Machine Learning (ML) is a field of study that uses algorithms to learn from data. It is a form of artificial intelligence (AI). MLOps is a term used in the ML community to describe a collaborative effort between data scientists and machine learning engineers. It reflects the need to build, train and run machine learning models in an automated fashion at scale.
The goal of MLOps is to make sure that data is clean and ready to be used by machine learning algorithms. This process requires collaboration between data scientists and engineers. Data scientists create the models and engineers build the systems that can run them. Before performing any machine learning experiments, it is important to verify that the data has been properly cleaned. As a part of a best practices approach to running machine learning experiments, teams should establish guidelines for how data is cleaned and prepared for modeling. These guidelines should be documented and shared with the rest of the team so that everyone is on the same page about what types of cleaning should be performed. Teams should also establish a process for identifying and fixing data quality issues as they arise. Monitoring data quality is an important component of this process and should be performed on an ongoing basis.
How does MLOps differ from DevOps?
The relationship between data science teams and IT operations teams is becoming increasingly important as more companies adopt machine learning technologies. In many cases, the teams are working hand-in-hand to build and deploy machine learning solutions that enable organizations to identify new business opportunities and achieve new levels of operational excellence. However, the two teams have historically operated in different silos. As a result, it has been difficult for these teams to work together effectively. The advent of MLOps is intended to change that by creating an open collaboration model that allows both teams to work more effectively together.
How is MLOps applied in practice?
Once an organization has established a set of best practices for using ML, it needs to put them into practice by running data-driven experiments at scale. In many cases, this will require the organization to work with large datasets that can be stored and analyzed in the cloud. In order for the organization to achieve this, it will need to address several key challenges related to the management and delivery of big data workloads.
Here are a few of the key challenges that MLOps addresses:
· Large scale data management: In most cases, organizations will need to take one or more of their mission critical applications to the cloud in order to run large-scale data processing experiments. This process can be complex and time consuming, and it can present a significant security risk if it is not properly managed.
· Secure data flow: Data security is of critical importance in the enterprise cloud environment. Because of this, it is critical that organizations be able to provide secure access to data sources and tools without compromising the flow of data across the network.
· Automatic monitoring: Running large-scale data processing experiments is a complex and time-consuming process. Organizations need a way to monitor the progress of these experiments in real time so that they can quickly identify and address any problems that arise.
What are some of the core MLOps tools?
Some of the core MLOps tools are:
· Monitoring tools: These include log aggregation and analysis tools that can make it easier for administrators to track user activity and detect any potential problems.
· Configuration management tools: These tools make it easy for administrators to ensure that their systems are running the latest version of the software and are running efficiently.
· Automated testing tools: These tools can be used to test software applications for security vulnerabilities before they can be deployed to users.
· Debugging tools: These tools can be used to track down bugs and ensure that software is working as intended.
· Infrastructure orchestration tools: These tools help organizations to manage and monitor their physical infrastructure resources such as servers, storage and networking equipment.
How can you build a successful MLOps team?
Building a successful MLOps team will require several steps. First, you will need to assemble a core team of individuals who are already proficient in cloud computing. Second, you will need to choose specific tools to automate the monitoring and debugging processes. Finally, you will need to develop an automation framework that can be used to scale the team and manage larger projects in the future.
To discover more about MLOPs, and the challenges faced when implementing it make sure to join our network and tune in to our event next week: AI OPs: How to Build Autonomous AI Operations.