data analytics models that are created in the organization.
Figure 1. Model Ops
Model Ops vs ML Ops
ML Ops is a data science platform designed to streamline the process of building, training, and deploying ML models while managing and monitoring model performance in production. It combines machine learning, software development, and operations that enable organizations to deliver high-quality models in a production environment with reduced time to market and increased accuracy.
Model Ops is a set of practices and tools that provide governance and operations for models in production. This includes enabling organizations to deploy, monitor, and manage machine learning models with greater visibility, control, and automation. It helps organizations to ensure that models are reliable, performant, and compliant with best practices and regulations.
MLOps automates the ML lifecycle, from creating models to deploying and monitoring them. It also improves collaboration between data scientists and operations teams, helping to ensure that models are deployed and maintained in a secure and efficient manner.
Model Ops helps businesses improve their operational efficiency by leveraging data-driven models and advanced analytics to manage predictive models with a focus on providing actionable insights that can be used to optimize decision-making and overall performance.
ML Ops focuses on enabling collaboration between data scientists and IT teams in development, operations, and production environments to quickly and reliably deploy ML models, track the performance of those models over time, and quickly iterate on improvements to maximize their business value.
Model Ops helps business leaders to understand the various ways to achieve their desired KPIs with accurate and fair data science models. Business leaders can ensure that the AI solutions are used to their full potential and can provide feedback to help refine and improve the AI solutions.
Business Benefits of Model Ops
- Reduced Cost:
Model Ops helps organizations reduce the cost of AI/ML models by automating the deployment, management, and monitoring of models. This helps to reduce the manual effort and time needed to deploy and manage models, as well as to reduce the cost of manual debugging and troubleshooting.
Additionally, ModelOps provides automated monitoring and alerting capabilities that enable teams to detect and fix issues in real time, reducing the cost of downtime and related losses.
- Reproducibility:
One of the main benefits of automating machine learning (ML) workflows is that it provides reproducibility and repeatability in many aspects, from data versioning to model versioning for model ops. This is crucial for industrial applications of ML, where model performance must be constantly monitored and improve efficiency.
Another benefit of automating ML workflows is that they can automate repetitive tasks such as data pre-processing and model tuning. Automated workflows can also help manage experiment tracking and results in storage, which can save valuable time when trying to reproduce previous results or track the progress of ML projects.
- Improved Decisions:
Model Ops helps to ensure that models are always up-to-date and accurate with round-the-clock monitoring, retraining, and deploying models on a regular basis. This makes businesses take informed data-driven decisions and present them with confidence to stakeholders.
With automated decision-making, businesses can eliminate the need for human intuition and make sure that decisions are made in line with the company’s goals and objectives.
- Increased Security:
Model Ops can help detect and classify cyber threats, identify malicious behavior, and provide alerts and incidents to resolve them before they impact the business. Additionally, these models can be used to create automated systems that can monitor the network and detect any suspicious activity, and secure data quality and integrity. This is critical for organizations that rely on AI for critical tasks.
Model Ops Implementation
For a model to get life from a business idea to a market product, several steps and processes are needed.
- Create a governance framework that aligns with the business objective of the model and adheres to the compliance standards.
- Identify the problem you want to solve with AI and assess whether the model can help the business achieve its goal with the best time-to-money ratio.
- Identify the roles and responsibilities of the members involved in the project.
- Select the algorithm, hyperparameters, and feature engineering for the model
- Identify the dataset to train the model and ensure that all data used for model training and testing is of high quality and is securely stored.
- Build the model or reuse the code from the previous template and validate the model by tuning the hyperparameters
- Create an automated CI/CD pipeline to streamline the model testing and deployment process.
- Monitor and maintain them in production on a regular basis to ensure that the model is performing as expected
The rapid implementation of various new applications, cloud services, and other technologies has complicated IT environments too much for humans to handle, negatively affecting profits. It is difficult for companies to pursue digital transformation in a highly competitive market despite the significant investments they’ve put into artificial intelligence. They cannot streamline all organizational models, making it difficult to gain valuable insights from the models and make informed business decisions.
Scaling all available models from the dev region to the CI/CD pipeline to the deployment region can be a challenging process for DevOps teams within traditional siloed environments. The challenge gets even more difficult when you have to monitor and manage all these models in production for performance, drift, bias, and other risks. All this while adhering to international and local regulations until the model decays or is retired.
The Model Ops solution solves the problem of scalability by operationalizing all AI/ML and decision models in production. It also focuses on model monitoring and governance throughout the ML life cycle. Automating all these processes with Model Ops can increase observability and help businesses focus better on their business goals.
Let us discuss in this blog in detail Model Ops and how they help businesses overcome challenges associated with AI implementation without affecting their ROI.
Model Ops
ML teams in organizations design the business objective and develop, validate, and deploy each model in production with the DevOps team. But there is no proper approach.
- To scale all the models throughout the enterprise and manage them across their ML life cycle.
- Create reliable and automated CI/CD ML deployment pipelines to streamline the model deployment process
- Monitor models in production and ensure that AI model compliance standards are met.
A practical solution to resolve these problems is “Model Ops.” It is a framework for AI organizations to operationalize their AI analytics. Model Ops can help to streamline the AI/ML model lifecycle by automating deployments, monitoring models, providing end-to-end governance, and continuous improvement of data analytics models that are created in the organization.
Figure 1. Model Ops
Model Ops vs ML Ops
ML Ops is a data science platform designed to streamline the process of building, training, and deploying ML models while managing and monitoring model performance in production. It combines machine learning, software development, and operations that enable organizations to deliver high-quality models in a production environment with reduced time to market and increased accuracy.
Model Ops is a set of practices and tools that provide governance and operations for models in production. This includes enabling organizations to deploy, monitor, and manage machine learning models with greater visibility, control, and automation. It helps organizations to ensure that models are reliable, performant, and compliant with best practices and regulations.
MLOps automates the ML lifecycle, from creating models to deploying and monitoring them. It also improves collaboration between data scientists and operations teams, helping to ensure that models are deployed and maintained in a secure and efficient manner.
Model Ops helps businesses improve their operational efficiency by leveraging data-driven models and advanced analytics to manage predictive models with a focus on providing actionable insights that can be used to optimize decision-making and overall performance.
ML Ops focuses on enabling collaboration between data scientists and IT teams in development, operations, and production environments to quickly and reliably deploy ML models, track the performance of those models over time, and quickly iterate on improvements to maximize their business value.
Model Ops helps business leaders to understand the various ways to achieve their desired KPIs with accurate and fair data science models. Business leaders can ensure that the AI solutions are used to their full potential and can provide feedback to help refine and improve the AI solutions.
Business Benefits of Model Ops
- Reduced Cost:
Model Ops helps organizations reduce the cost of AI/ML models by automating the deployment, management, and monitoring of models. This helps to reduce the manual effort and time needed to deploy and manage models, as well as to reduce the cost of manual debugging and troubleshooting.
Additionally, ModelOps provides automated monitoring and alerting capabilities that enable teams to detect and fix issues in real time, reducing the cost of downtime and related losses.
- Reproducibility:
One of the main benefits of automating machine learning (ML) workflows is that it provides reproducibility and repeatability in many aspects, from data versioning to model versioning for model ops. This is crucial for industrial applications of ML, where model performance must be constantly monitored and improve efficiency.
Another benefit of automating ML workflows is that they can automate repetitive tasks such as data pre-processing and model tuning. Automated workflows can also help manage experiment tracking and results in storage, which can save valuable time when trying to reproduce previous results or track the progress of ML projects.
- Improved Decisions:
Model Ops helps to ensure that models are always up-to-date and accurate with round-the-clock monitoring, retraining, and deploying models on a regular basis. This makes businesses take informed data-driven decisions and present them with confidence to stakeholders.
With automated decision-making, businesses can eliminate the need for human intuition and make sure that decisions are made in line with the company’s goals and objectives.
- Increased Security:
Model Ops can help detect and classify cyber threats, identify malicious behavior, and provide alerts and incidents to resolve them before they impact the business. Additionally, these models can be used to create automated systems that can monitor the network and detect any suspicious activity, and secure data quality and integrity. This is critical for organizations that rely on AI for critical tasks.
Model Ops Implementation
For a model to get life from a business idea to a market product, several steps and processes are needed.
- Create a governance framework that aligns with the business objective of the model and adheres to the compliance standards.
- Identify the problem you want to solve with AI and assess whether the model can help the business achieve its goal with the best time-to-money ratio.
- Identify the roles and responsibilities of the members involved in the project.
- Select the algorithm, hyperparameters, and feature engineering for the model
- Identify the dataset to train the model and ensure that all data used for model training and testing is of high quality and is securely stored.
- Build the model or reuse the code from the previous template and validate the model by tuning the hyperparameters
- Create an automated CI/CD pipeline to streamline the model testing and deployment process.
- Monitor and maintain them in production on a regular basis to ensure that the model is performing as expected
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