MLOps is an engineering discipline that aims to unify ML systems development and deployment to ensure continuous delivery of the models in production. 

Many businesses of different sizes encounter the same challenging situation while moving from a successful PoC phase of an ML-based project into production. Managing such deployments at scale is not an easy task, and various bottlenecks are to be taken into account. Following are the major challenges that teams have come up with:

• Shortage of IT professionals with Data Science skills as well as developing and deploying scalable web applications
• Complexity and many dependencies with the data continuously changing, ensure the performance of the models and data governance.  
• Communication gaps between technical and business teams.
• Continuous risk assessment of models usage and taking necessary actions to keep existing models within acceptable accuracy and performance range. 

The current stage of the MLOps recognizes the following phases:

1. Framing ML problems from business objectives
2. Architect ML and data solutions
3. Data preparation and processing 
4. Model training and experimentation
5. Building automated ML pipelines 
6. Deploying models to the production systems
7. Monitor, optimize, and maintain models

Intellica’s data science expert team with our MLOps practitioners will be happy to assess your project and identify improvements to be implemented or to ensure scalability and continuous operations of the existing models.