Introduction to MLOps and ModelOps
Artificial Intelligence has grown exponentially in the last decade, and enterprises and organizations have adopted it to scale their business and make smart decisions. Tell us how important it is in today’s world. Despite growing organizations investing in Artificial Intelligence, data science, and machine learning, many organizations still struggle to transform their investment into real business value. In big enterprises, AI solutions have to be implemented in hundreds of use cases, so it becomes difficult to manage them manually. According to a survey by Independent Research Firm Corinium Intelligence, 80% say difficulty managing risk and ensuring compliance is a barrier to AI adoption for their enterprises.
Organizations need a framework that can automate the whole process of ML models or AI solutions that can reduce manual efforts. It helps organizations to implement AI solutions and manage and monitor their performance at scale. While it focus mainly on models from model building to model deployment and managing them. It is an extension of it.The newly emerged technique that includes people, processes, and technology that give an edge to swiftly and safely optimize and deploy machine learning models. Click to explore about our, MLOps Platform - Productionizing ML Models
What is MLOps?
It is a process of managing the workflows of ML models. It's a subset of ModelOps. It is the practice of building, evaluating, deploying, and maintaining ML models. It seek to unify ML workflow to standardize and streamline the machine learning life cycle. Nowadays, ML engineers have to manage their workflows in production.
Activities that are performed as part of ML Ops
Following are some activities :
- Model training/retraining
- Integration with data pipeline and model deployment
- Integrate ML models into productions
- Automate machine learning models lifecycle management
- Monitoring the performance of the model in production
- Updating the models when needed
What are ModelOps?
It helps organizations to implement AI solutions and manage and monitor their performance at scale. It also integrates DevOps, DataOps, and ITOps. It include the process, the operations, the tools, and technologies that enterprises can use to deploy to monitor and even govern their machine learning models. It helps automate repetitive tasks, and teams can focus on the matters.
It is an extension of MLOps, plus a few additional skills relating to IT operations, risk management, governance, and more. It will be one key to unlocking value with AI for enterprises. For instance, the AI pipeline has data management, data wrangling, model training, model deployment and management, and business applications. It is the connectivity tissue. It links the disparate piece of the pipeline to deliver value through business applications. An organization reduces risk, better resource allocation, and high model reuse by providing a shared tool to track and manage AI assets across all stakeholders.
Activities that are performed as part of ModelOPs:
Following are some activities:
- Making ML and AI workflow operationalize
- Automate the operations for AI solutions
- Automate all the processes, including model training pipeline, version control, data management, experiment tracking, testing, and deployment.
Why ModelOps or MLOps?
Machine learning differs from standard software because the core of the applications is data. If we look at the bigger picture, machine learning is a small part of the solution. The real work began after the model deployment. To achieve maximum performance, models in production must be constantly monitored, retrained, and deployed. It helps to automate all the processes.
- According to a KDnuggets poll, 80% of models are undeployed, which means only 20% of the models get into production and give real value to the organization.
- This buildup of undeployed models can eventually have a negative impact on the growth of the organization.
- To match up in real-time, models must be retrained on new data to give better performance and provide real value.
- With traditional methods, it is tough to manage all this. This is where It comes to the rescue.
- It is an extension of It, which includes continuous training, automatic updating, and synchronized creation of more sophisticated machine learning models in addition to routine deployment of machine learning models.
ModelOps vs MLOps
The significant difference between them is that ML Operations focus on only machine learning models. Model Operations focuses on all AI models and solutions. It is for people at a higher position in the organization because it provides a dashboard, reporting, and other information needed by business leaders to understand what is going on with the project. It helps to understand what's going on in the enterprises. It monitors the performance of models and helps in retraining when the need arises. It allows the team to manage infrastructure, as they can track and plan what will be needed in the future.
These are not competitive solutions. They are complementary solutions. Its solutions can not manage models' production throughout their lifecycle across the organization. It can not build models. Both these together to scale the business model.
Some MLOps solutions offer limited management capabilities, but they become evident when an organization begins to scale AI and enforce risk and compliance controls uniformly. Its platforms automate the risk, regulatory and operational aspects of models. It also ensures that models can be audited and evaluated for technical conformance, business value, and operational risk. With the help of these capabilities and the efficiency of its tools, enterprises can exploit the investment in their tools and build a foundational platform for accelerating, scaling, and governing AI across the enterprise.
Enables businesses to shorten production cycles and deliver results to end users at scale while continuously improving outcomes.Click to explore about our, Model Operations Monitoring Model
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MLOps |
ModelOps |
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It stands for machine learning operations. |
It stands for Model Operations. |
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It automate the process of MLworkflow. |
It helps workflows operational. |
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It is only about operationalization of machine learning models |
It is about governance and full life cycle management of AI and ML models. |
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Aims to create AI-enabled applications by enabling collaborations between different teams. |
Provide transparency into AI usage using dashboard, reporting to business leaders. |
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Primary users are data scientist, ml engineers. |
Enterprises risk, IT or Line of Business Operations |
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Model management, model monitoring and CI/CD |
MLOps functionality plus provide insight into AI models performance, management of all models not just ML models. |
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Feature in data science platform |
Enterprise capability |
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Tools: Amazon SageMaker, Neptune, DataRobot, MLFlow, and more |
Tools: Cnvrg, Cloudera, ModelOps, Modzy, SAS |
Conclusion
MLOps and ModelOps are complementary solutions. They are not competing with each other. It focus on building models, evaluating, deploying them, and Model Operations focus on governance and full life cycle management of AI and ML. If any organizations want to implement AI or machine learning, they would need both. So, the business scale faster, and more models will be deployed. It is used by data scientists and ML engineers, whereas it is for the organization's people at a higher level. It automate the process of ML workflow, and its operationalize the whole process. It provides a dashboard, reports, and more.
- Discover here about Machine Learning Pipelines
- Explore here about ModelOps vs CloudOps
