Machine Learning Operations: How to Overcome Common ML Challenges

Adopting machine learning in an organization offers significant benefits, opening the door to automation, artificial intelligence and more accurate data modeling. But it’s not without its challenges – deploying models, incorporating automation and accuracy of data – which can hamper machine learning being accepted across an organization.

An answer to overcoming these common ML challenges can be found with Machine Learning Operations. By applying providen DevOps methods, MLOps speeds innovation, reduces time to deployment and drive accuracy and efficiency of machine learning and can immediately improve an organization’s ability to rapidly build and deploy ML solutions.

The automation of machine learning pipelines by extending DevOps techniques addresses the most common challenges of building and deploying ML models. ML pipelines are workflows, defined by a data scientist, that select ML algorithms, tune algorithm parameters, generate new features, and evaluate the performance and accuracy of the resulting models. Once defined by the data scientist, the MLOps pipelines automate all of the effort needed to rebuild and enhance models whenever new training data is available.

Benefits to MLOps Adoption

MLOps gives leaders, data scientists, developers and enterprise architects significantly new machine learning benefits.

1. Automated testing and deployment of machine learning models
   Once data scientists define the data and ML pipelines, MLOps tools automatically build, test and deploy the data pipelines and models into secure production environments. In the past, deployment of models presented challenges to data scientists and developers. MLOps assists every step of the ML process.

2. Greater use of artificial intelligence software and frameworks
   With MLOps, data scientists rapidly deploy models that leverage large and innovative ML libraries directly, without developers refactoring that code-base into more traditional but less capable programming paradigms. By automating the MLOps build-test-deploy activities, it overcomes the traditional hand-crafted ML models that were passed on to developers to recode-test-deploy, speeding deployment while using far more ML features.

3. Larger, automated data pipelines
   Machine learning depends on large amounts of data, transformed by multi-stage data pipelines. Automated data pipelines capture the hard work data scientists and data engineers perform to create data suitable for ML. Industry leaders routinely estimate that data preparation takes up to 80% of the effort needed to build ML solutions. By automating the data pipeline, we free data engineers from that task so thay might take on additional data wrangling projects.

4. Model development tasks move from manual to automated
   Often done manually by data scientists, machine learning tasks such as data transformations, algorithm selection and model training are automated with ML pipelines. This automation signicantly reduces the level of effort, freeing data scientists to take on more ML models in other mission critical areas.

5. More accurate models
   Many ML models lose accuracy over time because models are built by learning from historical data. If the data or underlying external systems change over time, models lose their accuracy. MLOps monitor models for proper behavior. If model accuracy drifts, the MLOps process automatically reruns all data pipelines and ML pipelines to create and deploy more accurate models for greater accuracy and trust in the ML solution.

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