The emergence of machine learning (ML) as a transformative force across all industries has been driven by the realization of its value in implementing a data-driven strategy. However, despite its potential, analysts suggest that around 80% of ML projects fail. Reasons cited include biased data, algorithms, or the team managing them, along with cost, lack of expertise, and life-cycle management tools. To prevent these pitfalls, it is recommended to define business goals, acquire large volumes of good-quality data and assess for biases, choose the right ML approach, train models carefully, and monitor them efficiently. Additionally, organizations adopting ML should also consider implementing MLOps, whereby machine learning workflows are combined with software development and operations processes to optimize the deployment, management, and maintenance of models in production. Amidst all of this, enterprises must remain agile, adapt to new trends, and adopt responsible AI practices while reaping the transformative power of artificial intelligence.
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Steps to adopt MLOps:
1. Collaborate between development and deployment teams
2. Automate processes such as ML and CI/CD pipelines to accelerate development and deployment cycles
3. Create processes for efficient and reliable model deployment and continuous performance monitoring
4. Focus on automation and the early detection of major performance issues to prevent any delays in functionality and improve the productivity and quality of machine learning products.
