Machine learning operations, or MLOps, is the process of building, deploying and maintaining machine learning models. It is a discipline that combines machine learning, DevOps and data engineering to find faster, simpler and more effective ways to productize machine learning. When done right, MLOps can help businesses align their models with unique business needs as well as regulatory requirements.
What is MLOps? How Does It Work?
A basic MLOps process looks like this: a business goal is defined, relevant data is collected and cleaned, then a machine learning model is built and deployed.
Productizing machine learning or ML is one of the biggest challenges in AI applications today. Many businesses struggle to figure out how to turn the insights discovered by data scientists into tangible value for their business.
It requires merging multiple processes across multiple teams, starting with the definition of business goals and continuing through data collection, model development and deployment.
This unification is achieved through a set of best practices for communication and collaboration between data engineers who acquire the data, data scientists who prepare the data and develop the model, and operations professionals who service the models.
MLOps Components
Below you can find the MLOps framework that makes up the different components of MLOps:
1. Use case discovery
It involves the identification of business problems, their formulation into a problem statement and targets that can be addressed/solved by ML approaches with relevant KPIs (Key Performance Indicator).
2. Data engineering
Bringing together data engineers and data scientists, this phase involves gathering sufficient data from various sources and preparing the data for the modeling process (processing/validation).
3. Machine learning pipeline
By designing and implementing a pipeline integrated with CI/CD, this phase allows data scientists to use the pipeline for experimentation and model testing and to keep a record of the data and model pedigree along with relevant KPIs throughout all experiments.
4. Production deployment
This phase ensures a secure and seamless implementation on the appropriate production server (public cloud, on-premises or hybrid server).
5. Production Monitoring
In this phase, appropriate monitoring is performed for both the model and the infrastructure.
- Models are monitored through specific KPIs such as input data distribution or changes in model performance. Triggers are made ready for active experimentation using new algorithms and hyperparameters to develop a new machine learning pipeline.
- Infrastructures are monitored according to memory and computational requirements and scaled as needed.
Why MLOps Matters?
Machine learning (ML) is a core technology that can be used to create solutions that uncover previously untapped revenue streams, save time and reduce costs. ML can make workflows more efficient, leverage data analytics in decision-making and improve the customer experience.
However, machine learning experts believe that achieving these goals is only possible with a solid framework. MLOps is a set of applications that combine ML and DevOps to automate the entire ML lifecycle, from model development to deployment and monitoring in production.
MLOps can help businesses do the following:
- Reduce time to market for new ML models.
- Improve the quality of machine learning models.
- Increase the reliability of machine learning models.
- Reduce the cost of machine learning operations.
- Align models with business needs.
- Meet regulatory requirements.
Whatever their constraints, individuals, small teams and even businesses can use MLOps to achieve their goals. MLOps can help businesses to be more agile and strategic in their decisions, saving time and money.
MLOps are not a set of rigid rules. It is a set of practices that can be tailored to the specific needs of each business. So businesses can experiment with different settings and keep only what works for them.
MLOps is a powerful tool that can help businesses get more value from their machine learning investments. By implementing MLOps, organizations can achieve their business goals and increase their competitive advantage.
Advantages of MLOps
MLOps can benefit in several ways. These include the following:
1. Scaling
MLOps applications are essential for scaling an enterprise’s machine learning-driven applications.
2. Building trust
MLOps develops a bond of trust to control machine learning applications in dynamic environments and establish a repeatable process through automation, testing and validation. It also improves the accuracy, validity and efficiency of developing machine learning models.
3. Correct data utilization
When considering how to manage effective business operations, MLOps relies heavily on big data. The method tends to improve products with each successive iteration, which shortens development lifecycles and reveals powerful insights.
MLOps also approaches a lot of feedback, which helps to analyze what the noise in the data is and what anomalies need to be taken into account.
4. Improved communication, smooth workflow
Defined in a well-structured format, MLOps follow a set of practices to seamlessly combine the development cycle and operations process to make it more efficient.
In particular, the data science team has an extensive knowledge of data and the operations team is aware of the business acuity. In this case, MLOps, together with the expertise of both teams, improves the efficiency of ML models. This collaboration reduces the barriers generated by sophisticated algorithms, while MLOps transforms ML production lines to be more adaptable and flexible to coordinate KPI-driven models.
5. Reducing risks
Due to inaccurate and deceptive models, businesses are constantly faced with the risk of losing or losing the trust of customers, not reaching the target audience, etc. For example, training data and current real-world complex data are different, which leads to poor predictions as it makes models less responsible, where MLOps reduce business risks.
Furthermore, MLOps avoid biases during model development, e.g. missed opportunities and legal risks.
The performance of ML models is somewhat difficult to formulate and therefore MLOps aims to implement and achieve the ML model lifecycle in its entirety across the enterprise, including reduction of technical debt, early detection of small risks, fast time-to-market solution as soon as possible for model performance.
What Problems Do MLOps Solve?
Implementing MLOps best practices solves several problems that plague businesses around the world:
1. Poor Communication
Without collaboration, you can forget about simplifying and automating the deployment of machine learning models in large-scale production environments. MLOps solves this problem by creating dynamic pipelines and adaptive frameworks that keep everyone on the same page, reducing friction and opening bottlenecks.
2. Unfinished Projects
According to VentureBeat, 87% of machine learning models never make it to production. In other words, only 1 in 10 data scientists’ workday ends up producing something of value to the company. This sad statistic represents lost revenue, wasted time, and a growing sense of frustration and fatigue among data scientists everywhere. MLOps solves this problem by ensuring that all key stakeholders are involved before a project starts. MLOps then supports and optimizes every step of the process, ensuring that every model is on its way to production without delay (and without never-ending email chains).
3. Lost Learnings
Building and delivering machine learning models requires input and expertise from multiple different teams, with each team executing a different part of the process. Without communication and collaboration between everyone involved, important information and critical insights are trapped within each silo. MLOps solves this problem by bringing disparate teams together in a single hub for testing and optimization. MLOps best practices make it easy to share learnings that can be used to improve and rapidly redeploy the model.
4. Redundancy
Too often, long development and deployment cycles mean that changing business goals make models redundant before they are fully developed, or changing business goals mean that the ML system needs to be retrained immediately after deployment. MLOps solves these problems by applying best practices throughout the entire process, enabling ML to be produced faster at every stage. MLOps best practices also create room for adjustments, so your models can adapt to your changing business needs.
5. Misuse of Talent
Data scientists are not software engineers and vice versa. They have different focuses, different skill sets and very different priorities. Expecting one to fulfill the tasks of the other is a recipe for failure. Unfortunately, many organizations make this mistake when trying to shorten or speed up the process of getting machine learning models into production. MLOps solves this problem and sets the stage for long-term success by bringing both disciplines together in a way that allows them to utilize their respective capabilities to the best of their abilities.
6. Dissonance
The era of big data is accompanied by an era of dense, ever-changing regulatory and compliance systems. Many businesses struggle to meet data compliance standards, let alone remain adaptable for future iterations and additions. MLOps solves this problem by implementing a comprehensive plan for governance. This ensures that every model, whether new or updated, is compliant with the original standards. MLOps also provides monitoring tools, ensuring that all data programs are auditable and explainable.
The Difference Between MLOps and DevOps
MLOps and DevOps are a set of practices aimed at improving the speed, efficiency and quality of software development and deployment. However, there are some key differences between the two approaches:
- Focus: DevOps focuses on software development and deployment, while MLOps focuses on the entire ML lifecycle.
- Automation: DevOps automates the software development and deployment process, while MLOps automates the entire ML lifecycle.
- Monitoring: DevOps focuses on monitoring the software application in production, while MLOps focuses on monitoring the ML model in production.
- Roles and Responsibilities: DevOps teams typically consist of software developers, systems engineers and quality assurance engineers. MLOps teams typically consist of data scientists, ML engineers and DevOps engineers.
The best approach for a business will depend on its specific needs and goals. For example, if a business wants to improve the speed, efficiency and quality of software development and deployment, DevOps might be a good option. On the other hand, if a business wants to improve the speed, efficiency and quality of the ML lifecycle, MLOps may be a better option.
In some cases, businesses may choose to implement both DevOps and MLOps. This can be a good option for organizations that need to increase the speed, efficiency and quality of software development and machine learning.
More details about DevOps: https://devopstipstricks.com/what-is-devops-for-beginners/
Implementation of MLOps
MLOps is a set of practices that combine machine learning and DevOps to automate the entire ML lifecycle, from model development to deployment and monitoring in production.
Here are the steps on how to implement MLOps:
1. Establish a Clear Governance Framework
A clear governance framework is essential to ensure the success of MLOps initiatives. This framework should define the roles and responsibilities of all stakeholders, as well as the processes and procedures for managing the ML lifecycle.
2. Define Roles and Responsibilities
It is important to clearly define the roles and responsibilities of all stakeholders in the MLOps process. This will help to ensure that everyone knows what they are responsible for and that there is no duplication of effort.
3. Use a Common Tool and Technology Set
Using a common set of tools and technologies can help increase the efficiency and effectiveness of MLOps initiatives. This is because it can make it easier to share data and collaborate on projects.
4. Automate as Much of the ML Lifecycle as Possible
Automating as much of the machine learning lifecycle as possible can reduce the risk of error and increase the efficiency of the process. This can be done using various tools and technologies such as continuous integration and continuous delivery (CI/CD) pipelines.
5. Tracking ML Patterns in Production
Monitoring ML models in production is crucial to ensure they perform as expected. This can be done using various tools and techniques such as metrics, logging and alerts.
By following these steps, businesses can effectively implement MLOps and achieve the desired business results.
In summary, MLOps is a systematic process for developing, deploying and monitoring machine learning (ML) solutions. It is an engineering discipline applied to a variety of industries and use cases.
MLOps acts as a ladder between collecting data and gaining actionable insights to add business value; a strong MLOps strategy facilitates data science best practices and operations to streamline scalable and repeatable ML models, and empowers organizations to enter the new data-driven era and reap real-world ML and AI benefits. The robust integration of data science and DevOps enables executives and individuals to be more agile and productive.
