As machine learning algorithms have advanced by leaps and bounds, the lack of standardized machine learning tools, frameworks and deployment practices remains a challenging task. This has resulted in low business value & usage of AI/ML models in production.
In this webinar, we will share our perspective on the need for standardization in the current machine learning lifecycle and discuss a comprehensive DevOps strategy for MLOps for organizations to successfully drive ML into production.
- Need for MLOps in the machine learning lifecycle
- Current challenges preventing organizations from successfully implementing enterprise ML in production
- MLOps best practices and success stories
- Collaborative tools available in the market to deploy predictive analytics solution
- Security and compliance for MLOps in healthcare