What is Machine Learning Model Management?

Machine learning model management is one of the cornerstones of modern data science initiatives. But what is it, why is it important, and what can it do for you? 

Ever wonder why you spent so much time learning about WW2 in history class? It was because of the lesson plan, the tool in any teacher’s arsenal that ensures you’re learning what you need to know. Anytime you’ve ever learned something in a professional environment, it’s a safe bet there was a lesson plan involved.  

So why should machines be any different? Everyone wants their technology to be continuously learning and improving. But to do this they need to ensure the data science services they have are up to scratch, because machine learning is only as good as the data fed into it.  

What’s more, the amount of data these systems can handle is dependent on the ‘lesson plan’ put in place at their inception. Another name for this lesson plan is machine learning model management. 

Read also: How to build a machine learning app?

ML model management – an overview 

Machine learning model management is essentially the education process for your technology – with the promise of real-world experience at the end. It’s responsible for developing, training, versioning, and deploying your machine learning models. For the purposes of our article – developing, training, and deploying all align to our education metaphor – if we imagine deployment as the beginning of your tech’s real-world experience. But what exactly does versioning refer to? 

Imagine versioning as the different drafts or improvements you made when writing your college thesis. Some documents in life are too important to rely on a first draft, and the same is true for machine learning. As the code develops overtime, developers work off better versions of their product (version 1, version 2, version 3 etc.) before releasing it onto the market. 

That, in essence, is what machine learning model management is – a process for your technology that includes a review stage and real-world experience. 

Learn more about our AI and Machine Learning Services

Why is model management important? 

Putting your technology through a learning process might sound silly, but in a world where clients and employees alike are clamoring for more human-like experiences when interacting with your technology, it’s more important than ever. Let’s look at the benefits of this idea in practice.  

During the pandemic, salespeople couldn’t physically meet clients due to government restrictions which presented them with a major challenge. In a field where reading emotions, language, and body movements accurately is paramount, how were salespeople to function in a world where sharing physical spaces was prohibited. This is where machine learning model management and large language models came into play. 

By leveraging this technology to identify minuscule changes in facial expressions and tone of voice, salespeople now had access to the same tools they had in the pre-covid years. This of course ensured major companies could still function despite the unique challenges of the time and enabled economies all over the world to keep moving. 

This is just one reason why machine learning is important, but there are others. For example, with machine learning we get smarter chatbots, thanks to LangChain technology, that can answer our queries in a human-like fashion without the need to look for the number of a call center. Machine learning model management components.

Read also: The Basics of Machine Learning Cloud Services

So how can you make machine learning model management work for you? First, let’s talk about the different parts that make machine learning what it is today, beginning with the machine learning model. In short, this is a file that developers have trained to recognize certain patterns.  

Our brain can recognize certain patterns very quickly and easily so the idea is to get this file to function in a similar way – especially if you have repeatable tasks that you want to automate or have an example of some task or process where you can clearly map out the result you want for your company. 

However, there are five key components you need to consider if you want your machine learning model to function correctly. They are: 

• Data versioning: a set of tools and processes that adapts your newest version of control processes to the new data you’re working with by managing any changes in relation to your own datasets and vice-versa – remember a university thesis never work as a first draft 
• Code versioning/notebook checkpointing: like data versioning outlined in the previous point, only these tools and processes handle changes in your code 
• Experiment tracking: used for collecting, organizing and tracking how your machine learning model interprets and validates information, as well as how it performs over multiple runs when handling different configurations and datasets  
• Registry system: basically, your administration cabinet that contains everything you need to know about what models are in training, where they are in their training, how many models are in the pipeline, and how many have been deployed etc. 
• Model monitoring: used to track model performance and identify any signs of degradation, which is often caused by data changes made to the model after deployment. 

How to implement machine learning model management 

Now, there’s just one more question left to answer – how can you add machine model management to your IT toolkit? There are four different stages to any machine learning model implementation – Level 0, Level 1, Level 2, and Level 3: 

• Level 0: This is your research stage – establishing if your lesson plan is worth investing in. It’s fantastic for machine learning newbies to start learning the ropes, while also good for experts to see if an idea is worth pursuing. However, the downside is that this stage has no way of tracking your versions or logging which ideas work for your team and which don’t. In short, if you want more structure and accountability while being able to reproduce the results of your experiments, you need to progress to the other levels in the machine learning implementation process 
• Level 1: This is the beginning of your lesson plan – the first draft where you begin to put your ideas to the test. It’s generally favored by well-structured teams doing rapid prototyping. At this level your developers will have access to all the benefits of level 0, along with data and model versioning, which makes experiments reproducible – at least partially. But this stage still lacks accountability through fully reproducible experiments, notebook checkpointing, and avenues for continuous improvement – which is essential if you want your technology to keep learning and growing 
• Level 2: Here, the first draft of your lesson plan is on paper, so you can begin to review it in earnest, move things around and sharpen key areas, while pulling out any avenues you might consider to be dead ends. This stage is favored by teams who want to test their hypothesis quickly, while getting their ideas into a production environment at speed. It has all the advantages of levels 0 and 1 and includes notebook checkpointing, which allows you to make changes to code as needed. It’s also very production driven, as any experiments are fully reproducible. It only lacks one thing – a continuous improvement pipeline that enables you to start giving your tech that human touch. For that you need to move onto the final level of machine learning implementation 
• Level 3: At this level you’re a master at writing lesson plans. So good, you could do it in your sleep – which you can – as everything is automated in the machine learning pipeline at this level of development. It has all the advantages of the previous levels and even includes a continuous improvement pipeline. However, it’s important to note that even this level has its challenges as it lacks a way to continuously train your machine learning model once it goes out into the world. Thankfully, this can be solved by several machine learning monitoring tools on the market such as Sagemaker and Azure Machine Learning.  

Best practices in machine learning model management 

Several best practices in machine learning model management are worth adhering to. With them in place, organizations can manage machine learning models throughout their lifecycles, ensuring dependability, scalability, and compliance with regulatory standards.  

1. Establish model lifecycle stages, including development, testing, staging, and production. Define criteria for transitioning models between stages and retiring obsolete models. 

2. Specify a standardized procedure for deploying models into production environments to facilitate deployment processes and ensure consistency across environments. 

3. Establish a standardized procedure for deploying models into production environments to facilitate deployment processes and ensure consistency across environments. 

4. Compare the retrained model against your original machine learning model to verify if it meets your objectives and expectations by collecting and labeling new data to conduct the comparison. 

5. Gather feedback from stakeholders and incorporate new data and techniques to implement a constant performance evaluation of a machine model. 

Machine learning model management 

MLOps is the discipline of managing the entire lifecycle of an ML model, from development and deployment to monitoring, retraining, and retirement. It applies reliable software engineering (DevOps) principles to data science to standardize and scale the use of AI. 

Why it’s important? Models are not static. Their performance naturally degrades over time as real-world data (“production data”) changes, a concept known as model drift. MLOps is crucial for ensuring models remain accurate, reliable, and valuable long after they are first deployed, turning them from research projects into dependable business tools. 

MLOps and its role in ML model management

There are several benefits MLOps brings to the table:  

• Accelerating deployment: Standardizes and automates the process of moving models from a data scientist’s laptop to a live production environment. 

• Ensures performance: Provides continuous monitoring for accuracy, drift, and bias, automatically triggering alerts or retraining. 

• Scales operations: Allows organizations to efficiently manage, version, and update hundreds or thousands of models simultaneously. 

Governance, Auditability, and Lineage

Managing the entire lifecycle of an ML model is essential for risk management and regulatory compliance: 

• Governance: Implements rules for model development, review, and deployment, including access controls, fairness checks, and “human-in-the-loop” approval workflows. 

• Auditability: Creates an immutable log (an “audit trail”) of all model activities, showing who did what and when for compliance checks. 

• Lineage: Tracks the end-to-end “bloodline” of a model—linking the exact data, code, and parameters to every model version, ensuring results are reproducible and simplifying debugging. 

MLOps tools that facilitate model management 

Several tools facilitate model management. For the sake of this article, let’s focus only on the most highly sought-after.

• MLflow: An open-source platform that provides an end-to-end MLOps solution. It is built on four key components: Tracking (for logging experiment parameters and metrics), Projects (for packaging code), Models (for managing and deploying models), and a Model Registry (for versioning and governance). 

• DVC (Data Version Control): An open-source tool that works with Git to manage large datasets, ML models, and data pipelines. It versions large files by creating small “pointer files” that are stored in Git, while the actual data is stored in remote storage (e.g., S3 or Azure Blob Storage). This enables reproducibility and Git-based workflows (like branching and merging) for data science. 

• Kubeflow: An open-source project dedicated to making ML workflows on Kubernetes simple, portable, and scalable. It is not a single tool but a platform of components. Its most popular feature is Kubeflow Pipelines, which allows you to build, orchestrate, and automate complex, multi-step ML workflows as containerized tasks. 

• Neptune.ai: A managed metadata store (or experiment tracker) focused on logging, visualizing, and comparing all aspects of your model lifecycle. Unlike MLflow, which you can self-host, Neptune is a SaaS product that acts as a central “source of truth” for experiments, capturing everything from hyperparameters and metrics to model artifacts for easy auditability and collaboration. 

Key development directions for machine learning model management

• LLMOps (Large Language Model Operations): This is the specialization of MLOps for managing large language models. Standard MLOps tools are not built for the unique challenges of LLMs, so LLMOps focuses on developing new best practices for managing prompt engineering, versioning massive model weights, token optimization, cost monitoring, and detecting model “hallucinations” and toxicity. 

• AutoML integration: The future of model management is increased automation. AutoML (Automated Machine Learning) platforms are being integrated directly into MLOps pipelines. This allows the system to automatically handle complex tasks like feature engineering, model selection, and hyperparameter tuning, ultimately moving towards self-healing systems that can automatically detect drift and retrain/deploy new, optimized models with no human intervention. 

• Federated learning: As data privacy regulations (like GDPR) become stricter, MLOps must adapt to a world where data cannot be centralized. Future model management platforms will orchestrate training on decentralized edge devices (like mobile phones or hospitals) without the raw data ever leaving. The platform’s job will be to send the model to the data, manage the local training, and securely aggregate the resulting model updates. 

• Explainability (XAI) and fairness: “Black box” models are no longer suitable for critical applications. The next generation of MLOps platforms is integrating Explainability (XAI) more deeply. This means that management dashboards will not only display a model’s accuracy but also explain why it made specific decisions, utilizing methods such as SHAP or LIME. This approach offers built-in auditability, facilitates fairness and bias detection, and enhances the trustworthiness required for regulatory compliance across sectors such as finance and medicine. 

Human-like interactions are within your grasp

If you want your technology to offer human-like interactions to your employees, clients and partners, then you need to take machine learning model management seriously. 

Here at Software Mind, we’re aware that teaching can be difficult, which is why our experts are always available to provide more information about what machine learning can do for you. 

Read also: Leveraging Cloud-Based AI/ML Services to Elevate your Business 

Read More: How to create an AI model

FAQ  

How does model management differ from MLOps? 

MLOps is the overarching, end-to-end discipline (like DevOps) that automates and manages the entire machine learning lifecycle—from data ingestion and model training to deployment and monitoring. Model management is a specific component of MLOps that focuses only on the governance and lifecycle of the models themselves after they are trained, including versioning, production monitoring, detecting drift, and managing retirement. 

What are the key components of an effective model management process? 

An effective model management process includes a model registry for versioning and storing trained models, continuous monitoring to track production performance and detect data/model drift, and a governance framework for auditability and compliance. It also requires automated retraining pipelines to update degrading models and standardized deployment strategies to roll out new versions safely. 

What are the challenges in managing machine learning models at scale? 

Key challenges include model drift, where performance degrades as real-world data changes, and data drift. Scaling requires managing immense computational resources and complex, automated deployment (CI/CD) pipelines. Continuous monitoring for accuracy, latency, and drift is essential but difficult, as is maintaining reproducible versioning of data, code, and models. 

What are the enterprise-grade solutions for machine learning model governance? 

Enterprise-grade solutions for machine learning governance include integrated tools from major cloud providers, such as Amazon SageMaker, Google Vertex AI, and Azure Machine Learning. These platforms provide built-in features for monitoring, bias detection, and audit trail creation. Alongside these are unified platforms like Databricks, which uses its Unity Catalog for governance, and specialized, agnostic tools like ModelOp and DataRobot, which offer dedicated risk management and compliance platforms to manage models across an entire organization, ensuring full audibility and regulatory adherence.