Artificial Intelligence (AI) has had a significant impact on the way the world works, and consequently, more and more organizations are interested in harnessing the power of AI. According to an MIT Sloan-BCG report, a high percentile (93%) of C-level executives believe that artificial intelligence can create valuable investments. 25% of the businesses that have adopted AI are in the mature stage, and most of them are in the assessment stage. However, AI adoption has not proved its worth within companies – 65% of those executives do not realize ROI despite significant investments in AI. Companies approach us for effective ML solutions, but the failure seems to stem from bad implementation of the AI approach.
In the entire AI development lifecycle, the responsibilities of data scientists and software engineers are clear – while data scientists focus on the model’s precision, software engineers are responsible for the deployment of the software. The distinct nature of these two roles has brought about a need for a role that can overlap the deployment and model precision functions. This role is now more popularly known as the ModelOps Engineer, who is responsible for the model’s scalability and delivery. To mitigate AI and Machine Learning (ML) adoption challenges, we always recommend that early AI adopters include this role in their projects from an early stage.
The field of IT DevOps includes a set of practices that empower constant delivery and a shorter development cycle. So, when these DevOps practices are applied to ML systems, we get MLOps. This is an ML engineering culture and practice that intends to bind together ML system development (Dev) and ML system operation (Ops). Implementing MLOps implies that you advocate for automation and monitor all phases of ML system construction that comprise architecture, implementation, deployment, and infrastructure management, testing, and releasing.
This article aims at emphasizing the architecture and core components of an MLOps platform.
The three significant components of MLOps are AutoML, ModelOps, and DataOps. AutoML aims at increasing and improving model creation while helping data scientists explore their ideas without hindrances. ModelOps allows the engineers to increase the speed of time-to-development while monitoring model-in-action. DataOps, on the other hand, describes the set of practices that improve data integration to address security and preprocessing issues.
Model Training Pipeline
This is the stage when Machine Learning Engineers create a training pipeline coupled with new features which allow them to fix any problems as they monitor the training process. Engineers for ModelOps test different parts of the pipeline so that they can use them as instruments for taking them into the target environment. Nonetheless, every time the pipeline does
- Data ingestion
- Data Preparation
- Model Training
- Model Validation
- Data Versioning
This stage enables ML engineers to collaborate and share models with Ops engineers for model management enhancement. A model registry acts as a centralized hub that stores all metadata for published models.
The model registry allows the growth of a communication layer that traverses between production environments and research furnishing the deployed model with the data it needs in runtime. It does all this while facilitating track changes.
In this stage, ML engineers will kickstart testing in production, whereas Ops engineers will control model deployment. Some of the common methods to put models in production are embedded in consumer applications, on an IoT edge device, and within a dedicated web service available through remote procedure call (RPC).
However, one new approach – Model-as-a-Service, is gaining popularity because of its simplified deployment and the ability to separate the ML part from software code. This ensures that you easily upgrade the version of the model without the need of redeploying the application.
TFX, MLflow, and Kubeflow put certain models together so that they can be incorporated into Kubernetes. These models, such as Docker images, are also merged on other special servers such as Clipper and TensorFlow Serving.
ML engineers use this stage to analyze metrics and understand system alerts. Until the model is given to production, the model performance might be influenced by numerous factors.
In certain instances, the accuracy is unacceptable, which means the algorithm must be retrained. Generally, ML models don’t show errors instantly, but their predictions can affect the results. Distorted insights can cause wrong decisions and encourage financial problems. To get away from all of this, we recommend companies deploy software solutions that will automatically identify anomalies, give early alerts, and trigger ML pipelines for retraining.
Here, Ops engineers bind multiple processes and automate the complete release process of the model. As we have all parts of MLOps flow in place, the next step is to orchestrate various operations such as performance of a training pipeline, carrying out tests and validations, and implementation of new model versions. For this, organizations should make use of CI/CD tools. These tools can visualize complex workflows and effectively manage the entire application lifecycle. This enables organizations to bring together pipelines easily.
Many organizations tend to neglect the need for a controller despite the above core components of an MLOps platform architecture. We believe the role of a controller is quite crucial as it oversees the challenges around governance, security, and best practices defined.
To end, MLOps is essentially the blend of the best practices’ pioneers have embraced after understanding the difficulties in executing AI. Our MLOps platform, Xpresso AI, accelerates your AI capacities and helps develop an edge over your competitors. With an MLOps platform, you can develop sustainable long-term practices, empowering a never-ending flow of benefits from AI.