The demand for AI and machine learning in the enterprise sector is on the rise, with a survey revealing that a majority of companies plan to increase or maintain their spending in this area. However, many companies face obstacles when it comes to deploying AI solutions in production. A recent poll conducted by Rexer Analytics highlights the challenges faced by organizations in implementing AI, including the lack of MLOps tools.
Here are the key points from the article:
– A survey indicates that approximately two-thirds of companies are planning to increase or maintain their spending on AI and machine learning.
– Despite the growing interest in AI, many organizations struggle to deploy AI solutions into production.
– A poll conducted by Rexer Analytics in 2020 reveals that one of the major challenges faced by companies is the lack of MLOps tools.
– MLOps tools refer to the tools and practices used to manage and deploy machine learning models in production environments.
– The absence of proper MLOps tools can hinder the deployment of AI solutions and limit their effectiveness.
– Organizations need robust MLOps tools to streamline the development, deployment, and management of AI models in production.
– By investing in MLOps tools, companies can overcome the barriers to deploying AI and ensure the successful implementation of machine learning solutions.
The increasing interest in AI and machine learning among enterprises is a positive sign for technological advancements. However, it is crucial for organizations to address the challenges they face in deploying AI solutions. The lack of MLOps tools is a significant hurdle that needs to be overcome. Investing in robust MLOps tools will not only facilitate the smooth deployment of AI models but also enhance their effectiveness in real-world scenarios.
As the demand for AI and machine learning continues to grow, organizations must prioritize the development and implementation of MLOps tools. These tools play a vital role in overcoming the barriers faced in deploying AI solutions into production. By investing in MLOps tools, companies can ensure the successful integration of AI and machine learning into their operations, leading to improved efficiency and innovation.
Original article: https://techcrunch.com/2023/07/19/fedml-raises-11-5-to-combine-mlops-tools-with-a-decentralized-ai-compute-network/