Understanding the MLOps Lifecycle

Imagine you spend weeks building a machine learning algorithm to predict churn rates. The model performs well on the data you already have, and you’re excited to deploy it for actual users. However, after some time, you notice that the model's predictions are inaccurate. What went wrong?

A possible explanation is that the training data used for the model was not representative of current user behavior. This is one of the problems that Machine Learning Operations (MLOps) addresses. MLOps doesn’t end after deploying your models; it encompasses a complex lifecycle. This lifecycle includes collecting data, testing, deploying models into production environments, automating workflows, monitoring, and retraining models.

The global MLOps market was valued at $720 million in 2022 and is projected to grow exponentially to $13,321 million by 2030. This growth highlights a critical trend that more organizations are recognizing the role MLOps play in the successful deployment and management of machine learning applications.

In this article, you’ll learn about the machine learning lifecycle, how MLOps fits into it, and why practices like continuous integration (CI), continuous delivery (CD), and continuous deployment (CD) are key to improving collaboration, speeding up model deployment, and guaranteeing long-term success.

What is MLOps, and why does it matter?

MLOps is a set of practices that applies DevOps principles to data science and machine learning. If you are familiar with DevOps in software development, you already understand its core principles: collaboration, automation, continuous integration (CI), and continuous delivery (CD).

In MLOps, these same principles apply, but they are focused on the unique challenges that machine learning presents, such as model training, data validation, and continuous model improvement.

MLOps incorporates automation at every step—from model development to deployment and maintenance, helping teams work more efficiently and achieve faster results. MLOps also enables operations teams to proactively monitor and manage machine learning systems, making sure they run smoothly even as data and models evolve.

MLOps is not a one-stage process. It is a lifecycle comprising several interconnected stages. Understanding this lifecycle is crucial in streamlining your machine learning workflows. Let's look at the key stages involved in this process.

The key stages in the MLOps lifecycle

The machine learning lifecycle is complex, involving multiple stages that require close collaboration between data scientists, machine learning (ML) engineers, software engineers, and operations teams. Here's a breakdown of the different stages and how MLOps practices are applied to each one:

1. Data collection and preparation
Before any ML model can be built, the input data first needs to be gathered, cleaned, and validated. Data preparation involves collecting training data from various data sources, performing exploratory data analysis (EDA) to understand the data, cleaning the data, and validating that the data is of high quality through data validation. The data quality directly affects model performance, so this stage is critical.

Data engineering is key at this point. Data engineers set up the infrastructure to process big data analytics efficiently, ensuring that large datasets are properly cleaned and transformed before they are fed into ML models. They also work with operations teams to make sure the data pipeline is robust and scalable.

Some popular tools for data extraction are Airbyte, Fivetran, Hevo Data, and many more.

2. Model development and training
Once the data is ready, ML engineers begin developing the models. This involves selecting ML algorithms, training models on the data, and hyperparameter tuning to improve performance. This stage requires careful monitoring of model performance, as it's common for model accuracy to fluctuate during the process.

Model training is the process of feeding data to a machine learning algorithm. This process allows the model to understand patterns in the dataset, enabling it to make good data-driven decisions.
During this phase, automated builds play a significant role. With the help of continuous integration (CI) practices, teams can verify that every new change to the source code repository—whether it's a new algorithm, a tweak to the training pipeline, or a data update—is automatically tested for issues. This means that new code or changes don’t break the system, and the team can quickly resolve issues as they arise.

Popular tools for model development are TensorFlow, MLFlow, and PyTorch.

3. Model testing and validation
Once models are trained, they need to be rigorously tested. In MLOps, this includes checking whether the model generalizes well on unseen data, as well as evaluation metrics such as accuracy, precision, recall, and F1 score. Model validation often includes reviewing how the model behaves in real-world conditions. These conditions may include diverse input data and data distributions, among others.

This stage also involves data validation to ensure that the training data used in the model is not only accurate but also representative of the kind of data the model will encounter in production. If a model is deployed without adequate validation, it risks producing poor results or even introducing biases into the system.

Some tools for model validation include Neptune AI, Kolena, and Censius.

4. Model deployment
Once you have tested and validated your models, they are ready for production. This makes your models' predictions available to your applications and end users so that they can use it to make predictions.

Based on your technological stack, various services are used to deploy machine learning models. Some popular services are AWS Sagemaker, Azure Machine Learning, Vertex AI, and many others.

5. Continuous Integration (CI) and Continuous Delivery (CD)
With CI/CD pipelines, the focus is on automating the processes of testing and deployment, making sure changes are smoothly integrated and deployed into the production environment. CI allows developers and ML engineers to automatically merge new code into the system, run tests to ensure everything works as expected, and check for issues such as code quality or integration problems.

Once the model has been thoroughly tested, continuous delivery takes over. It guarantees that models can be rapidly and safely deployed to the production environment—allowing for faster time-to-market. Continuous deployment (CD) goes further, automating the entire process so that models are automatically pushed to production without manual intervention.

Some popular CI/CD tools are GitHub Actions, Jenkins, GitLab CI, and Azure DevOps.

6. Model monitoring
After the model is deployed, the next challenge is ensuring it continues performing well in the real world. In MLOps, keeping track of production models and their performance in production environments is essential.

Operations teams use monitoring tools to track response times, accuracy, and other key performance indicators (KPIs). These teams maintain situational awareness and are prepared to quickly address issues if the model's performance degrades due to changes in new data or user behavior.

Some popular tools for model monitoring include MLFlow, Grafana, and Prometheus.

7. Model retraining
One of the greatest strengths of MLOps is its focus on continuous improvement. As the model is used in real production scenarios, it will likely encounter new data and evolving conditions. Over time, models can suffer from data drift, which causes them to become less effective.

To prevent this, models are retrained periodically using new data. Model monitoring is also important in detecting data drift, and acting on it. The ML engineers continuously monitor for changes in data distribution and re-implement models based on new features or changes in training data. This continuous deployment of improved models keeps the machine learning system up-to-date and effective in solving user problems.

The challenge of MLOps

The traditional MLOps lifecycle of moving from experimentation to production is complex and often frustrating. Data scientists, machine learning engineers, and operations teams face numerous challenges, such as version control, security, collaboration, and ensuring consistent environments across various stages of the pipeline.

The traditional MLOps lifecycle is split, with various artifacts (models, datasets, code, and metadata) scattered across different tools, systems, and environments. This leads to several key challenges:

1. Lack of standardization: Unlike software engineering, where tools like containers or version-controlled code are ubiquitous, AI/ML lacks widely adopted standards for packaging and versioning artifacts. Packaging and versioning model artifacts enable teams to easily track changes in models and their artifacts.
2. Collaboration barriers: Data scientists, ML engineers, and DevOps teams often work in silos, using different tools and systems to manage their parts of the pipeline. This fragmentation hinders communication and collaboration, slowing down development and making it harder to track changes.
3. Security and compliance risks: AI/ML models and datasets often deal with sensitive information and must comply with various regulatory requirements, such as the General Data Protection Regulation(GDPR). Maintaining the integrity and security of these artifacts is crucial, but often difficult to achieve in most systems.

KitOps solves these problems by offering a unified approach to managing the entire MLOps lifecycle, from development to production.

How to tackle these MLOps challenges

KitOps is an open-source tool that streamlines collaboration across data scientists, machine learning engineers, and DevOps engineers. It makes tracking your models, data lifecycle, and its versions easy.

KitOps simplifies the MLOps lifecycle by standardizing how AI/ML artifacts are packaged, versioned, and managed across all stages of the development pipeline. It builds on existing DevOps tools and workflows, enabling teams to use familiar processes and tools while extending them to AI/ML.

Here's how KitOps helps streamline the MLOps lifecycle:

1. Standardized packaging and versioning
In MLOps, one of the biggest challenges is managing the various artifacts that make up an AI/ML project. Model versions can include:

• Models stored in Jupyter notebooks or MLOps tools
• Datasets stored in data lakes, file systems, or databases
• Code stored in Git repositories
• Metadata such as hyperparameters, features, and weights

Without standardization, managing these artifacts can become difficult to track and maintain. KitOps solves this by enabling teams to package all these components (models, datasets, code, and metadata) into a single versioned artifact called a ModelKit. The ModelKit can then be tracked, stored, and deployed like any other production asset, enforcing consistency across the pipeline.

2. Streamlined collaboration across teams
MLOps often involves cross-functional teams, such as data scientists, machine learning engineers, and DevOps or SREs, each using different tools and working in different environments. KitOps helps bridge these gaps using the OCI (Open Container Initiative) standard for packaging and versioning artifacts. This allows teams to store and manage AI/ML artifacts in the same enterprise registries (e.g., DockerHub, JozuHub) used for other software components, like containers and microservices.

With KitOps, all stakeholders can easily collaborate on shared artifacts, no matter what tools they use. For example, data scientists can work in Jupyter notebooks, while DevOps teams can manage deployment pipelines without worrying about mismatches between different versions or environments. This reduces friction and accelerates the flow of work between teams.

3. Security and compliance integration
Security is a top priority in MLOps, especially when working with sensitive data or meeting compliance requirements. KitOps helps address these concerns by ensuring that all AI/ML artifacts are tamper-proof and secure. As an OCI artifact, ModelKits are immutable. When creating a ModelKit, a manifest is also generated. This manifest signs your ModelKits with a cryptographic hash, making them very secure.

ModelKits are an OCI-compliant packaging format for all AI/ML artifacts. This way, teams easily package their models, datasets, code, and configurations within ModelKits and share them in a standardized format that works with any OCI-compliant registry. This gives organizations control access while ensuring compliance with data protection and privacy regulations.

4. Integration with CI/CD
KitOps integrates easily into your CI/CD pipeline, enabling automated deployment of AI/ML models. This guarantees that all artifacts are consistently managed across environments. KitOps helps track AI/ML artifacts at every stage and ensures that each version is properly managed and secured. The ability to quickly roll back to previous versions, should issues arise, provides additional flexibility and peace of mind.

KitOps can be integrated with GitHub Actions, Jenkins, Dagger.io, OpenShift pipelines, and many others. This makes it easy to automate the process of packing, unpacking, tagging, and pushing models and their artifacts to an artifact registry.

Conclusion

MLOps is an iterative process that includes data collection, model training, model validation, model deployment, and monitoring. Each of these stages is paramount to the success of your machine learning models.

KitOps simplifies MLOps by bringing order and standardization to AI/ML development. By leveraging existing DevOps principles, KitOps allows teams to manage machine learning models, datasets, code, and metadata in a way that promotes collaboration, security, and efficiency. To learn more about KitOps, visit the project site, which also has an easy-to-follow guide to get you started.