Machine Learning Operations is streamlining AI/ML projects like what DevOps did for Application projects
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What is Machine Learning Operations (MLOps)
A solution to effectively manage AI/ML models & projects
While DevOps is a set of practices that combines development and operations to enable continuous high-quality software delivery, MLOps is a set of practices that combines ML and data engineering with DevOps. It is a basic component of ML engineering that focuses on optimizing the process of deploying ML models, as well as maintaining and monitoring them.3 Key Components of MLOps
1. DATA & FEATURE MANAGEMENT
Iteratively explore, share, and prepare data for the ML lifecycle by building repeatable, editable, and shareable datasets, tables, and visualizations.Make the features accessible and shareable across data teams from inception to rollout by leveraging solutions like a feature store.
2. MODEL TRAINING & REVIEW
Train & enhance model performance by leveraging tools including available open-source packages. Automated ML technologies may also be used to execute trial runs and generate code that can be reviewed and deployed.Keep track of lineage, versions, artifacts, and transitions of ML models. Collaborate, discover, and share across models throughout the lifespan of an AI/ML model.
Track the impact on the models due to discrepancies in training data by continuous monitoring, notifications, and potential automation of corrective actions.
3. MODEL DEPLOYMENT & MONITORING
Keep track of model refresh frequency, inference request timings, and other production-specific details. For testing and quality assurance, use Continuous Integration and Continuous Deployment (CI/CD) solutions like repositories and orchestrators to automate the pre-production workflows.CI/CD techniques can also be used to regularly check and optimize training data and enable continuous improvement of the model.
MLOps Usage in RTS'
RTS leverages MLOps extensively in its Cloud management solution (Juno) where AI/ML solutions are used to efficiently manage cloud operations. By using MLOps from initiation to rollout, RTS enables traditional IT operations to transform into DevOps and Site Reliability Engineering (SRE)-based operating models. Read more about Juno, an intelligent cloud management platform
Why MLOps
ML systems comprise many components related to data, infrastructure, and environments—in addition to ML Models. These components need to be managed effectively from inception to roll out with the right set of practices and tools for successful execution AI/ML systems and projects.
Another key factor in AI/ML projects is the need to manage ML models across the lifecycle from conceptualization to development and deployment. This evolution of models necessitates the use of tools for continuous development & integration, enabling iterative development of the models in an agile environment.
In addition to the technical aspects of AI/ML systems, collaboration among data scientists, data engineers, application developers, site reliability engineers, business analysts, and business users play a key role in operationalizing the ML models.
So, enterprises can translate their vision of AI/ML driven transformation programs into a reality not only by developing game-changing AI/ML solutions but also by using MLOps to manage system components, model evolution, and team collaboration.
Another key factor in AI/ML projects is the need to manage ML models across the lifecycle from conceptualization to development and deployment. This evolution of models necessitates the use of tools for continuous development & integration, enabling iterative development of the models in an agile environment.
In addition to the technical aspects of AI/ML systems, collaboration among data scientists, data engineers, application developers, site reliability engineers, business analysts, and business users play a key role in operationalizing the ML models.
So, enterprises can translate their vision of AI/ML driven transformation programs into a reality not only by developing game-changing AI/ML solutions but also by using MLOps to manage system components, model evolution, and team collaboration.
Two Key Challenges in Machine Learning Projects
DEPLOYMENT & MONITORING ISSUES
An organization can have multiple teams working on a project and writing code in multiple languages to build models. These models are sent to the IT team for deployment; however, at times, they do not make it to production. This creates a backlog of models waiting to be deployed. A standardized model is required to make this process more efficient and requires data scientists to spend a lot of time troubleshooting.The accuracy of ML models can deteriorate over time. The models deployed need constant monitoring to keep the system efficient. All deployed models need to be monitored, kept in sync, and refreshed periodically. Manually evaluating the health of an ML model is very time-consuming as there is no centralized way to view model performance across the entire organization.
GOVERNANCE & LIFECYCLE ISSUES
Various deployment techniques and modeling languages exist as a result of the lack of a centralized perspective of AI in production across an enterprise. To achieve compliance, businesses require time-consuming and costly audit processes.ML models in production might suffer from poor performance caused by improper coding and frequently changing data characteristics. Models can decay in more ways than traditional software systems.
How Does MLOps Help?
MLOps provide a technology backbone for managing the ML lifecycle through automation to scalability, allowing enterprises to solve issues regarding return on investment for AI projects.
Key Benefits with MLOps
EFFECTIVE COLLABORATION & TRACKING
MLOps establishes mechanisms to effectively transfer tasks from one department to another.Employees are able to observe past, current, and future developments as a project progresses through its lifespan.
A paper trail is created, and it provides seamless documentation for actions performed and by whom (which is useful for feedback and process improvement).
STREAMLINED PROCESSES & LIFECYCLE
MLOps establishes mechanisms to effectively transfer tasks from one department to another.Employees are able to observe past, current, and future developments as a project progresses through its lifespan.
A paper trail is created, and it provides seamless documentation for actions performed and by whom (which is useful for feedback and process improvement).
CLONEABLE WORKFLOWS & MODELS
MLOps helps to decrease volatility in model iterations and provides robustness for enterprise-level situations through repeatable learning and models.Dataset registries and advanced model registries help keep track of resources.
Organizations can improve model delivery consistency by tracking and creating ML pipelines to develop, deploy, and govern repeatable model processes.
The creation of repeatable models can be used as a benchmark at the start of each new project.
Efficient Deployment & Implementation
MLOps makes deployment and implementation easier with improved communication, the ability to create processes, and simpler automation.Robust Governance & Resource-Management
Version history and model origin enable auditing models. This allows for the evaluation of features and the creation of more advanced models.Policies are enforced to ensure compliance with security, privacy, and compliance standards and audit trails are created to meet regulatory requirements.
RTS'
MLOps Services
As part of our AI/ML services, RTS offers MLOps solutions and services that provide DevOps-like speed and agility to the ML lifecycle and empower large enterprises to overcome barriers in deploying and operationalizing AI/ML across the organization.
RTS’ MLOps solution consists of the following key components:
Model Building
Hybrid Deployment
Model Monitoring
Model Deployment
Collaboration
Security and Control
Model Training
Below is an illustration of our AI & ML Execution Lifecycle:
Key Benefits RTS’ MLOps Services
Create new environments quickly and onboard new data scientists faster
Reduce accuracy losses and performance degradations by increasing collaboration and reproducibility
Reduce risk with comprehensive model governance and auditability
Ensure critical applications do not fail through high availability deployments
Provide higher flexibility and elasticity by auto-scaling clusters based on dynamic workloads
