What Is MLOps? Levels, Lifecycle, and Tools Explained

Companies across industries are building and training machine learning models, but many still struggle to get them into production.

The problem isn’t the models themselves. It’s the lack of a repeatable process to train, deploy, and manage them at scale.

Without structure, teams face delays, brittle pipelines, and models that fail in real-world conditions. Machine learning operations (MLOps) solves this by giving teams a way to manage machine learning workflows end-to-end using automation, collaboration, and governance.

From gathering data to data pre-processing to creating models and final integration, MLOps controls all production processes. It converts your ML tasks into good-quality pipelines for seamless execution. Operationalizing ML reduces data storage and warehousing costs, shifts labor from the shoulders of data science teams, and puts ML processes into an automation framework.

Commercial sectors across banking, finance, retail, and e-commerce use the best artificial intelligence (AI) and MLOps software to optimize their data in line with their products and services. 

What is the history of MLOps?

Creating an MLOps environment is complex because you need to maintain data in the form of thousands of ML models. 

The origins of MLOps started in 2015 in a published research paper. This paper, “Hidden Technical Debts in the Machine Learning System,” highlighted ongoing machine learning problems in business applications.

“Hidden Technical Debts” focused on the lack of a systematic way to maintain data processes for a business, and it proposed the concept of MLops for the first time.

Since then, MLOps has been strongly frontloaded in many industries. Businesses use it to produce, deliver, and secure their ML models. It upholds the quality and relevance of the current data models being used. Over time, MLOps-powered applications have synchronized large petabytes or zettabytes of data modeling processes and treated data in a smart way to save ML team bandwidth, optimize GPU, and secure app workflows.

What are the levels or maturity stages of MLOps implementation?

An MLOps framework has several installation layers. Most organizations progress through three key levels of implementation, from manual workflows to fully automated pipelines.

Each level builds on the previous one by increasing automation, standardization, and collaboration across data science, engineering, and operations teams. 

1. MLOps Level O (Manual)

If you aren’t AI-ready as of yet, this is the solution you should begin with. Manual ML-specific workflows should be enough if the frequency of data influx is low. 

Characteristics of MLOps Level 0 

MLOps Level 0 is the first pitstop for a company that's on the road to automation. Accruing this framework would result in the following characteristics.

• Manual, documentation driven: Every step in the machine learning lifecycle is labor-intensive, including data analysis, model training, and validation. It requires team bandwidth and time to execute each step in the workflow.
  
• Team silos and disconnect: Dev teams and data scientists have no synchronization. Each associate is by themselves with their individual tasks.
  
• Infrequent release iterations: Data scientists only work on the data in case of an urgent iteration or retraining request. Other times, the process of ML operationalization remains constant.
  
• No continuous integration: Because of few implementations, CI is ignored. You test or execute the program manually.
• No continuous deployment (CD): Because there aren’t frequent model version runs, the CD isn’t initiated.
  

2. MLOps Level 1 

The goal of MLOps 1 is to train a model as new data enters the system and automate the ML pipeline. This way, your model remains in service at all times.

Characteristics of MLOps Level 1

Companies going for Level 1 have already attained some amount of AI maturity. They use AI for low-scale projects and sprints with a defined set of characteristics.

• Rapid experiment: Sub-steps of MLOps are designed and validated with automation workflow.
• Continuous model training: The continuous training of the ML model is automatically conducted during the production cycle.
• Experimental-operational symmetry: Preproduction or production pipeline are aligned so that nothing falls through the cracks.
• Modularized code: To construct ML pipelines, code needs to be shareable, reasonable, and reproducible.
• Continuous model delivery: Models are validated and delivered automatically as a part of the prediction service.
• Pipeline deployment: In level 0, you deploy a trained model for predictions. Here, you devote an entire ML pipeline to predictions.
• Data and Model Validation: These important steps of the ML pipeline are automated, so the machine learning model works with new, live data.
• Feature extraction: The features of machine learning models are stored in a central repository and repeated across all coding platforms.
• Meta information: The meta information and execution information of each ML pipeline is recorded. The models are trained fast, with lineage, artefacts, and other parameters. Meta information also reduces compilation errors.

3. MLOps Level 2

This level fits transformational companies that use AI on a large scale to cater to most of their consumer base requirements.

Characteristics of MLOps Level 2

MLOps Level 2 is appropriate for companies that use automation in every small sapling in their business forest.

• Development and experimentation: Iteratively trying new algorithms for your ML models and pushing the data into a source repository.
  

• Continuous integration:  Testing source code and pushing it to your model registry. The output of this stage is model packages, executables, and artifacts.
  

• Continuous delivery: Once you get through the CI stage, the output is exposed to the target environment. Output is usually the new code for ML models.
  

• Automated triggering: The new code is automatically executed in production based on a scheduler, resulting in a trained model.
  

• Model continuous integration: This trained model is then integrated with service applications. It’s also known as a model prediction service.
  
• Monitoring: You collect statistics based on your model's performance on live data. This cycle iterates on its own without any disturbance.

Every step in this workflow runs on its own, with little manual intervention from data and analytics teams. 

How does MLOps differ from DevOps?

MLOps and DevOps share similar goals: automation, faster release cycles, and operational stability, but they solve very different problems. DevOps focuses on code and infrastructure. MLOps extends those ideas to the unique needs of machine learning, where data, models, and monitoring are just as critical as code.

MLOps manages models, data, and continuous learning

MLOps supports the full lifecycle of a machine learning model, from development to deployment, monitoring, and retraining. It introduces pipelines for code, as well as data and models, with systems to track versions, monitor performance in production, and retrain when accuracy drops or data changes.

It also adds critical layers, such as experiment tracking, data lineage, and compliance, all of which are essential when working with dynamic, data-driven systems.

DevOps automates software deployment and reliability

DevOps focuses on building, testing, and deploying software reliably and at scale. It uses practices like CI/CD and infrastructure as code to automate releases and reduce downtime. The scope is typically limited to code and applications, with performance measured by availability, speed, and error rates.

While DevOps doesn’t cover ML-specific needs like model drift or retraining, its infrastructure and automation practices form the backbone of many MLOps pipelines. 

What are the different components of MLOps Lifecycle?

MLOps can be categorized into four phases: experimentation and model development, model generation and quality assurance, and model deployment and monitoring. No matter the phase, the machine learning model is the main pinwheel of MLOps. 

Before jumping into the actual process, let’s go through the following basics.

1. Experimentation and model development

The MLOps experimentation stage deals with how to treat your data. It collects engineering requirements, prioritizes important business use cases, and checks the source data availability.

Cleaning and shaping data takes up a lot of bandwidth for your ML teams, but it’s one of the most important steps. The better the data quality, the more efficient your model will be.

2. Model generation

Once your data is ready, it’s time to build the ML operationalization wireframe.

ML models are either supervised or unsupervised; the model runs on real-world data and validates it against set expectations. 

Brushing up an ML model is achieved in 8 defined steps: 

• Data analysis: Right after data is sourced, you need to run an exploratory data analysis or EDA to investigate the attributes of your data. Developers use this stage to spot patterns or anomalies in data and create co-relations. Overall, it makes data shareable, reproducible, and simple for other ML counterparts.
  
• Data prep and feature store: Next, you need to extract the main features of the data and store them in a separate sheet, known as a feature or model database. The features can be anything that describes the data best.
• Algorithm selection: Choose the right algorithm from dozens of options available. You can use an open-source tool like Python or Tensorflow to code your ML algorithm. Further, ensure the algorithm trains well on your sample datasets. Exercise complete control over your data to prevent any misuse. 
  
• Hyperparameter tuning:  Hyperparameters carry meta information regarding your data, like the size of your ML model or the model versions. Tracking these parameters helps you reproduce data when you encounter new challenges. You can easily go back to the coding platform and re-adjust your parameters.
• Model training: Fitting the right training data will make your model functional. Select the right data version and train your algorithm on it. This iterative process is known as model fitting. Fit your model with as many samples as possible to ensure accurate predictions.
  
• Model inference and serving: Once your model is verified and reviewed, you can roll it into production. Model inference checks your ML models against user needs and business requirements. It’s also referred to as “putting an ML model into production.”
• Model review and governance: Model governance defines the policies and organizational guidelines for your machine learning operations. Effective model governance checks violations, compliance, and brand reputation.
  
• Automated model retraining: As a particular business expands its footprint, the data shifts. For example, say your company used ML to detect suspicious documents, but now you also conduct health assessments. In this case, you need to retrain and rethink the model. MLOps automates model retraining on its own. 

3. Quality assurance and model validation

After models are deployed into production, it undergoes several tests. For example, Alpha testing, beta testing, or red and blue testing. Running software tests ensures the premium quality and robustness of machine learning models.

Quality assurance means that your models are gated and controlled. This process usually runs on an event-driven architecture. While some models go into production, others wait patiently for their turn in a scheduled queue.

Models are also validated at regular interventions. A human in the loop double-checks the performance of a model. Having a designated team member to keep track of models lessens the scope of error.

4. Model deployment and monitoring 

You might think that model validation is the last layer of the MLOps cake,  but it’s not. After repurposing and reviewing ML models, you need to deploy them into your ML production pipeline. 

The models are packaged into different containers and integrated with running business applications. Business applications get updated with newer use cases and functionalities. However, it doesn’t happen in one go. Proper scheduling and prioritization queues are set for each ML pipeline.

Each model is isolated, tested for accuracy, and then carried out for production. This process is known as unit testing. Unit testing checks the performance response latency (time taken to respond to input queries) and query throughput (units of input processed).

While setting a data supply chain, you need to ensure water doesn't flow above the bridge. You never know when a sudden data burst will destroy everything you have in place. Model pulling and pushing is a constant rally in MLOps.

Should organisations build or buy an MLOps infrastructure? 

Tech companies like Microsoft Azure, AWS, and Google Cloud Storage have on-premise cloud infrastructure that makes machine learning processes much easier. But not every company can build everything, and some companies don’t want to build anything, which brings us to the three types of MLOps infrastructure: building, buying, and hybridizing.

To build an MLOps infrastructure, you need an in-house machine learning team and the required resources like time and labor. A well-qualified team can tackle complex data since they have enough skill and expertise for it. You might have to shell out more money from your budget, but it could be worth it for your team’s needs.

Buying an MLOps infrastructure might look like the smart way, but again isn’t cheap. Your company would also have to bear inflexibility, compliance, and security risks if data went wrong.

Hybrid MLOps infrastructure combines the best of both worlds. It equips you with skilled expertise, like on-premise infrastructure, and the flexibility of the cloud. However, underlying performance, security, scalability, and availability concerns always catch you off guard. Hybrid MLOps stakeholders face challenges managing this kind of infrastructure.

What are the top benefits of MLOps?

MLOps plays a critical role in helping teams scale machine learning beyond experimentation. As more businesses move models into production, MLOps provides the structure, automation, and visibility needed to manage complexity and ensure consistent results.

Below are the key advantages of implementing MLOps effectively:

• Better productivity: By automating repetitive tasks and reducing ad hoc troubleshooting, MLOps frees technical teams to focus on high-value work like improving model performance or expanding use cases.

• Robust ML pipelines: ML pipeline is deployed to deliver prediction services to machine learning models. It starts with ingesting the new data and aligning it with the right algorithm for better predictability. MLOps enables reliable, end-to-end pipelines, ensuring consistent outputs across environments.
  

• Cross-team collaboration: Software teams, data operations teams, engineers, and ML developers connect with each other in MLOps processes. Labor, resources, and energy are distributed equally, reducing system downtime.
  
• Lower infrastrucutre costs: If you have one accurate model, you don’t need to add more to the stack. Since there are no hardware requirements for MLOps, IT infrastructure becomes lean and agile. 
  
• High reproducibility: Code can be easily reused and repeated. Having complete code control reduces the time needed to build new models. Most open-source tools have built-in syntaxes to fast-forward code writing.
  
• Audit-ready processes: MLOps makes data auditing a breeze. You won’t lag behind in terms of data runs or data feeds, and your teams can conduct audits at every stage of the production pipeline. Audits can identify and prevent ambiguous pieces of code and rectify these mistakes without harming any other workflow.
• Continuous monitoring and reliability: MLOps enables continuous monitoring of models in production: tracking performance, detecting drift, and triggering retraining when needed. This proactive oversight ensures that models remain stable, accurate, and reliable as conditions change.

What are the main challenges when implementing MLOps? 

Too many cooks spoil the broth, and too much automation results in a system breakdown. MLOps monitors the performance of your ML models from start to finish. But when machines control production, even a slight misstep can be lethal.

Let’s see what challenges you must overcome to make your ML processes more efficient. 

• Unrealistic expectations: As the steps are preset, stakeholders usually make unrealistic expectations of end goals. To design big solutions, roll up your sleeves and go into the well of data yourself.
  

• Misleading business metrics: A poor analysis of the model's behavior, impact, and performance can hamper the health of your ML projects.

• Data discrepancies: Data is often sourced from different verticals, which leads to confusing data entries. Perform statistical analyses of raw data to standardize formats and values.

• Lack of data versioning: The version and control model runs to draw a clear line of difference. Don’t let users load improper data versions into the system.

• Inefficient infrastructure: Running multiple experiments can be chaotic and harsh on company resources. Different data versions need a compatible infrastructure with high graphical processing power. Without these, the entire production might come to a halt.

• Tight budgets: Sometimes, senior leadership teams don’t accept a project if it takes too much time or bandwidth. MLOps eats up a lot of resources and capital.

• Lack of communication: A sudden communication outage may occur at any time in the MLOps process. While developers need to manage tasks, they also need to keep the lines of communication running.
  

• Incorrect assumptions: If you’re running a hospital and storing critical patient information, you must verify each critical detail. Incorrect assumptions can result in uneven, erroneous outcomes.
  

• A long chain of approvals: For every model review, a chain of approvals are needed. This includes your IT department, senior leadership, and legal and compliance departments.
  

• Surprising the IT Department: After the model is devised, dev teams often want it produced sooner than possible. They demand expensive setups from IT teams and want them to run system maintenance quickly. The inflexibility of knowledge results in a communication gap between two teams
  

• Lack of iterations: There’s a constant delay between the tech teams. ML engineers handle the data and technology side of the process. DevOps teams take care of business applications and software practices. Toward the end of the ML pipeline, the two teams collaborate. As there is no synergy, there are no iterations for data.

• Not reusable: Most of the time, the data used to build one data model won’t be ideal for another model. The data disparities depend on the different use cases you are committed to.  

What are the best MLOps tools in 2025?

G2 helps businesses find MLOps platforms that allow companies to label, automate, and orchestrate their data models in line with their business operations. An elevation of your data workflows with MLOps paves the way for success.

To be included in this category, software must:

• Offer a platform to manage and monitor ML models.
• Provide an end-to-end deployment environment for ML models.
• Allow users to integrate ML workflows with business applications.
• Run health and diagnostic checks on ML models.
• Provide a holistic MLOps visual dashboard to glean insights from.

1. Vertex AI

Vertex AI is a fully managed machine learning platform designed to simplify the process of building, training, and deploying ML models at scale. It offers seamless integration with BigQuery, AutoML capabilities, custom model support, and end-to-end pipeline orchestration.

What G2 users like best:

“What I like most about Vertex AI is how it unifies the entire machine learning workflow — from data preparation and training to deployment and monitoring. We’ve used it to streamline our ML pipeline, and the integration with BigQuery and Google Cloud Storage makes data handling incredibly efficient. The UI is intuitive, and it’s easy to move between no-code experimentation and full-scale custom model development.”

- Vertex AI review, André P.

What G2 users dislike:

“Sometimes the pricing can be a bit confusing, especially when working with large datasets or long training jobs. Also, documentation could go deeper in some areas for beginners. It’s powerful, but new users might need some time to get used to it.”

- Vertex AI review, João S.

2. Databricks Data Intelligence Platform 

The Databricks Data Intelligence Platform unifies data engineering, analytics, and AI workloads on a single lakehouse architecture. It enables teams to build collaborative ML workflows with MLflow, accelerate development using notebooks, and scale production with powerful automation, governance, and compute optimization.

What G2 users like best:

"I mostly use the Databricks Data Intelligence Platform to mangle large datasets that we store across cloud buckets and create ETL pipelines, as well as stand up notebooks on which I do a lot of explorative work. I very much like that everything feels ready to go, such as clusters start quickly, scaling just works in the background, and I can really stop worrying about infrastructure stuff and focus on analysis.”

- IBM Watson Studio review, Donnie M.

What G2 users dislike:

“The initial setup was a bit confusing, and some of the advanced features could use better documentation. Figuring out the pricing took some time, but once we got going, the benefits were clear.”

- IBM Watson Studio review, Naga Likhita C.

3. Snowflake 

Snowflake is a cloud data platform built for modern analytics and ML workloads. It combines a high-performance data warehouse with native support for Python, Snowpark, and integrated ML tools, making it easy for teams to prepare data, train models, and operationalize insights directly within the platform.

What G2 users like best:

"Depending on the size of your warehouse, we can handle a large amount of data without performance issues. This would be very difficult with an on-prem server. Further, the separation of storage and compute helps with resource management. Additionally, we're a Tableau shop, and Tableau has a built-in connector with Snowflake that is reliable and efficient."

- Snowflake review, Christopher R.

What G2 users dislike:

“Honestly, the toughest part with Snowflake is keeping costs under control. If a query isn’t optimised, just exploring data can get expensive fast since you’re charged for each get processed. Snowflake is that it feels a bit easier to manage costs, you only pay for the compute you actually use, and it can auto-suspend when not in use. That makes it less stressful to dig into data without constantly worrying about the bill.”

- Snowflake review, Ashish S.

4. IBM watsonx.ai

IBM watsonx.ai is an enterprise AI studio for building, deploying, and governing machine learning and foundation models. It offers pretrained models, automated pipelines, and model monitoring, enabling businesses to accelerate AI adoption while meeting compliance and transparency standards.

What G2 users like best:

“What I appreciate most about IBM watsonx.ai is its user-friendly AI studio. I was able to create a chatbot for internal support by leveraging pre-trained models, which made the process much more efficient. This approach saved me a significant amount of time, required minimal coding, and integrated smoothly with our existing systems. As a result, our helpdesk now responds more quickly, leading to greater employee satisfaction.”

- IBM watsonx.ai review, Mayank V.

What G2 users dislike:

“Sometimes, the platform can feel a bit slow, especially when handling large datasets or switching between tools. The user interface, although clean, can be a little overwhelming at first because there are so many options and settings to learn.”

- IBM watsonx.ai review, Denitsa D.

5. Microsoft Fabric

Microsoft Fabric is an end-to-end data and AI platform that brings together data integration, real-time analytics, and machine learning under one unified experience. With deep integration across Azure, Power BI, and Synapse, Fabric helps organizations build intelligent data products and AI-driven applications faster and more securely.

What G2 users like best:

“The integration of all the tools that Microsoft has in just one place makes it easy to use, and it has a high number of features.”

- Microsoft Fabric Review, Enmanuel M.

What G2 users dislike:

"Microsoft Fabric pricing concepts are a bit complex. Solutions deployment within Microsoft Fabric is challenging for new users as they need to learn about tenants, capacities, and workspaces across Azure and Power BI platforms."

- Microsoft Fabric Review, Hosam K.

Related software categories for MLOPs platforms

MLOPs is best known for automating software supply chain. But, to set up a complete machine learning framework, you would need a set of additional tools to label, train and test your model before pushing it into production.

1. Data labeling

Data labeling software is pivotal as it assigns a label to incoming set of data points and categorizes it into clusters of the same data type. Data labeling can help clean the data, prepare it and eliminate outliers for a smooth analysis process.

2. Machine learning

Machine learning software is an intrinsic part of data analysis as it leverages an algorithm to study data and generate an output. This software is typically available as an integrated data environment or a notebook where users can code, fetch libraries and upload or download databases.

3. Data Science and machine learning platforms

Data science and machine learning tools are used to build, deploy, test and validate machine learning models with real life data points. These platforms help in intelligent analysis and decision making with processed data, which enables users to build competitive business solutions. 

Frequently asked questions about MLOps

Got more questions? We have the answers.

Q1. Do I need MLOps if I only have a few models?

Yes. MLOps provides structure, monitoring, and version control, even for small ML projects. It helps ensure models are reproducible, traceable, and easier to maintain over time, even as data changes.

Q2. How do you monitor ML models in production?

ML models are monitored using metrics like prediction accuracy, latency, data drift, and error rates. Monitoring tools can trigger alerts or retraining workflows when performance drops below defined thresholds.

Q3. What causes model drift, and how does MLOps help?

Model drift occurs when real-world data changes over time, making a model less accurate. MLOps platforms detect drift using live data monitoring and automate retraining workflows to keep models up to date.

Q4. Is MLOps only for large enterprises?

No, while enterprise teams benefit greatly from MLOps, small and mid-sized teams can also adopt lightweight MLOps tools to improve reliability, reduce rework, and scale faster with limited resources.

Q5. How does MLOps improve collaboration between teams?

MLOps bridges the gap between data scientists, ML engineers, and DevOps teams by standardizing workflows and using shared tools for deployment, monitoring, and version control. This reduces silos, aligns goals, and speeds up the path from experimentation to production.

Data’s redemption 

Working with machine learning sounds tricky, but it does reap benefits in the long run. Scavenging through the correct machine-learning solution is the only challenge you have at hand. Once you find the sweet spot, half of the job is already done. With MLOps, data glides in and out of your system, making your operations clutter-free, smooth, and crisp. 

Now that you know all about machine learning operations or MLOPs, see how this technology can be used to build revolutionary AI applications.

This article was originally published in 2022. It has been updated with new information.