What is MLOps?
MLOps is the abbreviation for Machine Learning Operations. MLOps is a substantial function of Machine Learning engineering, concentrated solely on streamlining the process of bringing machine learning models to production, then maintaining and finally, monitoring them. MLOps is also a large collaborative function, often composed of data scientists, DevOps engineers, and IT. MLOps could also be defined as a set of courses carried out for a combined effort of collaboration and communication between data scientists and operations professionals. Applying these approaches increases the quality, facilitates the management process, and automates the implementation of Machine Learning and Deep Learning models in production domains. It’s more straightforward to appropriate the models with business needs, as well as regulatory requirements.
MLOps is gradually growing into an autonomous strategy for ML lifecycle management. It involves the entire lifecycle – data collecting, model creation (software development lifecycle, continuous integration/continuous delivery), orchestration, deployment, health, diagnostics, governance, and business metrics.
The key phases of MLOps are:
- Data collection
- Data analysis
- Data transformation/preparation
- Model training & development
- Model validation
- Model serving
- Model monitoring
- Model re-training.
Why do we need MLOps?
MLOps is essential in machine learning environments. Machine learning helps individuals and businesses apply solutions that open previously unused sources of revenue, save time, and reduce cost by making more efficient workflows, leveraging data analytics for enhanced decision-making, and improving customer experience.
These objectives are difficult to achieve without a solid framework to pursue. Automating model development and deployment with MLOps means more rapid go-to-market times and lower operational costs. It enables managers and developers to be more nimble and strategic in their findings.
MLOps acts as the map to guide individuals, small teams, and even businesses to reach their goals no matter their limitations, be it sensitive data, fewer resources, small budgets, and so on.
You choose how big you want your map to be because MLOps are procedures that are not written in stone. You can experiment with diverse configurations and only use what performs better for you.
Following are the necessities that teams have come up with:
- There is a shortage of Data Scientists who are equipped to develop and deploy scalable web applications. There is a new grade of ML Engineers these days that aims to fill this vacancy. It is a specific need of Data Science and DevOps.
- Reflecting on transforming business goals in the model —There are various new essentials with the data continuously changing, maintaining performance standards of the model, and providing AI governance. It’s challenging to keep up with the continuous model training and evolving business objectives.
- Communication issues between technical and business teams with a difficult-to-find common language to collaborate. Most often, this problem becomes the reason for the failure of major projects.
- Risk assessment — there is a lot of controversy around the black-box disposition of such ML/DL systems. Often models learn to drift away from what they were initially trained to perform. Considering the risk/cost of such problems is a very important and meticulous step.
- For example, the value lost from an inaccurate video recommendation on YouTube would be much lower compared to marking an innocent person for fraud and blocking their account and cancelling their loan applications.
What are the best practices for MLOps
The best approaches for MLOps can be described by the stage at which MLOps principles are being applied.
- Exploratory data analysis (EDA) – Intelligently explore, share, and prep data for the machine learning lifecycle by developing reproducible, editable, and shareable datasets, tables, and visualizations.
- Data Prep and Feature Engineering– Creatively transform, aggregate, and de-duplicate data to create refined qualities. Most importantly, make the attributes visible and shareable across data teams, leveraging feature storage.
- Model training and tuning – Use prevalent open-source libraries such as sci-kit-learn and hyperopic to evaluate and improve model performance. As a more straightforward approach, use automated machine learning programs such as AutoML to autonomously perform base runs and create reviewable and deployable code.
- Model review and governance– Track model value, and model updates, and manage model details and transitions through their lifecycle. Discover, share, and collaborate across ML models with the usefulness of an open-source MLOps forum such as MLflow.
- Model inference and serving – Control the period of model reconfiguration, inference submission times and similar production-specifics in testing and QA. Use CI/CD tools such as notifications and orchestrators to automate the pre-production process.
- Model deployment and monitoring – Automate permissions and cluster deployment to production registered models.
- Automated model retraining – Create notifications and automation to make useful updates. In case of model drift due to discrepancies in training and inference data.
What are the skills needed for MLOps Engineer?
MLOps engineers need to have a basic understanding of machine learning and software development. Here are some of the specialised skills required to become an MLOps engineer:
- Ability to create and implement cloud solutions (AWS, Azure, or GCP)
- Working Knowledge with Docker and Kubernetes
- Ability to design MLOps pipelines
- Good knowledge of Linux
- Knowledge of architecture such as Keras, PyTorch, Tensorflow
- Background in software development
- Expertise to understand programs used by data scientists
- Experience in using widespread MLOps frameworks like Kubeflow, MLFlow, and DataRobot
Here are some non-technical skills needed to know to become an MLOps engineer:
- Strong communication skills-you need to be able to intercommunicate with the data science team to breakdown the frameworks and types of models built
- Teamwork-As an MLOps engineer, your need to be able to work with individuals with diverse skillsets and work with their strengths to design a scalable application.