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MLOps (Machine Learning Operations)

Reviewed by Kuntal ChakrabortyCheckmark | Last updated: November 18, 2022

What Does MLOps (Machine Learning Operations) Mean?

Machine learning operations (MLOps) is an approach to managing the entire lifecycle of a machine learning model -- including its training, tuning, everyday use in a production environment and retirement.

MLOps helps organizations to effectively develop, deploy, and maintain machine learning models in a production environment, by improving collaboration, efficiency, model performance, governance and faster time to market.

MLOps was developed with the knowledge that not all data scientists and ML engineers have experience with programming languages and IT operations. The continuous feedback loops that MLOps provides allows employees outside data science to focus solely on what they know best instead of having to stop and learn new skills.

An important goal of MLOps is to help stakeholders use artificial intelligence (AI) tools to solve business problems while also ensuring an ML model's output meets best practices for responsible and trustworthy AI.

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Techopedia Explains MLOps (Machine Learning Operations)

MLOps, which is sometimes referred to as DevOps for ML, seeks to improve communication and collaboration between the data scientists who develop machine learning models and the operations teams who oversee an ML model's use in production. It achieves this by automating as many repetitive tasks as possible and improving feedback loops.

MLOps Implementation

A well-designed MLOps implementation can be used as a monitoring and automation system for ML models from the early stages of development to end-of-life. At its best, MLOps will support the needs of data scientists, software developers, compliance teams, data engineers, ML researchers and business leaders.

An MLOps rollout requires five important components to be successful:

1. Pipelines
ML pipelines automate the workflow it takes to produce a machine learning model. A well-designed pipeline supports two-way flows for data collection, data cleaning, data transformation, feature extraction and model validation.

2. Monitoring
Machine learning uses iterative mathematical functions instead of programmed instructions, so it's not unusual for an ML model’s performance to decline over time as new data is introduced. This phenomenon, which is known as model drift, requires continuous monitoring to ensure model outputs remain within acceptable limits.

3. Collaboration
Successful ML deployments require a variety of technical skills as well as a work environment that values inter-departmental collaboration. Feedback loops can help bridge the cultural and technical gaps between the data scientists who create machine learning models and the operations teams who manage them in production.

4. Versioning
In addition to versioning code releases, other elements that need to be tracked include training data and meta-information that describes specific ML models.

5. Validation
MLOps uses shift left testing to reduce bugs in development and shift right testing to reduce bugs in operations. Shift right is a synonym for "testing in production."

Unfortunately, MLOps has a high failure rate when it is not implemented properly. One of the most common challenges is cultural, created by competing priorities and siloed communication between business divisions. In response, new tools and services that facilitate feedback loops as well the technical aspects of a model's lifecycle are being adopted on a frequent basis.

MLOps Pipelines

MLOps teams are cross-functional, which means they have a mix of stakeholders from different departments within the organization. To ensure data scientists, engineers, analysts, operations and other stakeholders can develop and deploy ML models that continue to product optimal results, it’s important for the team to maintain good communication throughout the model’s lifecycle and follow best practices for each of the pipeline’s components. This includes:

Data Preparation

Data is the backbone of a ML model, and data quality is an important consideration. It is important to ensure the data used to train ML models follows best practices for data preprocessing. This includes best practices for data transformation, exploratory data analysis and data cleaning.

Feature Engineering

An important goal of feature engineering is to optimize the accuracy of a supervised learning outputs. Best practices include a process known as data verification. It's also important to make sure feature extraction scripts can be reused in production for retraining.

Data Labeling

Label quality is very important for supervised learning tasks. A best practice is to ensure the labelling process is well-defined and peer-reviewed.

Training and Tuning

It's useful to train and tune simple, interpretable ML models to start with because they are easier to debug. ML toolkits such as Google Cloud AutoML, MLflow, Scikit-Learn and Microsoft Azure ML Studio can make the debugging process easier for more complex models.

Review and Governance

Like DevOps, MLOps best practices include keeping track of versioning. This includes tracing the model’s lineage for changes throughout the model's lifecycle. Cloud platforms such as mlflow or Amazon SageMaker can be used to support this best practice.

Monitoring

After deploying the model, an important best practice is to monitor model outputs and summary statistics on a continual basis. This includes keeping an eye on:

  • The infrastructure on which the model is deployed to ensure it meets benchmarks in terms of load, usage, storage and health.
  • Statistical summaries that indicate the existence of bias introduced by input data that is either over-represented or under-represented.
  • The ML model itself. An automated alert system can be used to trigger a model’s re-training process when outputs for the model drift beyond acceptable statistical boundaries.

MLOps vs DevOps

MLOps and DevOps share many similarities in their development phases. They both support the continuous integration of source control, automated testing and a continuous delivery approach to code releases. An important difference, however, is that while DevOps embraces a shift left approach to conducting integration tests and unit tests during the development phase, MLOps uses both shift left and shift right testing to prevent model drift in production.

Role of the MLOps Engineer

An MLOps engineer's role is a combination of software engineering and machine learning, they work on integrating and streamlining the machine learning pipeline and infrastructure, and making sure that models are deployed, monitored and maintained in production. Essentially, an MLOps engineer is responsible for implementing and maintaining the processes and infrastructure needed to develop, deploy, and manage machine learning models in a production environment.

Responsibilities may include:

  1. Collaborating with data scientists to understand the requirements for machine learning models and to ensure that models are deployed in a way that is both accurate and efficient.

  2. Setting up and maintaining the infrastructure needed to train and deploy machine learning models, including cloud-based platforms, containerization technologies, and data storage solutions.

  3. Automating the model development process, including data pre-processing, model training, and model deployment.

  4. Monitoring the performance of machine learning models in production and making adjustments as needed to improve model accuracy and reliability.

  5. Ensuring that machine learning models are developed, deployed, and maintained in compliance with legal and regulatory requirements.

  6. Managing and optimizing the resources used by machine learning models, including compute resources, data storage, and network bandwidth.

  7. Continuously testing and evaluating the models to detect and fix errors and bugs in the models.

  8. Communicating with stakeholders to report the performance of the models and to make recommendations for how to improve model performance.

How to become an MLOps Engineer

Becoming an MLOps engineer requires a strong understanding of machine learning (ML) concepts and techniques, as well as experience working with ML frameworks. Additionally, it's important to have a solid understanding of software development best practices, including version control, testing and continuous integration/deployment.

The specific requirements for becoming an MLOps engineer can vary depending on the employer and the specific role -- but generally, the following skills and qualifications are considered important:

  1. Strong understanding of machine learning concepts and techniques: MLOps engineers should have a good grasp of ML models and algorithms, as well as the ability to implement them using popular ML frameworks such as TensorFlow, PyTorch and scikit-learn.

  2. Experience with software development: MLOps engineers should have experience with software development best practices such as version control, testing, and continuous integration/deployment.

  3. Familiarity with cloud platforms and containerization technologies: MLOps engineers should have experience working with cloud platforms such as AWS, Azure, and GCP, and containerization technologies such as Docker and Kubernetes.

  4. Strong communication and teamwork skills: MLOps is a collaborative field that requires clear and effective communication with both technical and non-technical stakeholders.

  5. Programming experience: Strong programming skills in languages like Python, Java, and C++ are highly desirable.

  6. Familiarity with data pipeline and data engineering: You should be familiar with data pipeline, data storage and data warehousing technologies like Apache Kafka, Apache Spark, and Hive.

  7. Keeping yourself updated: Stay current with the latest MLOps trends and best practices by following industry leaders and participating in the MLOps community.

Having a degree in a related field such as computer science, software engineering, or statistics is typically beneficial, but it is not always a requirement.

MLOps Certifications

While certifications are not always required for becoming an MLOps engineer, they can be useful for demonstrating your skills and knowledge to potential employers. Some popular certifications in the field include:

  1. AWS Certified Machine Learning - Specialty: This certification validates your ability to design, implement, deploy, and maintain machine learning solutions on the AWS platform.

  2. Google Cloud Professional Machine Learning Engineer: This certification demonstrates your ability to apply machine learning to real-world problems and use Google Cloud's machine learning platforms and tools to build, deploy, and productionize models.

  3. Microsoft Certified: Azure Machine Learning Engineer: This certification demonstrates your ability to design, build, test, and maintain machine learning models using Azure Machine Learning.

  4. Cloudera Certified Data Engineer: This certification validates your ability to design, build, and maintain reliable, scalable, and high-performance data processing systems.

  5. Data Science Council of America (DASCA) Senior Big Data Engineer: This certification validates your ability to design, build, and maintain big data infrastructure and applications using the latest technologies and best practices.

  6. Docker Certified Associate: This certification validates your knowledge of containerization and Docker, an important tool for MLOps engineers.

  7. Kubernetes Administrator (CKA) and Kubernetes Application Developer (CKAD) certifications: These certifications demonstrate your knowledge of Kubernetes, which is a popular open-source platform for container orchestration.

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