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In Comet’s 2021 Machine Learning Practitioner survey, 47% of respondents reported needing 4-6 months to deploy a single ML project, and 68% admitted abandoning their experiments. Organizations are increasingly realizing the value of machine learning and looking for more ways to apply it to more innovative cases. However, ML teams today face people, processes, and tools challenges. These create friction between teams, making it difficult to deploy models, scale operations, and realize business value. MLOps can help organizations streamline processes and move models into production faster. Let’s dive into MLOps, its benefits, and its stages.
MLOps stands for machine learning operations. It’s a set of practices and processes to streamline ML model management, deployment, and monitoring. In machine learning, data scientists collaborate with other teams, including engineers, developers, business professionals, and operations, to push ML models into production. In addition, the ML lifecycle consists of various components such as data collection, data preparation, model training, model deployment, and model monitoring. Because of this, model development and deployment are often separate processes handled by different teams. This workflow creates a deployment gap, builds siloed tasks, introduces human errors, and causes lengthy cycles. With MLOps, each stage of the ML lifecycle is unified in a single workflow to facilitate collaboration and communication.
The principles of MLOps borrow the same principles of DevOps but differ in execution as Machine Learning inherently involves more experimentation and iteration and it is not very linear. DevOps is a set of practices and tools that integrate software development (Dev) and IT Operations (Ops). It aims to bridge the gap between the teams that write the code (Dev) and those that oversee the infrastructure and management of the tools used to run and manage the product (Ops). The wide adoption and rapid success of the DevOps concept prompted the adoption of similar principles (MLOps) to streamline & improve the processes in machine learning.
Implementing MLOps helps organizations enhance productivity and collaboration while ensuring ML teams build reliable and explainable models. MLOps can make a difference in various sectors and use cases.
There’s been a considerable increase in ML investment across various industries. And along with it, many organizations have increased the number of ML projects and the complexity of these projects. As these organizations scale up, they need a structured framework for training multiple models simultaneously while incorporating business and regulatory requirements and ensuring AI governance. MLOps provides a framework for managing the ML lifecycle efficiently, creating repeatable processes, and staying in compliance with regulations. By establishing MLOps, organizations can quickly scale.
The increased demand for machine learning requires rapidly iterating ML processes like experimentation, training runs, and deployment. Borrowing the concept of DevOps, MLOps aims to meet this demand by implementing a set of practices that aim to streamline, automate, and integrate the development and production phases of the ML cycle seamlessly. Establishing a robust MLOps process helps teams speed up the model development process, enabling faster deployment of models into production.
MLOps helps establish rules and practices that foster collaboration. It keeps everyone informed of each other’s progress and improves the model hand-off process between development and deployment. Every ML project involves a development and deployment team and internal stakeholders like project managers, business owners, legal teams, and key decision-makers. To create the best ML product for the problem, ML teams must work with internal stakeholders to align business goals and strategies. MLOps ensures alignment and promotes frequent communication across these teams to achieve business goals and hit KPIs.
Due to increased collaboration, different teams can leverage the expertise of each other to build, test, monitor, and deploy machine learning models more efficiently. MLOps helps teams save time and resources and increase productivity with a streamlined and automated workflow.
Ultimately, the critical output of establishing an MLOps culture is to build a high-quality model that users can trust. With MLOps, teams can create better models because of continuous & focused feedback. This constant and cyclical testing and validation reduce model bias and improve explainability.
Explainability plays a crucial role in machine learning and AI. It aims to answer a user’s or a stakeholder’s question about how an ML model arrived at its decision. A lack of explainability poses risks across industries. In healthcare, where an ML model suggests patient care, healthcare providers must trust the model’s reasoning since the stakes are exceptionally high.
We must build responsible, trustworthy, reliable, robust, accountable, and transparent models to achieve explainable AI. Establishing MLOps helps accomplish this through well-defined frameworks, processes, and practices across the ML lifecycle. MLOps helps us understand the model’s outcome and behavior and, in turn, enables us to explain it to others and build trust in the model. Continuous model training and monitoring help ensure that a model performs as intended.
AI governance refers to implementing a legal framework that ensures ML models and their applications are explainable, transparent, and ethical. In AI governance, organizations must define policies and establish accountability in creating and deploying these models. MLOps ensures that these policies are in place through well-documented activities on an ML project, keeping prior versions of models, testing models for biases, monitoring models in production to prevent concept drift, and more. Implementing MLOps protects your organization from legal, ethical, and regulatory risks which can harm your organization’s reputation and financial performance.
AI observability is a method that provides deep insights into an ML model’s data, behavior, and performance across the model lifecycle. It goes beyond model monitoring since monitoring tells us “what” issues are happening, while observability explains “why” they occur. We must implement the MLOps principle of automation, continuous training and monitoring, and versioning to fully embrace AI observability.
MLOps consists of four main stages, each with substages. Optimizing work in each step can improve ML projects and produce better results.
The first stage in the MLOps lifecycle is collecting data and preparing it for model development. In machine learning, a model is only as good as its data. However, data can come in different formats and from different sources. It’s crucial to establish a set of rules to define and label your data. Data scientists must agree on a standard procedure for labeling to ensure uniformity. Cleaning, versioning, and attaching attributes to your data is an important step to determine any underlying patterns that will help you build your model.
Once your data is ready, it’s time to build your model. Model development comprises different substeps such as feature selection, algorithm selection, hyperparameter tuning, model evaluation, and more. Also called the experimentation stage, this stage involves trying different combinations of features, hyperparameters, and algorithms until we get the right combination that provides a model that fits our business goals. In this step, tracking any changes in the model is essential for easy reproducibility.
After building the most suitable model for your business needs, it’s time to release it to the real world or integrate it into an application. But before deploying your model, it needs to be validated and tested for performance, efficiency, bugs, and other issues to determine if the trained model is fit to be released. This stage requires collaboration and coordination between data scientists, ML engineers, IT teams, developers, and business teams to ensure the model works reliably in the production environment.
Once deployed into production, monitoring the model’s performance is necessary to ensure it is still performing as expected. A model’s performance may deteriorate over time when the real world presents new and unknown data (data drift) or when there are changes in the environment and the model’s learning pattern no longer holds (concept drift). These issues can negatively affect consumers and businesses if not detected on time.
ML practitioners can define metrics and alerts once a model’s performance exceeds the established threshold. Once that happens, ML practitioners can retrain models to cater to the new requirements and deploy them again.
MLOps streamlines the ML workflow, allows teams to build better models, and helps organizations realize business value faster. With so many moving parts, you’ll need a platform that supports every stage of the machine learning pipeline. Comet’s platform helps ML teams manage, visualize, and optimize models—from training runs to production monitoring. Try Comet for free today.