MLOps: A Brief Explainer, Implementation and Top Tools

When data comes in waves, you need to define a shoreline.

If you want to set up a new retail or manufacturing plant in an area, you can’t just show up with your cranes.

You have to go back to the drawing board and talk numbers. The better you observe data, the sooner you disrupt the market with your sales strategy.

Every business has a swarm of big data flying around in different forms. The main concern is regrouping and restructuring it in a digestible format with machine learning operationalization software or MLOps.

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. 

Activating an MLOps production model saves up a lot of time, resources, and bandwidth for your teams.

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.

MLOps includes ML engineers, software developers, and data operations teams. Every technical stakeholder of your business brings their own expertise to the table to manage data. The more times your data is cross-checked, the better. The MLOps process forms an infinity loop containing all sub-processes.

Software development was the parent concept of MLOps. Soon after, MLOps emerged as a standalone concept.

MLOps implementation

An MLOps framework has several installation layers. If you want to implement it and plug it into your existing stack, check out the three ways mentioned below. 

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. 

Benefits of MLOps

You can only benefit from MLOps if you have a set framework for taking care of machine learning models. It gives you faster time to market and execute your ML projects on time while saving up on resources, cost and data wastage. That being said, let’s check on some benefits of MLOps.

• Better productivity: As MLOps cuts down on data and security hassles, the tech teams don’t have to spend extra time on it. The team utilizes the saved hours to perform other important tasks.

• ML pipeline: 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. 
  

• Efficient collaboration: Performing machine learning runs was a nightmare for ML engineers. Not only did they have to research, build, and version models, but they also needed to know about software practices. Collaboration with the DevOps team eased the stress of ML engineers. Both teams have equal participation in managing the MLOps lifecycle.
  

• Less expenditure: 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. 
  

• 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 awareness: 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.
• Reliability: MLOps is a mechanism that treats your data as a standalone asset. Every step in production is auto-managed. You only have to check in once, supervise, and leave.
• Connectivity: Software teams, data operations teams, engineers, and ML developers connect with each other in MLOps. Labor, resources, and energy are distributed equally, reducing system downtime.
• Monitorability: With MLOps, you can review, recheck, and verify your data elements at any stage of model production.

Importance of MLOps

Operationalizing machine learning across the software lifecycle isn’t easy. While data scientists take care of it, even they feel stranded among large volumes of data. Re-assembling structured or unstructured data without any intervention from other teams takes up a lot of effort and resources. MLOps solves these problems by putting each step into an automation framework. 

Software developers and ML engineers together grow the ML production process. They acquire resources, expenses, and infrastructure requirements. Once raw data is acquired, it goes through several processes of data ingest, data preprocessing, ML model training, model deployment, verification, re-training, and final production rollout and delivery in MLOps. These processes are automated and run on a single environment with preset controls. This essentially means that an ML engineer doesn't have to toil by manually cleaning the data and training the machine learning model through heavy chunks of coding. 

MLOps also focuses on the exchange of information, notebooks, and other rich text documents among data scientists, DevOps, and data engineers, who look after specific stages of the product lifecycle.

MLOps vs. DevOps

There is a clear distinction between MLOps and DevOps, except for the fact that the former deals solely with "artificial intelligence."

MLOps is an engineered care center for machine learning models. Data is molded into multiple ML models, which are carried from the beginning to the end of production through designated steps.

DevOps has flared up as one of the most effective means of software collaboration. It’s a rapid, iterative software feedback mechanism that unravels hidden loopholes in the system. The outcome is higher software quality, faster prints, and a better product. 

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 have 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.

An MLOps lifecycle constitutes machine learning model generation, continuous integration, continuous deployment (CI/CD),  model validation, continuous deployment, model health and performance check, and retraining. This end-to-end framework puts your machine learning models on the assembly line and executes them one by one. 

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.

Building vs. buying vs. hybrid 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.

Challenges of MLOps 

Too many cooks spoil the broth, and too much automation result 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.  

Best MLOps tools in 2024

MLOps platforms 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. DataBricks Lakehouse Platform

What users like best:

“I have been using the Databricks platform for business research projects and building ML models for almost a year. It has been a great experience to be able to run analysis and model testing for big data projects in a single platform without switching between a structured query language server and development environment with Python, R, or Stata. Also, I like the fact that MLflow can track data ingestion for any data shift in real-time for model retraining purposes.”

- Databricks Lakehouse Platform Review, Norman L.

What users dislike:

“I believe it could be a steep learning curve for someone who may not know how to program or have a general understanding of it.”

- Databricks Lakehouse Platform Review, Aashish B.

2. IBM Watson Studio 

IBM Watson Studio or IBM cloud is a leading data solution that creates a low-cost training environment to build, train, and optimize your machine learning models.

What users like best:

"IBM Watson is an all-in-one platform that allows me to build various data solutions with cutting-edge AI technologies and an easy-to-use user interface. It allows me to train AI models with minimal coding experience and seamlessly embed cognitive AI elements into data analysis projects.”

- IBM Watson Studio Review, Hany I.

What users dislike:

“In my opinion, it's kind of hard to code, and the user interface should be better.”

- IBM Watson Studio Review, Ricardo G.

3. Vertex AI Workbench 

Vertex AI Workbench is a Jupyter-style notebook that simplifies your access to data with BigQuery, Dataproc, Spark, and Vertex AI integration. Using Google's security and expertise keeps your consumer and organizational data safe and compliant.

What users like best:

It eases the process of training and deploying machine learning models that can be used for various use cases. In addition, all of their services are well-documented and easily available.

- Vertex AI Workbench Review, Anmol A.

What do users dislike:

“It is pretty difficult to browse through options to import any file or library. It could be improved by creating sections according to the environment, like a virtual environment.”

- Vertex AI Workbench Review, Rishikesh G.

4. Weights and Biases

Weights and biases build better ML models. You can deploy, validate, debug, and reproduce your models with a few lines of code. It helps you compare existing ML projects with each other to cross-verify weight and bias elements. 

What users like best:

“WandB allows my team to collaborate and share information. As soon as we became users of the tool, I noticed that we would spend time analyzing the training loss graphs for model runs and asking each other for help. These runs used to be squirreled away on people's desktop machines, and it was nearly impossible to reconstruct old runs. Now we can look at older runs easily, and our team can collaborate on experiment results. The support from the WandB team has been amazing too.”

- Weights and Biases Review, Chris P.

What users dislike:

“It would be nice if there was model deployment functionality. Also, it would be nice to have a service user option or a team API key. Since our runs are triggered using AWS Sagemaker pipelines, we have had to hardcode one of our team member's user API keys, which isn't the nicest solution since he isn't always the person triggering the run, yet it's still linked to him.”

- Weights and Biases Review, George R.

5. SuperAnnotate

SuperAnnotate is the world’s leading platform for building high-quality ML pipelines for computer vision and natural language processing. It features advanced tooling, quality assurance, data curation, robust SDK, and application integration capabilities. 

What users like best:

“SuperAnnotate is a good tool for image segmentation that offers a helpful support team. The software is easy to use and efficient, making annotation tasks faster and more accurate. It also offers a variety of annotation tools and features, such as customizable hotkeys, collaboration options, and a user-friendly interface.”

- SuperAnnotate Review, Liangyu C.

What users dislike:

“The classes are prefixed, but I hope to add them when annotating. Some annotation projects are quite open-vocabulary. Also, I hope the video segmentation pipeline will use some tracking algorithms to help us annotate automatically.”

- SuperAnnotate Review, Jingkang Y.

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. 

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 in 2024.

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