The ModelOps is all about managing the lifecycle of complex Artificial Intelligence and Machine learning models to reduce the complexity associated with the actual model development/deployment process. Due to large scale adoption of AI and ML based prediction in the industry the ModelOps became preferred tool by the Data Scientists around the world. The ModelOps technologies are going to boom in coming years and we will see tremendous use of it in coming years. In 2021 ModelOps will become top trending technologies in the Artificial Intelligence and Machine Learning landscape.
The ModelOps is a discipline related to the popular DevOps, which is used for managing the complex lifecycle of machine learning (ML), artificial intelligence (AI) and other models. The DevOps coordinates between model development and deployment processes. It helps in streamlining the model development, training, testing and deployment of complex models. It handles all the steps from model training to model deployment in the production environment as per the rules defined by the Data Scientist.
The ModelOps uses AutoAI and other DevOps technologies such as continuous integration and continuous deployment (CI/CD) for updating the models on the regular basis, which gives better results to the business. This way the complex model training and deployment task is automated and it becomes much easier to run the CI/CD pipeline for machine learning models. This pipeline can also be configured to evaluate the model before actually deploying on the production. This way business expert can use the AI model and evaluate it before deployment. This way business expert can deploy the model independently without Data Scientist help. Once model is developed and ModelOps pipeline is configured it can be handled by business experts. This makes ModelOps very popular and in 2021 it will be trending technologies in Artificial Intelligence and Machine Learning landscape. Further the use of ModelOps will grow beyond 2021 and in coming year we will see tremendous opportunity in this field.
ModelOps is fast evolving technology which uses the pipeline concepts similar to the DevOps, where model training, testing and deployment are configured as pipeline job. This pipeline can be executed by one click in the pipeline management console. This makes complex process of model training jobs much easier for the Data Scientists. So, with the introduction of ModelOps artificial intelligence (AI) model development follows the same process as followed in the application development.
The AI/ML model development process is complex because it includes a lot steps to develop a functional model which can be used in the production. It includes the process like collecting the data from source, saving the data, processing of data for model training, training the model, validating the model, deploying model in production and finally developing the dashboard to visualize the results. So, these steps are complex and require lot of technological knowledge to compete all the steps. The ModelOps have been design to automate these complex processes where you can define the steps for data processing, model training, model evaluation, and retraining if necessary. Finally you can configure the steps into ModelOps to deploy your model into production. Once the ModelOps is configured for a model it can be run by just single click. The ModelOps environment keeps track of each step and show the status in the nice visualization to the administrator. Further ModelOps environment can be improved over time by modifying the pipeline processes.
The ModelOps is all about modernising and automating the today’s AI/ML model development process. ModelOps can be used to develop pipeline which can do the jobs quickly, at very large scale and with accountability. Such jobs can be run for mission critical business problems without any problem. So, ModelOps is a good choice to run the mission critical AI/ML pipelines in coming years 2021 and beyond. The ModelOps will help business in running their AI/ML pipeline in 2021 and beyond. There will be good demand for the Data Scientists having experience in setting up of ModelOps for artificial intelligence and machine learning projects.
ModelOps can be setup onsite and also move the process on the cloud to take advantage of high power computing and unlimited resources. To train the model ModelOps pipeline can be setup on the cloud hosting environment to meet the heavy processing requirements if it is not fulfilled in the in house setup. So, ModelOps will help in setting up the pipeline in house and then same can be used in cloud for heavy computing.
The ModelOps process will supersede the traditional means of data processing, model training, validating and deployment. This will help the business to streamline the model building process to get full advantage of artificial intelligence and machine learning for their business. It can be configured the model building/inference process to work with the traditional static data to streaming data. So, there is huge opportunity in the coming years and ModelOps will be a trending technology in 2021.
ModelOps can be used to deploy the models on Edge devices, Cloud Environment and AIoT devices. It can also be used to run the model training for Supervised Learning, Reinforcement Learning, Unsupervised Learning, Deep Learning and Robotic Process Automation. All these can be done fast, with great accuracy and with accountability.
How to learn in ModelOps?
To learn the ModelOps you should learn the principles of DevOps and then apply the knowledge to Machine Learning (ModelOps) to ease artificial intelligence application development. On the Amazon AWS you can use Amazon SageMaker to develop ModelOps pipeline. Check more at DevOps Tutorials section.
Deepak Kumar is Science Graduate from Delhi University with more than 18 years of experience in Technical field. Currently his interest lies in researching for Media and Journalism field. He has years of rich experience in various technological fields. With a background in Science and Media field, Deepak has been offering services in the media houses and technical research. He has worked as director and chief in many companies. As a technical writer, editor and reviewer, he is offering services to many research organizations, media houses and online educational portals.
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