It is difficult for data scientists to categorize data and construct correct machine learning models, but managing models in production might be even more difficult.
Recognizing system drift, updating models with updated data sets, enhancing performance, and managing underlying technology platforms are all critical data science processes.
According to one machine learning survey, 55% of organizations have not released models into production, and 40% or more need more than 30 days to deploy a single model.
The lesson was that once machine learning techniques are deployed in production and used in business operations, new challenges emerge.
Model administration and operations were formerly considered difficult tasks for more advanced data science teams.
Monitoring operational machine learning algorithms for drift, managing model retraining, warning when drift is considerable, and recognising when models require updates are now jobs.
As more businesses invest in machine learning, there is a rising need to educate employees on model maintenance and operations.
The best part is that open source MLFlow and DVC, as well as commercial tools from Dataiku, SAS, Alteryx, and others, are making method management and operations simpler for data science teams.
Public cloud providers are also offering best practises, such as how to integrate MLops with Azure ML. Model management and DevOps share several commonalities.
Model management and operations (MLops) is a term used to describe the culture, techniques, and technologies required to construct and maintain a machine learning algorithm.
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