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What is MLOps, and why do we need it?

By Aliona Surovtseva, Innovation Analyst
Published on

Making the leap from a proof-of-concept to a production-ready application is one of the hardest tasks in the field of machine learning. The point is that ML models that perform flawlessly in a lab environment frequently fail when applied to real-world scenarios. Only 32% of data scientists surveyed say their ML models usually deploy. The pervasive failure of AI/ML projects comes mainly from the lack of structured framework and standardized processes that can help with the shift.

This is where machine learning operations, or MLOps, comes in handy.

Machine learning operations has played a pivotal role in reinventing the way we approach machine learning development. So what is MLOps, and why do we need it?

The purpose of our article is to provide a thorough exploration of machine learning operations, give a clear and concise MLOps definition, overview its key components, and explain why MLOps is important to implement and how to get it right.

Leverage ITRex’s MLOps consulting services to learn more about MLOps possibilities in your sector.

What is MLOps?

You can encounter a wide variety of MLOps definitions on the web. At ITRex, we define MLOps as follows.

Generally, the primary objective of MLOps is to streamline the process of deploying, maintaining, and tracking machine learning models in production environments by bridging the gap between data scientists, ML developers, and operations teams. As the statement above suggests, MLOps is a collaborative approach that amalgamates machine learning, data science, and software engineering into one cohesive practice.

More fundamentally, MLOps applies to the entire machine learning lifecycle—data collection, exploratory data analysis, data preparation, feature engineering, model training and development, model deployment, model monitoring, and model retraining. It offers a structured framework to support the seamless transition of machine learning models from the experimental to the live environment.

mlops definition

Key components of MLOps

What is MLOps in terms of its key elements? While there may be more, the following are the most crucial components of MLOps that work together to streamline the end-to-end process of deploying and maintaining machine learning models, ensuring reliability, scalability, and efficiency:

  • Collaboration

    As we’ve mentioned previously, with MLOps, teams can collaborate more effectively and quickly to pool their knowledge and expertise to construct machine learning models that are faster, more scalable, and more broadly applicable. In contrast, if we examine the traditional scenario of collaborating on ML projects, we observe a disjointed and unrelated collection of people with entirely different sets of skills. In this way, MLOps offers a solid framework and a set of tools and techniques to facilitate effective collaboration across data scientists, ML engineers, and operations teams.

  • Automation

    The goal of MLOps is to automate every step of the ML workflow to ensure repeatability, consistency, and scalability. Changes to data and model training code, calendar events, messages, and monitoring events can all act as triggers for automated model training and deployment. A crucial component of MLOps is automated reproducibility, which ensures the accuracy, traceability, and stability of machine learning solutions across time.

  • CI/CD

    MLOps involves using continuous integration and deployment (CI/CD) techniques to help facilitate collaboration between data scientists and machine learning developers and thus speed up the creation and production of ML models.

  • Version control

    A large number of events can result in changes to the data, code base, or an anomaly in a machine learning model. There is a code review phase for every ML training code or model specification; each is versioned. Version control is a crucial aspect of MLOps used to track and save different versions of the model. This makes it easy to reproduce results and revert to a previous version in case any issue arises.

  • Real-time model monitoring

    The job is far from done once a machine learning model is put into use. MLOps allows organizations to continuously track and assess the performance and behavior of machine learning models in production environments. Real-time model monitoring helps swiftly identify and address issues, thereby ensuring the model remains effective and accurate over time.

  • Scalability

    There are several ways MLOps contributes to scalability. One of the ways is through the automation of ML pipelines. This automation reduces the need for manual intervention, allowing for quicker and more reliable scaling of ML operations​. Another way MLOps ensures scalability is through continuous integration/continuous deployment techniques. By putting in place CI/CD pipelines, new code and models can be automatically tested and released, cutting down on time to market and facilitating the quick scaling of machine learning solutions.

  • Compliance

    MLOps ensures that machine learning models are created and deployed in an open, auditable manner and adhere to rigorous standards. Furthermore, MLOps can aid in improving model control, guaranteeing proper and ethical conduct, and preventing bias and hallucinations.

Why do we need MLOps?

The broad answer to the question “What is MLOps and why do we need it?” can be outlined as follows. Taking machine learning models to production is no mean feat. Machine learning lifecycle consists of many complex phases and requires cross-functional team collaboration. Maintaining synchronization and coordination between all of these processes is a time and resource-consuming task. Thus, we need some standardized practices that could guide and streamline all processes across the ML lifecycle, remove friction from ML lifecycle management, and accelerate release velocity to translate an ML initiative into ROI.

To explain this further, let’s explore the main reasons why organizations need MLOps.

1. ML models perform poorly in production environments

There are a number of reasons for ML models to underperform in production environments. Failed productionized ML models mostly arise from data dismatch, model complexity, overfitting, concept drift, and operational issues. Operational issues relate to the technical difficulties of implementing and running a model in a dynamic environment, including compatibility, latency, scalability, reliability, security, and compliance. When a model has to interact with other systems, components, and users as well as manage changeable workloads, requests, and failures, it might not function as well in a real-world production environment as it would in a regulated and isolated one.

Addressing these challenges often requires a combination of careful model selection, reliable training procedures, continuous monitoring, and close collaboration between data scientists, ML engineers, and domain experts. MLOps is the newest field meant to prevent and tackle these problems with strict, automated monitoring throughout the entire pipeline, from collecting, processing, and cleaning the data to model training, generating predictions, assessing model performance, transferring the model output to other systems, and logging model and data versions.

2. Limited collaboration between data science and IT teams

The traditional way of deploying ML models into production is a disjointed process. After a model has been created by data scientists, it is passed on to the operations team for deployment. This transfer frequently leads to bottlenecks and challenges because of complex algorithms or disparities in the settings, tools, and goals.

MLOps promotes collaboration that weaves together the expertise of siloed teams and thus helps to lessen the frequency and severity of these kinds of problems. This improves the efficiency of machine learning model development, testing, monitoring, and deployment.

3. Failure to scale ML solutions beyond PoC

The desire to extract business insights from massive amounts of data is constantly increasing. This has led to the requirement for machine learning systems to be adaptable to changing data types, scale with rising data volumes, and reliably produce accurate results even in the face of uncertainties associated with live data.

Many organizations have a hard time utilizing machine learning in its more advanced versions or applying it more broadly. According to the McKinsey survey, only about 15% of respondents have successfully operationalized ML at scale. Another survey by Gartner found that only 53% of AI initiatives successfully transition from prototype to production. This mostly relates to the inability of ML solutions to be applied in a commercial environment with rapidly scaling data.

This mainly arises from different teams working on an ML project in isolation—siloed initiatives are hard to scale beyond a proof of concept, and crucial operational elements are often disregarded. MLOps serves as a standardized set of tools, culture, and best practices that involve a number of defined and repeatable actions to address all ML lifecycle components and ensure a reliable, quick, and continuous production of ML models at scale.

4. The abundance of repetitive tasks in the ML lifecycle

The MLOps approach helps shorten the ML development lifecycle and boost model stability by automating repetitive processes in the workflows of data science and engineering teams. In addition, by eliminating the need to repeatedly complete the same steps in the ML development lifecycle, automation allows different teams to become more strategic and agile in ML model management and focus on more important business problems.

5. Faster time-to-market and cost reductions

A standard machine learning pipeline consists of multiple phases, including data collection, pre-processing, training models, assessment, and deployment. Conventional manual approaches frequently result in inefficiencies at each stage—they are time-consuming and labor-intensive. Fragmented processes and communication gaps impede smooth ML model deployment. Problems with version control can cause confusion and wasted effort. These inefficiencies lead to faulty models, sluggish development cycles, excessive costs, and eventually lost commercial prospects.

Lower operating expenses and quicker time-to-market are two main benefits of automating model creation and deployment with MLOps. The goal of the newly-emerging area of MLOps is to give the ML lifecycle speed and agility. With MLOps, ML development cycles become shorter, and deployment velocity rises. Effective resource management, in turn, leads to significant cost reductions and faster time-to-value.

A high-level plan for implementing MLOps in an organization

Implementing MLOps in an organization involves several steps to enable a seamless transition to a more automated and efficient machine learning workflow. Here is a high-level plan from the ITRex experts:

  1. Assessment and planning:
    • Identify the problem to be solved with AI

    • Set clear objectives and assess your current MLOps capabilities

    • Ensure cross-functional collaboration between your data science and IT teams, clearly defining roles and responsibilities

  2. Establish a robust data pipeline:
    • Set up a reliable and scalable data ingestion process to collect and prepare data from various sources

    • Implement data versioning and lineage tracking to maintain transparency and reproducibility

    • Automate quality assurance and data validation processes to guarantee accurate and reliable data

  3. Set up infrastructure:
    • Decide whether you should build MLOps infrastructure, buy it, or go hybrid

    • Select an MLOps platform or framework that aligns with the organization’s needs, preferences, and existing infrastructure

    • A good option is to utilize fully-managed end-to-end cloud services like Amazon SageMaker, Google Cloud ML, or Azure ML equipped with the advantageous feature of auto-scaling and offering algorithm-specific features like auto-tuning of hyper-parameters, easy deployment with rolling updates, monitoring dashboards, and more

    • Set up the necessary infrastructure for ML models training and tracking model training experiments

  4. Streamline model development:
    • Use version control systems like Git and implement code and model version control solutions

    • Leverage containerization (e.g., Docker) to ensure consistent and reproducible model training environments

    • Automate model training and evaluation pipelines to enable continuous integration and delivery

  5. Implement model monitoring:
    • Establish thorough monitoring for system health, data drift, and model performance

    • Define key metrics to measure the quality of the model

    • Use tools for model performance monitoring with alert and notification mechanisms to notify stakeholders of any issues or anomalies

  6. Ensure model governance and compliance:
    • Provide procedures for detecting bias, evaluating fairness, and assessing model risk

    • Establish strict access controls and audit trails for sensitive data and model artifacts.

    • Ensure compliance with industry and region-specific regulatory requirements and privacy guidelines by protecting data and models from security threats (through access control, encryption, and regular security audits)

  7. Automate model deployment:
    • Adopt a containerized or serverless approach to deploy and serve your models

    • Select an effective model deployment strategy (batch, real-time, etc.)

    • Configure CI/CD pipelines with automated testing, integration of data and code updates, and automatic deployment of ML models into production environment

  8. Monitor and maintain:
    • Refine MLOps practices and establish feedback loops for continuous model optimization

    • Implement automated tools for model retraining based on new data or triggered by model degradation or drift; the same goes for hyperparameter tuning and model performance assessment

Why collaborate with an MLOps company?

Partnering with an MLOps company can offer numerous benefits and advantages for organizations seeking to successfully implement MLOps practices. Let us outline the most common ones:

  • Specialized knowledge

    MLOps firms offer teams of seasoned professionals with expertise in machine learning, software engineering, data engineering, and cloud computing across a range of sectors and use cases, capable of providing valuable insights and best practices tailored to your specific needs.

  • Faster implementation

    MLOps experts help expedite the adoption of MLOps methods by offering tried-and-true frameworks, tools, and processes. They use established processes to create roadmaps, define goals, evaluate the current state of your company, and carry out ML implementation plans effectively.

  • Avoiding common pitfalls

    Adopting MLOps comes with its own hurdles. Experienced MLOps professionals can help anticipate potential pitfalls, navigate complex technical landscapes, and take proactive measures to address issues, thereby mitigating risks associated with implementing MLOps practices.

  • Access to the latest tools and technologies

    It might be challenging for organizations to navigate the technology landscape because of the multitude of tools and platforms used for different stages of the machine learning lifecycle. MLOps engineers can help navigate this maze and recommend and deploy cutting-edge solutions that may not be readily available or accessible to your organization.

  • Tailored approach

    MLOps companies are able to customize their offerings to fit the particular needs, goals, and limitations of your company. They are able to evaluate your current workflows, infrastructure, and skill sets in order to create solutions that are specifically tailored to business needs and objectives.

Here, at ITRex, we help organizations harness the full potential of ML models effortlessly. ITRex’s MLOps team matches technological skills with business knowledge to produce an iterative, more structured ML workflow. Our extensive expertise in all AI domains, from classic ML to deep learning and generative AI, a strong data team, and an internal R&D department allow us to build, deploy, and scale AI solutions that generate value and translate into ROI.

For instance, our MLOps experts helped a social media giant with dozens of millions of users improve live stream content moderation by developing an ML tool and applying MLOps best practices. The client wanted to develop AI algorithms that would automate live stream content policing and implement MLOps approach to accelerate the deployment of the model. Our ML/AI engineers built a computer vision model for sampling and analyzing live streams, and MLOps engineers transferred the model to a graphical processing unit (GPU) to improve the ML model’s throughput performance. Go to the case study page to learn about the results of the project.

Key takeaways

  • MLOps definition refers to a set of practices for collaboration and interaction between data scientists and operations teams, designed to enhance the quality, optimize ML lifecycle management process, and automate and scale the deployment of machine learning in large-scale production environments.

  • Putting ML models into wide-scale production requires a standardized and repeatable approach to machine learning operationalization.

  • MLOps includes essential components that are key to successful ML project implementation and also help answer the question “What is MLOps and why do we need it?”. These are collaboration, automation, CI/CD, version control, real-time model monitoring, scalability, and compliance.

  • The key reasons why MLOps is important and why organizations should look forward to adopting it include poor performance in production environment, ineffective collaboration between data science and operations teams, inability to scale ML solutions to enterprise production, a plethora of repetitive tasks in the ML lifecycle, slow development and release cycles, and excessive costs.

  • Hiring MLOps experts means getting access to specialized knowledge, the latest tools and technologies, reducing the risks associated with implementing MLOps practices, accelerating the deployment of ML models, getting expert help tailored to your business needs, and achieving faster returns on AI/ML investments.

What is MLOps?Key components of MLOpsWhy do we need MLOps?1. ML models perform poorly in production environments2. Limited collaboration between data science and IT teams3. Failure to scale ML solutions beyond PoC4. The abundance of repetitive tasks in the ML lifecycle5. Faster time-to-market and cost reductionsA high-level plan for implementing MLOps in an organizationWhy collaborate with an MLOps company?Key takeaways
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