Why Machine Learning Operations
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Enterprises face significant challenges in machine learning (ML) operations, including inefficient data handling and slow deployment times for ML models. Despite substantial investments, many organizations struggle with operational issues and lengthy transition periods, indicating a need for improved management processes. Gartner predicts that by the end of 2024, a majority of enterprises will shift towards operationalizing AI, which will enhance their data and analytics capabilities.
Why Machine Learning Operations
- 1. Inefficient data handling practices like manual data processing, validation and inference retrieval Periodic manual re-training and deployment of the ML models to accommodate the Major challenges faced by Enterprises in ML Lack of in-depth visibility of the model's performance as they interact with real-world events Lack of standard change management process to address the change request in ML pipeline ML Operations (MLOps) approach automates and monitors the entire machine learning lifecycle, enabling faster time to production of ML models “Launching ML pilots is deceptively easy but deploying them into production is from idea to production Source: The State of Development and Operations of AI Applications While 63.2% reported they are spending between $500,000 and $10 million on their AI efforts, about 60.6% continue to experience a variety of operational challenges Despite the significant spend dedicated to AI, 64.4% said that it is taking them between 7 and 18 months to move AI/ML models 28.4% stated that they rebuild the models every time they deploy them data drift pipeline and operations To overcome these challenges Enterprises need to shift from the current method of model management to a faster and more agile format. According to Gartner, “By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures” Inspite of spending more, Enterprises face numerous ML operational challenges notoriously challenging”-Gartner