Artificial intelligence is making noteworthy strides in almost every domain possible. It has provided wings to creativity and boosted analytic and decision-making abilities. In the past few months, Generative AI has become increasingly popular. From organizations to AI researchers, everyone is discovering the vast potential Generative AI holds to produce unique and original content, and that too in a wide range of fields.
What is Generative AI?
Generative AI is a common term for any type of process that uses an algorithm to generate, manipulate, and synthesize data. It can be explained as a subset of artificial intelligence which helps generate new data by learning from existing data. The new content resembles the existing data with some creativity and unique characteristics. The data can be in the form of images or human-readable text and generate something that didn’t exist.
How is Generative AI Being Used?
Generative AI has been evolving since its introduction at a great pace. The development of Large Language Models (LLMs) can be termed as one of the major reasons for the sudden growth in the amount of recognition and popularity generative AI is receiving. LLMs are AI models that are designed to process natural language and generate human-like responses. OpenAI’s GPT-4 and Google’s BERT are great examples that have made significant advances in recent years, from the development of chatbots and virtual assistants to content creation. Some of the domains in which Generative AI is being used are ? content creation, development of virtual assistants, human imitating chatbots, gaming, and so on. Generative AI is also used in the healthcare industry to generate personalized treatment plans for patients, improve the accuracy of medical diagnoses, etc.
What is MLOps?
With every company trying to inculcate the potential of AI ML into their services and product, MLOps has become popular. MLOps (Machine Learning Operations) is an essential function of Machine Learning engineering that mainly focuses on streamlining the process of putting ML models into production, followed by their maintenance and monitoring. It blends the features of both DevOps and ML to help organizations design robust ML pipelines with minimal resources and maximum efficiency.
Power of MLOps in Making Generative AI even better
Generative AI comes with the complexity of training and deploying the models, requiring massive computing resources and dedicated infrastructure. MLOps, when combined with Generative AI, can help address these challenges by providing a great framework for managing the development and deployment of generative AI models along with automating the processes involved. For an organization to improve its infrastructure, integrating MLOps can help them include features like parameter optimization, automated deployment & scaling to generative AI applications without any additional manual cost.
The primary benefits that MLOps offer Generative AI are efficiency, scalability, and risk reduction. Apart from this, MLOps can contribute in the following ways ?
- Data management: MLOps can help manage large volumes of data that are used for training generative AI models, making sure that the data is of high quality, diverse, and particular to the required domain.
- Model development: MLOps can help in the entire model’s development process, from training to testing and validation, including providing tools for version control, code review, etc.
- Deployment: MLOps can help automate the deployment of generative AI models, making production easier.
- Scaling ? MLOps can help in handling increasing volumes of traffic. This includes providing tools for managing infrastructure and the amount of data.
- Monitoring and maintenance: MLOps can help monitor the performance of working generative AI models by detecting issues, checking performance, anomalies, etc.
Generative AI is becoming increasingly popular due to the availability of more data, advances in computing technologies, and its ability to generate unique and innovative content. With the addition of MLOps, it can play a critical role in managing the lifecycle of generative AI models, thereby getting the most out of their products and applications.