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Introduction to ModelOps and CloudOps

In the last few years, there has been an increase in AI and machine learning (ML) technology. These technologies are getting implemented in hundreds of use cases in big companies, and it isn’t easy to monitor all this manually. At any enterprise level, the deployment of AI and ML models needs to be operationalized. There is a need for a methodology to reduce manual efforts and streamline the deployment of ML models, so that methodology is ModelOps.

Transfer models as rapidly as possible from the lab to validation, testing, and production while assuring quality outcomes. Click to explore about, ModelOps in Artificial Intelligence Projects

CloudOps is a cloud-based business process filter that improves efficiency and flexibility. It can be defined as management systems that help simplify infrastructure services and cloud computing delivery. CloudOps relies heavily on analytics to provide the insights needed to run the software efficiently.

What is ModelOps?

AI ModelOps is a skill that focuses on managing the full life cycle and domination of all decision-making and AI models, including native language strategy models, agents, knowledge graphs, ML, and optimization. ModelOps also focuses on operationalizing all AI and decision models. Below is the flow chart of how ModelOps performs:

modelops workflow

Why is ModelOps important?

ModelOps provides key benefits for helping companies with lifecycle management and allows the final stretch of AI operationalization challenges.

Better risk management

ModelOps play a very important role in risk management and model governance which is becoming critical nowadays, while sending more models into production, mainly ModelOps platform enables to identify of risks and monitor model performance.

Visibility and insights

ModelOps tools provide easy-to-understand dashboards with key metrics, making it easy for organizations to evaluate and monitor the behavior or performance of the AI models. The dashboards clarify how teams are using and deploying AI over the organization.

Reduced Cost

The ModelOps platform reduces the resources, effort, and time required in a lifetime cycle. ModelOps reduces the amount of time it takes to move a model to prediction so that the model can be deployed in minutes, not months are required, and it also automates the workflow to reduce effort and time across the board.

ModelOps enables technology to converge multiple AI objects, solutions, and AI frameworks while maintaining scalability and governance. Click to explore about, Click to explore about, What is ModelOps and its Operationalization?

What is CloudOps?

CloudOps combines device management, security, network, help disk, performance, and other tasks that keep cloud-native applications and infrastructure running. Some see cloudOps as a continuation of Information technology operations (ITOps). Still, the best way to define the relationship between these two is that CloudOps uses IT operations applied to architects to accelerate and improve the business processes. CloudOps are not reactive; they are proactive as problems are solved before they occur. CloudOps helps diagnose the problem so you can solve the problem. With the rise of the cloud, organizations increasingly require dedicated cloud engineers.

cloudops-best-practices

What are the benefits of using AI in cloud computing?

Nowadays, IT operations have evolved over the last decade through which the companies such as Google, Amazon, Microsoft have removed much of the heavy lifting related to installing, storage, data center, and managing network, so increasing adoption of cloud and AI and ML are allowing companies to make decisions on predicted issues and known problems.

These are the top benefits of using AI in cloud computing:

Cloud Security Automation

The use of AI in the cloud has also increased the security of the cloud. AI could easily detect irregular events and obstruct them as soon as possible. It also helps in preventing unofficial access to the cloud environment.

Seamless Data Access

AI collects the data as inputs for making agile decisions and advanced performance. A cloud environment with AI learns from the data it gathers, then it makes predictions and troubleshoots the problem before they occur.

ML and AI also collect seamless data for transferring that data between the on-premises infrastructure and the cloud environment. Organizations that use cloud computing AI have the scalability and data insights to set drive modernization and industry standard.

Data Mining

Data mining is interpreting and exploring huge blocks of data to extract meaningful trends and patterns. AI helps manage large volumes of data to find out the meaning of the data. Through this, it will increase the responsiveness of the cloud environment and can also change the Artificial component of all the combinations.

A key factor for the successful cloud journey, not just during migration but also for ongoing optimization.Click to explore about, Click to explore about, Orchestration vs Automation

How does ModelOps help to execute AI strategy?

  • Is modelOps necessary? The answer is yes. As one knows, companies are spending millions of dollars on applications, data scientists, and software engineers every day.
  • What are ways to handle this type of complexity? AI ModelOps is doubtless at the heart of AI strategy.
  • ModelOps is a comprehensive approach to making ML and Predictive analytics workflow in action.
  • ModelOps is a practice whose goal is to automate a common set of operations in Data Science projects, testing, distributions, data management, model training pipeline, version control, and experiment monitoring.
  • ModelOps also aims to initiate all the AI, Predictive Analytics, and ML models. ModelOps helps in making automated test procedures that detect the coding error while delivering the project pipeline.

AI for cloud Operations

In cloud computing, AI capabilities are riding a big wave that is making organizations better automated and more cost-efficient.

AIOps cloud use case in action.

  • Observe - Traditional IT operations during the cloud era is a non-effective and complex task to achieve the service level objectives(SLO). There is an AI-based CloudOps that helps the IT teams have better event reduction and faster decision-making capabilities to solve this problem. The observer part of the cloudOps builds on top of KPI and log analytics. This KPI is then used for cross-domain and multivariate KPI anomaly detection to derive meaningful events.
  • Engage - The engagement in Artificial Intelligence operationalization (AIOps) is uninterrupted, enabling the IT support team to have better IT service management and IT user engagement process. The AI-based CloudOps enables an automated way to resolve the root cause of events and the anomalies. The AI for cloudOps provides more understanding, including cloud service, agent performance analytics, Service Level Agreement (SLA) analytics, change risk analytics, and IT service Analytics.
  • Act - The act is also known as the automation phase. The cloud-native application releases patch installation automation, automation, and cloud infrastructure as a code deployment possible with AI-based cloudOps tools. The AI for clouds also helps automate cloud service provisioning, request fulfillment, and incident remediation.
  • Govern - To continuously manage the working of the entire business organization is difficult. So for this CloudOps governance model is the best key for the solution. The governance functionality helps the Combined intelligence objectives (CIOs) manage and analyze governance in one place, security, cloud cost, usage, and potential vulnerabilities that put the cloud environment at risk. It also controls the usage and creates visibility across used services to achieve maximum cost-effectiveness.
Streamline organization’s capabilities for Managing and Deploying Machine Learning Models, and build AI-enabled Cloud Solutions.Click to explore about, Click to explore about, Explainable Artificial Intelligence Principles and ModelOps

CloudOps vs ModelOps

ModelOps

CloudOps

Monitoring Model - As in large enterprises, monitoring each and every different model can be challenging. So here ModelOps helps to monitor all kinds of models. It also looks for any possible problem and then learns how to resolve it.

CloudOps- It is a specific consumption, management, and delivery of software where there is finite clarity in app architecture.

Deploying Model - There is one advantage to the modelOps platform once the models are built on the ModelOps platform they can be integrated into any application easily and can send and deploy models virtually anywhere.

CloudOps helps to achieve the goals in any business while delivering, deploying, and building the cloud services. It uses DevOps to create some greatest practices for accomplishing better business processes.

Generating Model Pipeline - ModelOps platform generates a pipeline automatically as once the first setup is done, the whole modeling lifecycle selecting models, preparing data, feature engineering, modeling life cycle, and hyperparameter optimization will be automated with a few clicks and the least human interaction.

CloudOps helps in analyzing the patterns for determining problems and root causes, It is also able to find patterns in the data which are being collected. More importantly, it also predicts the issues that are likely to occur.

One-step solution - As all know Data Scientists build the model, then IT professionals use a platform to deploy these models. So this process usually reduces the time to market and takes a lot of time. So here comes the ModelOps platform where teams can manage, run and build the model easily.

ClouOps correlates a large amount of system noise in meaningful ways which include finding out the pattern. For example where the data is coming from and group the data before the data can be analyzed in some deeper ways. 

Java vs Kotlin
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Conclusion

It is an era of automation, and it is not limited to production. Today, systems of all kinds are automated to reduce human error and increase efficiency. And the days of a single project team that keeps the system from end to end are also gone. So the deployment of the model needs to be operationalized, so for that, there is a need for a methodology which is ModelOps. As cloudOps are proactive, not reactive, as problems are solved before they occur. CloudOps help to identify the problem so that the problem can be resolved. So increasing cloud and AI and ML adoption allows companies to make decisions on predicted issues and known problems.

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