As we explored in our previous two articles, Devops is ill-suited for Machine Learning (ML), and DataOps is a better approach to provide the foundation for the last miles in the ML process. This is about ML model development, deployment, serving, monitoring, and management. Also known as MLOps, which sets forth DataOps and equally presents unique challenges that require specialized skills, knowledge, infrastructure, and processes. As we learned in the previous articles, these challenges make it difficult for DevOps teams to effectively manage the ML process, which is why dedicated Data and ML engineering teams along with DataOps and MLOps practices are a better approach. In this article we will take a deeper dive into MLOps.
MLOps in a nutshell
In a nutshell, MLOps is the combination of ML development and operations, with a focus on the end-to-end process of managing ML models, from experimentation and prototyping to deployment, serving, and monitoring. MLOps therefore sets forth the DevOps and - in particular - DataOps principles and best practices to the specific challenges of ML, such as feature management, model selection and tuning, version control, and deployment and monitoring. This helps organizations optimize their operational ML process, reduce errors and downtime, and improve the accuracy and reliability of their models, enabling faster and more accurate decision-making.
The ML process
The basic ML development and operations process involves several stages, each with its own specific tasks and requirements. A general outline of the process looks like:
- Business Requirements Engineering: The first stage in ML involves identifying and understanding the business requirements for ML models. This involves working with stakeholders to define the business problem, understand the data sources available, and identify the key performance indicators (KPIs) that will be used to measure success. This stage helps ensure that ML models are aligned with the needs of the business and that they are designed to address specific business problems or opportunities.
- Data Collection and Preparation: In this stage, data engineers gather and prepare data for the data scientists to use in ML models. This involves identifying and collecting relevant data, cleaning and pre-processing the data, and preparing it for use in training and testing ML models. Also called feature engineering. Principally you can consider this stage DataOps.
- Model Development and Testing: In this stage, data scientists develop and test ML models using the prepared data. This involves selecting the appropriate ML algorithms, building the models, training them on the prepared data, and testing their accuracy and performance.
- Model Deployment: Once a model has been developed, tested, and validated by the business, then it is deployed into production environments. This is when a model leaves its experimentation environment and gets integrated into the operational environment, which entails an abundance of operational considerations and agreements, such as SLAs.?
- Model Monitoring and Maintenance: After deployment, ML models require ongoing monitoring and maintenance to ensure that they are performing as expected. This involves monitoring model accuracy and performance, identifying and addressing issues or errors, and updating and fine-tuning the model as needed.
- Model Retirement and Replacement: As models become outdated or are no longer effective, they may need to be retired and replaced with newer models. This involves identifying when models are no longer effective, planning for their replacement, and developing and testing new models to replace them.
Sounds complex? It absolutely is, thus let’s explore it in more detail.
Business Requirements Engineering
This is a critical step in the ML process, and it involves identifying and defining the business requirements and objectives for a ML model:
- Understand the business problem: To effectively identify business requirements, it is important to have a deep understanding of the business problem that the ML model is intended to address. This involves working closely with stakeholders, domain experts, and other members of the business to gain a comprehensive understanding of the problem and its impact on the business.
- Define the problem statement: Once the business problem has been understood, it is important to define the problem statement. This involves clearly articulating the problem that the ML model is intended to solve, as well as the goals and objectives for the model.
- Identify success criteria: To ensure that the ML model meets the business requirements, it is important to identify success criteria. This involves defining the metrics and KPIs that will be used to evaluate the success of the model in meeting the business objectives.
- Consider constraints: When defining business requirements, it is important to consider any constraints that may impact the development and deployment of the ML model. This may include technical constraints, regulatory constraints, and resource constraints.
- Refine the requirements: Finally, it is important to refine the business requirements as needed throughout the development process. This may involve working closely with stakeholders and domain experts to ensure that the requirements remain aligned with the needs of the business, and that any changes to the requirements are carefully evaluated and documented.
Data Collection and Preparation
Data collection and preparation is probably the most critical step in the ML development process, and it involves identifying and gathering the relevant data, cleaning and pre-processing it, and preparing it for use in training and testing ML models. The process behind this is principally DataOps, which we explored in our previous article. In a nutshell DataOps emphasizes the importance of managing data as a strategic asset, and of applying Agile development principles to data management and analytics. By leveraging DataOps principles and practices during data collection and preparation, organizations ensure that the data is accurate, reliable, and aligned with the needs of the business. This, in turn, shall lead to more accurate and reliable ML models, which can drive business value and provide a competitive advantage.
Model Development and Testing
When developing an ML model, there are several key details that should be considered to ensure that the model is accurate, reliable, and meets business requirements. Here are some key details to consider:
- Data Quality: The quality of data used to train and test the model is critical to its accuracy and reliability. Data quality issues, such as missing values or incorrect data formats, can negatively impact the model's performance. Therefore, data should be carefully prepared, cleaned, and validated before being used to train and test the model.
- Feature Engineering: Feature engineering involves selecting and preparing the variables or features that will be used to train the model. This is an important step in the development process as it can greatly impact the model's accuracy and performance. Feature engineering should be carefully planned and executed, taking into account the business requirements and data characteristics.
- Model Selection and Tuning: Selecting the appropriate ML algorithm and tuning its parameters are important steps in the model development process. This involves evaluating different algorithms and parameter settings to identify the best-performing model. This process should be carefully planned and executed, with appropriate validation techniques used to ensure that the selected model is accurate and reliable.
- Model Interpretability: Understanding how the model works and how it makes predictions is important for gaining trust in the model and for identifying areas for improvement. Therefore, model interpretability should be considered during the development process, with techniques such as feature importance analysis and model visualization used to gain insight into the model's behavior.
- Model Evaluation: Model evaluation involves testing the accuracy and performance of the model on new data. This is an important step in the development process as it can identify issues with the model and provide insight into areas for improvement. Appropriate validation techniques should be used to ensure that the model's performance is accurately evaluated.
Model Deployment
Subsequent to the models development and obtaining a “go” from the business sponsor, there are several details that should be considered to ensure that the deployment is successful and the model performs optimally in production environments, for example:
- Infrastructure: The infrastructure required to support the deployment of an ML model should be carefully planned and provisioned to ensure that it can support the expected load and scale as needed. This may involve selecting appropriate cloud providers, configuring virtual machines or containers, and setting up load balancers.
- Data Inputs and Outputs: The inputs and outputs for the model should be carefully defined and tested to ensure that data is being passed into and out of the model correctly. This may involve setting up APIs or other data interfaces, and defining the expected data formats and structures.
- Security: Security is an important consideration when deploying ML models, as models can potentially expose sensitive data or be vulnerable to attacks. Security measures should be put in place to ensure that the model is protected, such as encryption of data in transit and at rest, and access control measures.
- Performance Metrics: Metrics should be defined and monitored to track the performance of the model in production environments. This may involve monitoring accuracy, latency, throughput, and other performance indicators to ensure that the model is performing as expected and meeting business requirements.
- Versioning and Rollback: Versioning of models should be carefully managed to ensure that changes are tracked and can be rolled back if necessary. This may involve implementing version control systems and setting up processes for testing and deploying new versions of the model.
By considering these details when deploying an ML model, organizations can ensure that the model is deployed successfully and is performing optimally in production environments.
Model Monitoring and Maintenance
Model monitoring and maintenance is an essential aspect of ML operations, and it involves regularly monitoring the performance of the ML model and making necessary updates and adjustments to ensure that the model remains accurate and reliable. Here are some key aspects to consider when it comes to model monitoring and maintenance:
- Model performance monitoring: It is important to continuously monitor the performance of the ML model in production environments to ensure that it is performing as expected. This may involve monitoring key performance indicators such as accuracy, precision, recall, and F1 score, as well as monitoring data drift and concept drift to detect changes in the data distribution that may impact the model's performance.
- Feedback loop: Implementing a feedback loop is essential to monitor model performance and improve it. By collecting feedback from end-users and stakeholders, and feeding that data back into the model, organizations can improve the model's performance over time.
- Re-training: Re-training is an important aspect of model maintenance, as it involves periodically updating the model with new data to ensure that it remains accurate and reliable. This may involve re-training the model with new data, updating the training pipeline, or adjusting the model architecture.
- Version control: Version control is important when it comes to model maintenance, as it enables organizations to track changes to the model over time and ensure that the model is properly documented and reproducible.
- Model governance: Model governance is important to ensure that the model is being used ethically, transparently, and in accordance with organizational policies and regulations. This may involve implementing appropriate data privacy and security measures, as well as establishing guidelines for the use of the model and ensuring that it is being used in an unbiased and responsible manner.
Model retirement and replacement
Model retirement and replacement is the process of decommissioning a Machine Learning model and replacing it with a new or updated model. Here are some key aspects to consider:
- Performance evaluation: It is important to regularly evaluate the performance of the existing model to determine whether it is still meeting the business requirements and objectives. If the model is no longer meeting the requirements, it may be time to consider retiring it.
- Relevance and accuracy: Another important aspect to consider is the relevance and accuracy of the model. As the business requirements and data change, the model may become less relevant and accurate over time. If the model is no longer relevant, it may be time to retire it.
- Resource usage: Another consideration is the resource usage of the model. If the model is using too many resources, it may not be cost-effective to continue using it. In this case, retiring the model may be the best option.
- Replacement model selection: When retiring a model, it is important to select a replacement model that meets the current business requirements and objectives. This may involve retraining an existing model with new data or selecting a new model architecture altogether.
- Transition plan: A transition plan is important when retiring a model to ensure that the transition to the new model is seamless and that the organization can continue to operate smoothly. This may involve carefully planning the migration process, testing the new model, and training staff on how to use the new model.
Model consumption
And this is not the end as one wants to consider how a model is best consumed after deployment. It is important to consider how the model will be consumed by end-users and stakeholders. Here are some key considerations:
- Deployment environment: One of the first considerations is the deployment environment for the model. This may include considerations such as the operating system, hardware, and networking configuration of the deployment environment.
- Model serving: Once the deployment environment has been established, the model needs to be served so that it can be consumed by other services and applications. This typically involves implementing a REST API or similar, which exposes the model's functionality through a standardized interface that can be consumed by other services and applications.
- Scalability and availability: As the model is consumed by other services and applications, it may need to handle a large volume of requests. As a result, it is important to consider scalability and availability when deploying the model. This may involve deploying the model on a cluster of servers, implementing load balancing, and using auto-scaling techniques to ensure that the model can handle high levels of traffic. Kubernetes will be your friend.
- Model versioning: As new versions of the model are developed, it is important to consider versioning to ensure that different versions of the model can coexist and that different services and applications can use different versions of the model as needed.
- Service level agreements (SLAs): Finally, it is important to establish service level agreements (SLAs) for the model, which define the expected performance, uptime, and availability of the model. This may involve implementing monitoring and alerting mechanisms to ensure that the SLAs are being met.
Why use an API Management Framework
An API management framework provides an abundance of benefits when deploying Machine Learning models for consumption by other services and applications, eg:
- Security: An API management system can help to ensure that the model's API is secure by implementing authentication and authorization mechanisms, rate limiting, and threat protection. This helps to prevent unauthorized access to the model and protects against malicious attacks.
- Monitoring and analytics: An API management system can provide detailed monitoring and analytics of the model's usage, performance, and availability. This helps to identify issues early on and enables proactive management of the model.
- Governance and compliance: An API management system can help to ensure that the model is compliant with governance and compliance policies. This may include implementing policies for data privacy, data protection, and data access.
- Version control: An API management system can help to manage different versions of the model's API, enabling different services and applications to use different versions of the model as needed. This helps to ensure that the model is always up to date and that it can be easily updated or rolled back as needed.
- Scalability and reliability: An API management system can help to ensure that the model's API is highly scalable and reliable, enabling it to handle high volumes of traffic and ensuring that it is always available when needed.
At OriginML we make heavy use of an API management framework on the back of the above considerations in order to provide a cloud agnostic, yet enterprise scaling API environment. An API management framework is therefore a part of the ML related toolchain required to support and execute the ML process end-to-end.
Toolchain Considerations
But choosing the right toolchain is not easy and has a plethora of considerations to follow through with:
- Complexity: There are a wide range of ML tools and technologies available, each with its own set of features, strengths, and limitations. This can make it difficult to choose the right set of tools that are suitable for the entire MLOps process.
- Integration: Even if individual ML tools are selected for different stages of the MLOps process, it can be challenging to ensure that they integrate seamlessly with one another. This can result in inefficiencies and difficulties when moving data between different tools and technologies.
- Scalability: As ML models become more complex and larger in size, it can be challenging to ensure that the underlying infrastructure is scalable enough to handle the load. This may require specialized hardware or cloud infrastructure, which can be costly and difficult to manage.
- Cost: Many ML tools and technologies are proprietary and come with high licensing costs. This can make it difficult for organizations to justify the cost of building and deploying machine learning models at scale.
- Expertise: Finally, it can be challenging to find individuals with the right expertise to work with specific machine learning tools and technologies. This can result in skill gaps and difficulties in managing and maintaining machine learning models over time.
While there are countless tools available to support every single step in the ML process, the challenge is in fact “choice”. It is certainly not easy to master that, but at OriginML we have made a sound selection of open source components to get you kickstarted.
How can OriginML help?
OriginML
helps organization reap the benefits of ML by simplifying the ML process. See, while the ML value proposition is clear, the challenge is how to get started under a few conditions:
- Low learning curve - we have simplified the DataOps and MLOps process for you with our SDK
- High flexibility - our infrastructure dynamically scales up and down with your workloads?
- Low risk - we built on best of breed open source; hence no particular vendor lockin
- Fast Deployment - our SDK allows you to train and autodeploy your models, accessible immediately via REST API
