MLOPS 101: Tips, Tricks and Best Practices- Vladimir Osin | PyData Eindhoven 2021

MLOPS 101

• MLOPS (Machine Learning Operations) is a discipline that combines machine learning with DevOps practices to manage the lifecycle of machine learning models
• It’s about creating a culture of collaboration between data scientists, engineers, and product teams
• MLOPS is not just about tooling, but also about defining roles and responsibilities

Key Roles and Responsibilities

• Data Scientists: Should own the model development cycle and be responsible for model training and validation
• Machine Learning Engineers: Should be responsible for deploying and managing models in production
• Data Engineers: Should be responsible for providing infrastructure and data storage for machine learning workloads

Importance of DataOps

• DataOps is the discipline of managing the data pipeline and ensuring data quality, availability, and usability
• DataOps is essential for machine learning because it helps to ensure that data is accurate, complete, and consistent
• DataOps should be responsible for monitoring data quality and providing insights to data scientists and engineers

Challenges and Best Practices

• Challenge: Seamlessly integrating machine learning models into existing systems and processes
• Best Practice: Define clear roles and responsibilities, and establish a culture of collaboration between teams
• Best Practice: Implement a data governance framework to ensure data quality and consistency
• Best Practice: Implement automation and orchestration for repetitive tasks, such as data preparation and model deployment
• Best Practice: Monitor and measure the performance of machine learning models in production

Importance of Iterative Process

• Machine learning is an iterative process that requires continuous experimentation and improvement
• Iterative process helps to ensure that machine learning models are accurate, reliable, and efficient
• Iterative process should be continuous and ongoing, with regular updates and refinements

Importance of Execution

• Execution is key to success in machine learning
• Execution requires a clear understanding of goals and objectives, as well as a well-defined plan and budget
• Execution should be based on a clear understanding of customer needs and business goals