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.
Source: Best practices to embrace an ‘MLOps’ mindset
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.
MLOps best practices focus on:
- Providing end-to-end visibility of data extraction, model creation, deployment, and monitoring for faster processing.
- Faster auditing & replicating of production models by storing all related artifacts such as versioning data and metadata.
- Effortless retraining of a model as per varying environment and requirements
- Faster, securer, and accurate testing of the ML systems.
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.
Perfecting the CI/CD pipeline automation
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.
Constructing lakes for convenient data assessment
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.
To start with, set up a data lake environment with seamless access to all the data sources. Just like a centralized warehouse, data lakes should be the epicenter of data assessment. This is the repository to filter and qualify data for MLOps processing and further to the data analytics landscape. To ensure that the data has enough value to build qualitative analytics and necessary business change, accommodating continuous experimentation becomes necessary. To ensure this, use a scalable computing environment that processes available datasets in a fast manner.
At the same time, lakes deserve an interactive dashboard for advanced visualization. Consider using tools such as AWS Quick Sight, Plotly Dash, and Power BI as examples of data visualization dashboards. These dashboards are easily customizable to suit varying business needs.
By the end of the data assessment, all the data sets are filtered and structured for future use. This is also the phase to include cataloging. Data catalogs are required for discovering and visualizing metadata structures and the lineage from source to consuming microservices.
Monitor predictive service and performance
Apart from the training, data, and model type, there are other metrics to determine the performance of the deployed model based on business objectives. To clock optimal output of the machine learning models, consider the following metrics:
- Latency: Evaluate seamless UX. Measure latency in milliseconds
- Scalability: The ability to handle service traffic for a particular latency. This is measured in Queries-per-second (QPS).
- Service Update: Ensuring minimum service downtime while updating.
Using data fabric
A data fabric is a framework to collect data from a multitude of sources and make it business-ready for the analytics staff. MLOps initiatives work closely with data fabrics across a diverse range of operational use cases on the cloud and on-premises. Since fabrics create a centralized flow of coordination, they mitigate risk and abbreviate the overall costs of big data management. Interestingly, organizations have used fabric as a foundation to upscale their DataOps initiatives.
K2View, for example, provides a data preparation hub that is built on its fabric technology. A data preparation hub captures the data from different sources, filters it, enriches, and masks it as per the re-defined schemas and rules. Here, a Digital Entity whose data is stored in an exclusive Micro-DB represents every customer. Such an approach of pipelining data by the business entity ensures integrity thereby delivering uninterrupted access to the teams.
Bonus tip: Choosing the right cloud architecture
Your data landscape is likely tied to a cloud application in some way. Given the increasing inclusion of cloud models in our enterprises, it is necessary to check the basics: Is the cloud platform in compliance with MLOps?
While most cloud platforms provide built-in data science capabilities, check if they support resilient and high-performing processing of end-to-end ML pipelines (storage, ingestion, modeling, visualizing, monitoring, etc.).
Here, infrastructure-as-code automates the provisioning of the ML environments that are scalable and reproducible. Just like on-premises, cloud platforms depend upon CI/CD for accurate ML model training and testing. Examples of ready-to-use cloud environments that support MLOps are AWS SageMaker, Original Postroducts/ai-machine-learning/introducing-cloud-ai-platform-pipelines" rel="noreferrer noopener" target="_blank">Google Cloud AI Pipelines, and Databricks.
Conclusion
This article walked through the key metrics to consider for an MLOps strategy. Since automation is a mainstream service, the next challenge for organizations will be to level up their ‘XOps’ skills. With MLOps, not only will they improve their engagement with the DataOps process, but they will also meet the expectations of the impatient customer.