Gartner predicts that improvements in analytics and automated problem-solving features will shift 30% of IT operations tasks from support to continuous engineering. One way to achieve this is to streamline IT operations by adopting ITOps. IT operations is a term that gained popularity over the years (See Google Trends Graph above ).
The number of large language models (LLMs) has been increasing since 2019 due to the models’ extensive application areas and capabilities (See Figure 1). Yet, the estimates show that designing a new foundation model can cost up to $90 million while fine-tuning or enhancing existing large language models can cost $1 million to $100 thousand.
Large language model (LLM) applications are increasing after business users realized the language generation capabilities of GPT models like ChatGPT. Generative AI technologies Enterprises can benefit from these advantages by developing a new foundation model or fine-tuning an existing LLM. LLMOps platforms facilitate these activities, lowering operational costs and enabling less technical personnel to complete them.
Adoption of artificial intelligence and machine learning in the enterprise skyrocketed after the pandemic (Figure 1) as AI and ML are changing industries and how businesses function with hundreds of use cases. Figure 1.
90% of top businesses have an ongoing investment in AI and 79% of executives say AI makes business processes easier. However, developing and deploying AI and ML models in enterprise has its challenges. The process requires constant effort to achieve efficient and reliable functioning of an ML system, or ML projects are prone to failures.
AI is perceived as a strategic priority by 83% of businesses, and 54% of executives agree that the implementation of AI improved productivity. However, 78% of the ML projects do not have a chance to reach the deployment phase.
Artificial intelligence (AI) and machine learning (ML) applications are changing every industry with hundreds of use cases.
According to a survey by Anaconda, data scientists spend nearly half of their time on data preparation. According to another study, it can take over a year to take a trained model into production (Figure 1) . Figure 1.
Developing a machine learning (ML) application involves lots of trial and error with different datasets, ML models, model parameters, or codes to achieve the best results. Once you want to scale your ML initiatives across your organization, managing different versions of multiple models can get pretty complicated.
The ability to replicate an experiment or a study and obtain the same results by using the same methodology is a crucial part of scientific method. This is called reproducibility in scientific research and it is also important for artificial intelligence (AI) and machine learning (ML) applications.