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While three out of four marketing executives believe that their business goals can be better accomplished with the use of machine learning and automation, the technology continues to gain in relevancy and popularity. However, the complexity of model training makes the technology harder to access for many companies. Machine learning operations, or MLOps, is a set of practices aimed at simplifying and streamlining model development. Using data science services, organizations adopt MLOps techniques and build models faster and more efficiently.
In this article, we will cover what MLOps is, explore its use cases and benefits, and define best practices that optimize machine learning projects.
Before we delve into the nuances, let’s answer the question, “What is MLOps?” Machine Learning Operations (MLOps) is the practice that focuses on delivering consistent and scalable machine learning models. It’s a collaborative framework that helps engineering teams to develop higher quality ML models during each iteration. The MLOps teams are usually composed of data scientists, machine learning experts, DevOps specialists, and other IT team members.
For example, machine learning in marketing provides data-driven insights and enhances decision-making, which helps marketers better understand their customers and create effective campaigns. Machine learning in healthcare delivers a range of helpful products such as online symptom checkers, medical imaging, and robotic surgeries. Finally, machine learning in education enables more solutions for inclusive and adaptive learning as well as AI tutors and chatbots.
While machine learning applications grow in demand, the need for a coherent set of processes and practices has become more relevant. MLOps has garnered a wide range of use cases, including model building and deployment, data management, data integration, model maintenance, and more.
The main objective of MLOps is to build and deploy AI and machine learning solutions. MLOps practices aid in the standardization of the model development lifecycle and automation of model training. This can be achieved by offering open communication among the MLOps teams as well as incorporating continuous integration and continuous development (CI/CD) principles.
While seamless collaboration helps teams to stay on the same page, your engineers can consistently reproduce models, datasets, and code and deliver reliable results. Additionally, MLOps practices provide automation for machine learning workflows, thus streamlining the creation and deployment of models.
Let’s take a look at some popular use cases of MLOps:
Fraud detection: Data scientists develop and train machine learning models that detect fraudulent transactions in real time. Using these insights, businesses can take proactive steps to mitigate or even avoid cyber threats.
Predictive maintenance: Based on a trained and validated model, engineers can deliver an accurate prediction service when it comes to equipment maintenance. Having access to real-time data allows business owners to repair their machines before the damage hinders operations.
Personalized recommendations: An MLOps-trained model allows engineers to generate user recommendations based on their search, order history, and other behaviors. Offering personalized suggestions increases user satisfaction and provides a better shopping experience.
Customer churn: Training data in telecommunications companies allows data scientists to make accurate predictions pertaining to customer churn rates. These findings allow managers to reassess their current strategies and improve customer experience.
Imaging in healthcare: MLOps and deep learning models facilitate computer vision production and help physicians identify abnormalities in medical images. These findings support doctors in diagnosing and improve patient satisfaction and treatment efficiency.
Demand forecasting: Based on data analysis, MLOps can deliver accurate predictions in the retail industry. Engineers can deploy a trained model that considers historical sales data and other factors to forecast product demand. Accurate forecasting helps companies minimize the risks of overstocking, reduce waste, and enhance the efficiency of the entire supply chain.
The specific elements of machine learning ops vary depending on the project. While some projects require complex procedures throughout the entire span, others only involve MLOps during the deployment stage. However, at the core of MLOps lies data management that deals with data collection, storage, and processing.
Another vital component is model development which revolves around designing, training, and tuning of machine learning models. To ensure the model meets the predefined criteria, engineers run tests to validate the model’s efficiency and reliability. They facilitate the environment via tools to enable exploratory data analysis for the project. The successfully validated model is then moved to the deployment phase to later utilize it in the production environment. MLOps components also include model monitoring and management to track its performance metrics and improve its efficiency.
Since both roles deal with machine learning and software engineering, MLOps and ML engineers are often confused with each other. In reality, ML operations engineers consider the larger picture and work on enhancing and automating the entire end-to-end machine learning workflow. In this section, we’ll take a look at the duties and responsibilities of both professionals and explore the differences between the roles.
Machine learning engineers are tasked with designing and building models, processing data, and improving the model performance. First and foremost, they are in charge of training and implementing ML algorithms to deliver valuable insights into the collected dataset and help businesses make smarter decisions.
On the other hand, an MLOps engineer is tasked with facilitating the efficient deployment of ML models. From implementing CI/CD principles and monitoring the performance metrics to optimizing and automating workflows, MLOps specialists are in charge of the operational aspects of managing machine learning models in the production environments.
The main distinction between ML and MLOps engineers lies in the project phases. Machine learning engineers largely operate in the development stage, while MLOps engineers deal with the production phase. In this section, let’s delve into concrete differences between machine learning engineers and MLOps engineers.
A machine learning engineer is responsible for selecting the best ML algorithms and frameworks to solve the business’s problem. They evaluate different available solutions and, based on their performance, scalability, and interoperability, choose the suitable tools. An ML operations engineer has to include other aspects in their evaluation process. For example, they assess the solution’s capacity for data management, model deployment, and model monitoring to ensure the tools can integrate with the existing infrastructure.
While ML engineers solely focus on the development and training of machine learning models, MLOps experts are involved with the model deployment process. Their duties encompass facilitating the necessary infrastructure, automating the deployment procedures, and integrating the models with the existing systems. Additionally, MLOps engineers maintain the deployed model to update and scale it when required.
Machine learning engineers set performance metrics and monitor their progress during the model development and testing phases. MLOps engineers, on the other hand, are in charge of the deployment stage. They provide comprehensive model monitoring to estimate the performance of machine learning models in production.
In the same vein, ML experts focus on the model development phase, and MLOps specialists handle the deployment tasks. While ML engineers troubleshoot bottlenecks associated with the model performance or model drift and try to improve the model’s accuracy, MLOps experts deal with the production environments. They are in charge of infrastructure management, data pipeline disruptions, and model deployment. However, most of the time, these engineers work together to pinpoint and repair issues that affect the performance of ML models.
While ML specialists focus on generating version control to keep track of model code iterations, MLOps engineers create strategies that encompass the entire machine learning pipeline. From model management and artifacts to deployment configurations, they develop and implement versioning systems that complement the CI/CD pipeline and enable smooth updates and rollbacks.
DevOps stands for development and operations and is a broader term in software development. While MLOps is used for machine learning projects, DevOps can be applied in virtually any software development life cycle. In this part, we’ll focus on the core differences between MLOps and DevOps practices and processes.
DevOps practices are closely tied to CI/CD deployment of software systems and involve the automation of processes associated with it to offer frequent and stable releases. In contrast, MLOps’ continuous integration and continuous development pipelines focus on providing replication and traceability of machine learning experiments and models. By broadening the principles of continuous integration and continuous delivery, MLOps is tasked with automating the entire ML workflow and handling ML artifacts like model and feature engineering.
DevOps is concerned with the general workflow in software development production and promotes collaboration between MLOps teams, process automation, and a continuous feedback loop. By streamlining processes related to developing, testing, and deploying, DevOps experts achieve high-quality code and products.
MLOps is focused on machine learning projects specifically and optimizes the end-to-end workflows of creating and deploying ML models in production. MLOps engineers rely on their expertise to tackle ML-specific challenges like data dependency and model experimentation. They employ MLOps practices such as versioning, model registry, and experiment tracking to deliver compliant and reproducible workflows.
DevOps strives to achieve automation in software development and deployment processes by automating code builds, unit tests, and integration tests. DevOps engineers rely on various tools like Jenkins to implement continuous integration and Kubernetes for efficient deployment. MLOps performs ML-specific automation tasks, including feature engineering, model training, and data ingestion. Their toolkit involves Apache Airflow for data pipeline management, MLflow for experiment tracking, Amazon SageMaker as the best cloud-based technology for machine learning, and others.
Your choice of methodology is contingent on the project’s specificity, complexity, and maturity. If you’re building machine learning models, MLOps would be more helpful. However, businesses in the early stages of ML model development may gain more from the benefits of DevOps. A more general framework, DevOps will allow you to adopt certain practices to gradually pivot to MLOps when the ML project demands.
Why do companies embrace MLOps? From process automation and increased model performance to cost optimization and shorter time-to-market, MLOps offers an array of advantages to IT teams.
MLOps practices are designed to ensure the quality of data used in training a machine learning model. By conducting range checks and schema validation, data scientists can eliminate anomalies and errors. Furthermore, adopting automated data validation pipelines enables compliance with expected data formats and quality standards.
Model validation practices include cross-validation and performance metrics assessment. They allow data scientists to evaluate the model performance and identify issues. By establishing automated model validation procedures, software engineers can guarantee consistent and reliable performance of their models across iterations.
One of the integral goals of MLOps is to produce replicable machine learning experiments and results. Data scientists employ various techniques like version control, infrastructure management, and automation to ensure the models and results can always be reproduced. This feature is vital for debugging and auditing ML projects as well as for future collaboration.
Using various specialized tools, MLOps engineers can record and monitor different model versions and performance metrics. The centralized version control enables MLOps teams to organize and analyze their outcomes and deliver data-driven decisions. Moreover, version control allows for improved transparency and management of the machine learning model during the development process.
Suitable MLOps tools can significantly boost your productivity and efficiency, but with one caveat: You have to select the solution that aligns with the project’s needs and goals. By choosing the right combination of MLOps systems, you can streamline your workflows, automate processes, and reduce overhead costs.
MLOps approaches can minimize the time and effort required to build and deploy models. Task automation in the model training and deployment eliminates manual labor and reduces errors, which speeds up the model development process. The integration of CI/CD pipelines and optimization of ML workflows can also aid in delivering high-quality solutions faster and cheaper.
MLOps demands standardized workflows and documentation practices that enhance communication among data scientists, ML engineers, developers, and other IT team members. By continuously exchanging knowledge and progress, teams can align their efforts and achieve goals faster and more effectively.
Maintaining rigorous documentation and versioning allows data scientists to achieve compliance with regulatory requirements and industry standards. MLOps practices aim to establish clear roles and responsibilities within the teams, create transparent approval processes, and reduce risks of an unusable or erroneous ML model.
MLOps engineers utilize containerization platforms like Kubernetes to unlock scalable deployment across various environments. The modular structure of MLOps architectures allows businesses to adapt to changing circumstances and maintain effective ML workflows.
The minimized manual effort required to manage ML models and workflows can also reduce the costs of model development. MLOps solutions also provide cost monitoring features that help companies take control of their spending. Finally, enhanced productivity and accelerated development will also lead to cost optimization.
Implementing a new practice across the organization is a challenging undertaking. Especially when it comes to complex technologies like machine learning, educating staff, changing current standards and regulations, and instigating the cultural shift is a lengthy and difficult process. Using best practices can speed up and simplify the change as well as allow a gradual and steady adoption.
In this next part, we’ll investigate best practices for adopting MLOps practices and implementing them in the most beneficial and smooth way.
One of the pivotal aspects of MLOps is the automation of model deployment. This enables consistency in the deployed models and ensures they adhere to standards and best practices established in the industry. Process automation helps data scientists to minimize errors in the model and prevent them from moving to production.
Furthermore, automation enables faster development and shortens time-to-market. As a result, data scientists benefit from data-driven insights much quicker and can implement these findings to enhance their business.
Building a simple ML model at first will shorten each iteration and allow data science teams to identify issues sooner. Less complex models facilitate better conditions for young teams to establish and fine-tune the infrastructure before moving to more challenging scenarios.
Additionally, simpler models are easier to test and debug which is quite beneficial for MLOps beginners. They are also more scalable as growing a smaller project is much easier compared to a large complex endeavor. Finally, simpler models allow teams to create and refine a robust infrastructure.
Shadow deployment is basically a training wheel that enables teams to test the performance of the model without disrupting its functions. It can process the same data in a pseudo-production environment without impacting the actual product. Using this technique, you can safely identify and remedy bottlenecks before releasing the model.
You can build a shadow deployment model by setting up the infrastructure that allows you to run multiple models at the same time. Next, direct input data to both production and shadow deployment models, separately collect the outputs, and monitor the performances.
Data sanity checks are quick tests that verify whether the environment is performing efficiently. They help teams to ensure that incoming data is in compliance with the required formats and types of data. To avoid inconsistencies, develop MLOps strategies for identifying and fixing erroneous inputs, duplicates, and missing values.
Scripts are great tools for process standardization across the company. Since data preparation is a complex process involving cleaning and transforming of data, having a reusable script can significantly improve the process quality. To generate the script, split the task into smaller sub-tasks to simplify the process for your teammates.
Furthermore, preparing data for machine learning can be automated with various tools and techniques. Automation will help you reduce errors and risks and allow you to improve efficiency. Finally, rely on version control to oversee the changes in the scripts and ensure that old versions fall out of use.
Similarly to shadow deployment models, parallel training experiments also allow organizations to test various parameters. They’re mainly used to enhance the existing ML model by experimenting with different architectures, hyperparameters, and data preprocessing approaches without jeopardizing the original model.
Testing different configurations at the same time makes it more likely for you to find the right combination that works for your needs and requirements.
Setting up measurable and reasonable metrics is vital for coherent tracking. Having a clear and understandable benchmark allows a data science team to assess the model’s performance and make necessary adjustments. To establish a tangible metric, align it to the goal of the project. Additionally, make sure the metric is understood by your team, including both technical and non-technical employees.
Continuous monitoring of deployed ML models ensures they remain consistent throughout the changing environments. For example, the data distribution can change over time, which will impact the performance of the ML models. If your team monitors the models on a regular basis, you’ll be more likely to catch anomalies and retrain the models to ensure their efficiency.
In MLOps, continuous monitoring involves collecting and analyzing KPIs, tracking data for errors and anomalies, optimizing resource usage to meet the demands, and enabling alerts to inform engineers of a potential issue.
To avoid violating user privacy and to prevent biases and discrimination, it’s critical to create and enforce regulations and guidelines. Based on current laws and rules, establish how your team should adhere to privacy requirements. Not only can non-compliance lead to legal and monetary penalties, it can also cause reputational losses to your organization.
Establishing clear communication channels within and between the teams is key to a successful and fruitful model training and development. From defining project scope and deliverables, maintaining documentation, and organizing regular meetings to utilizing version control, it’s important to make sure everyone is on the same page.
Machine learning projects are challenging due to their complexity, resource demand, and need for processing enormous data volumes. However, working with a trusted agency can mitigate the majority of your concerns. If you’re interested in the machine learning life cycle, would like to improve model performance of your existing projects, or want to learn more about the benefits of data science, reach out to NIX. We’re a team of seasoned engineers with decades of experience across various sectors. Contact us to discuss your needs and implement MLOps techniques that can help you achieve your business goals.
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