Differences Between MLOps, ModelOps, AIOps, DataOps

MLOps vs ModelOps vs DataOps

In this blog post, we will talk about MLOps, AIOps, ModelOps and Dataops and difference between these terms. MLOps stands for Machine Learning Operations, AIOps stands for Artificial Intelligence-Operations (AI for IT operations), DataOps stands for Data operations and ModelOps stands for model operations. As data analytics stakeholders, it is important to understand the differences between MLOps, AIOps, Dataops, and ModelOps. For setting up AI/ML practice, it is important to plan to set up teams and practices around AIOps, MLOps/ModelOps and DataOps.

What is MLOps?

MLOps (or ML Operations) refers to the process of managing your ML workflows. It’s a subset of ModelOps that focuses on operationalizing ML models that have been already created or are being actively used in production. MLOps is the practice of building, deploying and maintaining ML models using established engineering best practices such as CI/CD pipelines. MLOps has emerged because ML engineers are increasingly being asked to manage their own workflows in production rather than leaving it up to data or infrastructure teams.

What activities are performed as part of MLOps?

The following are some of the activities performed as part of MLOps:

  • Model training / re-training
  • Model deployment and integration with data pipelines or ETL workflows
  • Integrate ML models into production workflows and systems
  • Automate ML model lifecycle management (for example, versioning and releases)
  • Monitoring model performance in production, updating models when needed to reflect new information (for example, adding a new input feature)
  • Integrating ML results into business strategic processes such as decision-making for marketing campaigns or sales forecasting.

What skills are required for MLOps engineers?

MLOps engineers are different from data scientists in that they are often more comfortable with ML algorithms and software engineering tools than data science topics such as machine learning theory or statistics, although their skillset may include advanced knowledge of ML. You will require some of the following skills to perform MLOps:

  • ML engineering skills for building and training ML models.
  • Linux/Unix system administration skills to deploy ML models in production environments.
  • CI/CD pipeline experience so that you can maintain a version control system and produce artifacts such as Docker images, Helm charts or Kubernetes deployment files using automated build and deployment systems like Jenkins or Spinnaker. Knowledge of services such as AWS ECS, EKS and related services on Azure and Google cloud will be very handy.
  • When deploying models on the cloud, MLOps engineers will also need to be familiar with the cloud ML services available, for example, AWS SageMaker or Google Cloud
  • Monitor model performance metrics using ML monitoring tools
  • Provide self-service ML to business users through an ML platform that supports sharing of models across teams.

What is ModelOps?

ModelOps incorporates MLOps, which is the process of managing ML models throughout their lifecycle at an enterprise scale. ModelOps is considered as a superset of MLOps, which refers to the processes involved to operationalize and manage AI models in use in production systems. The advantage of ModelOps over MLOps is that MLOps focuses on the machine learning models only, whereas Modelops is focused to operationalize all AI models.

The organization looking to set up ModelOps should set up MLOps first before moving on to ModelOps. The skills required for ModelOps are the same as MLOps, with some additional skills pertaining to the entire gamut of AI.

The activities performed as part of ModelOps is very much similar to that of MLOps. Here is a great post on What is ModelOps and how is it different from MLOps?

What is DataOps?

DataOps helps achieve the goal of having the entire organization be data-driven. The DataOps team, similar to DevOps or Site Reliability Engineering (SRE), is responsible for developing and operating software tools & frameworks that help support data analytics initiatives across an organization.

DataOps is a reference of how Data Engineers and DevOps engineers work together.  The goal of the team is to focus on developing infrastructure that helps people throughout an organization be more effective with their analytics initiatives, whether they are ML or BI-focused. This means building tools like ETL pipelines and dashboard infrastructure, as well as the infrastructure needed to support ML and BI projects.

Similar to DevOps or SRE teams who focus on building tooling that enables their developers to be more productive, DataOps focuses on data engineers being more effective by giving them access to useful tools like ETL pipelines built for operating at scale. 

DataOps professionals can perform activities such as building tools (ETL pipelines) which are responsible for ingesting data into the data lake and then managing ETL pipelines across different storages (hot, cold, etc) in the data lake. 

What isn’t DataOps?

With DataOps as a term being thrown around so much, it’s important to understand what it isn’t first:

  • DataOps is not MLOps, which refers to ML Engineers being more effective.  Data Ops is focused on data engineers and making them more productive with ML projects in a team setting.  MLOps focuses on helping ML modelers be productive by giving them access to internal tooling that can help with hyperparameter tuning or cross-validation.
  • DataOps is not AIOps, which refers to the general trend of ML models being deployed on production infrastructure with ML Engineers using AI/ML tools for operational use cases like alerting or anomaly detection.  AIOps focuses on helping ML modelers be more productive by giving them access to internal tooling that can help with hyperparameter tuning or cross-validation.

What is AIOps?

AIOps (Artificial Intelligence for IT Operations) is the use of machine learning and other AI technologies to automate many processes that are currently done manually in an organization. AIOps is similar to MLOps in that it uses machine learning and other AI technologies to automate IT processes. It is different from MLOps in that the process automation occurs within an organization’s IT operations department instead of an organization’s machine learning and AI team. AIOps is also different from MLOps because it uses AI to automate many processes, not just one or two tasks like MLOps does.

AIOps enables an organization to incorporate ML and AI technologies into their IT operations. This is a significant improvement over the traditional manual use of these technologies in an organization’s DevOps efforts, as many problems were addressed by ML or AI could be automated but required weeks or months to accomplish. AIOps allows for faster automation so that organizations can benefit from ML and other AI technologies in their DevOps processes.

AIOps is a complex discipline to implement, as it requires ML and AI knowledge combined with thorough understanding of IT operations. It has the potential for huge benefits but without some serious investment up front on training, consulting or tooling can be challenging to get right within organizations that are already under pressure from their DevOps teams to do more with less.

The organizations planning to implement AIOps will need to have in place MLOps. This is because AIOps requires the organizations to already be using ML for automated tasks, and MLOps uses ML technologies like TensorFlow or SparkML.

Ajitesh Kumar

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
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