Moving an AI project from ideation to realization is a vicious loop, and there is only one way to resolve it – don’t let the loop begin! That is true because data deserves expert handling at all levels. Starting with extracting it from different sources to cleaning, analyzing, and populating it, machine learning systems are prone to latencies if the underlying architecture lacks an operational approach to ML – known as MLOps.
Most AI projects do not make it to production due to a gap that sounds very basic but has a massive impact: improper communication between the data scientists and the business. This survey from IDC focuses on the importance of continuous engagements between the two verticals. It has compelled organizations to look for immediately available solutions, and that is where MLOps enters the scene.
However, developing, implementing, or training ML models was never the main bottleneck. Building an integrated AI system for continuous operations in the production environment, without any major disconnects, is the actual challenge. For example, organizations that have to deploy ML solutions on demand have no choice but to iteratively rewrite the experimental code. The approach is ambiguous and may or may not end in success.
That is exactly what MLOps tries to resolve.
Put simply, DataOps for ML models is MLOps. It is the process of operationalizing ML models through collaboration with data scientists to achieve speed and robustness. A company called Neuromation has a complete service model wrapped around strategizing the MLOps. The ML services provider emphasizes bringing data scientists and engineers together to attain a robust ML lifecycle management.
Apart from data scientists, the collaboration includes engineers, cloud architects, and continuous feedback from all stakeholders. Along the way, it emphasizes implementing better ML models in the production environment and creates a data-driven DevOps practice.
What more should be done? Read along.
Continuous integration (CI) & continuous development (CD) automate the building, testing, and deploying of the ML pipelines. They deploy a new continuous ML pipeline with newly engineered model architecture, features, and hyper-parameters. This deployed pipeline is further executed on new data sets. When given new data, the continuous automation pipeline implements a new prediction service. By this time, the output is a source code of the new components. These are further pushed to a new source repository on the intended environment.
The new source code triggers the CI/CD pipeline to build the new components followed by continuous unit and integration testing. After all tests have passed, the new pipeline is deployed in the targeted environment. The pipeline is automatically executed in the production environment as per pre-defined schedule and training data.
ML perfects huge volumes of data. That is why data feasibility is necessary to ensure appropriate volume and efficiency before considering it for in-the-moment forecasting. For example, the QSR (Quick Service Restaurant) system that processes data of millions of customers should have ML backing it. Here, not only the data is continuously growing but also changing in agility. So is the case of eCommerce landscapes that have numerous systems tied together such as last-mile delivery, CRM, and in-house ERP.
