Introduction to ModelOps Platform
It was 1913 when Henry Ford put forward the assembly line design for the first time. It increased the production of his automobile factory, saving thousands of hours of manual work and energy for the workers. What Henry Ford did can be termed workflow optimization or even building a pipeline. 100 year later, people are trying to do something similar, but the difference is we now have more complex problems and better technologies, and huge amounts of data to assist us.
Enterprises are shifting towards applying Data Analytics and Artificial intelligence to automate their workflow, increase revenues, forecast sales, build new services, or solve industry problems. But this shift towards data-driven technologies does not always lead to a better outcome. Enterprises tend to overestimate AI's value and underestimate its cost. The problem is not in AI and Analytics but the gap enterprises often overlook. Developing a model and operationalizing it is not essential to generate value for the enterprise.
ML models are increasingly used by Enterprises to transform enormous amounts of data into new insights and information. Click to explore, ModelOps and its Operationalization
What is the significance of life cycle models?
According to The State of the Model Ops 2021, enterprises consider risk management and integration with software and applications the two biggest hurdles in operationalizing an AI model. It is crucial that the enterprise sees through the whole process of a model's life cycle and does not reach a dead end once the model is deployed.
Life-cycle of a model usually (not necessarily) follows the following pattern:
- Understanding the business problem and transforming it into an ML problem.
- Data collection (what data is required, from which source, architecture for data storage and processing)
- Selecting and training the ML model
- Model testing
- Deployment on the selected platform
- Model monitoring ( to check performance and model drifting )
- Re-training ( in case needed )
- Risk assessment
- Measuring the value, they provide to the enterprise
Models are often made to go through regular risk assessments to ensure that their results align with the enterprise/user requirements and do not cause risk to assets or people. It is necessary to assess the model's value compared to its built and maintenance cost to ensure that it provides value to the enterprise. If that is not the case, models are often modified or discontinued.
Why are ModelOps Platforms important?
From model selection to deployment to monitoring and maintenance, a model's lifecycle includes a lot of operations. One team cannot perform these operations, and it needs enterprise-wide collaboration and management.
- The model ops platform provides an ecosystem to handle each step of the model lifecycle to ensure that the time and resources invested in the model do not go in vain.
- Governance of the process provided by ModelOps platforms leads to transparency and security of the data and processes as authorized personal can check and maintain the access and model production at any stage of the project.
- Regular monitoring and maintenance of the model are necessary because models often lose their accuracy as they encounter more varied data, known as "model drifting." Re-training the model in the future is possible if model performance is monitored regularly and the cause of failure or inaccuracy can be studied.
- Enterprises can have all the expertise and tools to build a great model, but it will be useless if they cannot put it into production to interact with real-world data.
The common challenges organizations face while productionizing the Machine Learning model into active business gains. Click to explore, MLOps Challenges and Solutions
What are the most common ModelOps Tools?
Some common tools that are used in ModelOps include:
- Version Control Systems: Git, Mercurial
- Model Serving Frameworks: TensorFlow Serving, PyTorch Serving, ONNX Runtime
- Model Deployment Platforms: AWS SageMaker, Azure ML, Google Cloud AI Platform
- Containerization Platforms: Docker, Kubernetes
- Workflow Orchestration Tools: Apache Airflow, Luigi, Argo
- Monitoring and Logging Tools: Prometheus, Grafana, ELK (Elasticsearch, Logstash, Kibana)
It's important to choose the right tools for your specific needs, as the needs of different organizations and projects can vary significantly.
Track and understand the model performance in production from both operational data science perspective.Click to explore, Machine Learning Observability and Monitoring
Conclusion
Model development is an intricate process. It does not rely on just the AI and Analytics development team. The process requires enterprise-wide collaboration between MLOps, ITOps, DevOps, etc. The reason enterprises fail to scale AI models and operationalize them is because they are not aware of the Model Ops architecture and their development teams work in isolation, or they have knowledge about ModelOps but cannot develop an ecosystem around it. Models Platforms are services that solve the above problem so enterprises can save resources on building and monitoring their models. These platforms are flexible enough to cater to various models and their requirements while also automating the processes.
- Explore more about Data Intelligence vs Data Analytics
- Learn more about MLOps Roadmap for Model's Interpretability
