MLOps was predicated on DevOps, a software development process streamlining enterprise application development, deployment, and operation. These tools facilitate the management of model metadata and aid in monitoring experiments.
Data science and IT engineering teams use MLOps to deploy machine learning models faster in real-world projects, enabling more project pipeline processes.
Employing MLOps
Statistical dataset analysis is performed as an initial step in a machine learning endeavour. The selection of appropriate algorithms is the result of a meticulous analysis. With enormous datasets, however, these duties must be automated. Over time, modifications to the input dataset are probable, necessitating an equivalent reflection in the output.
Furthermore, working with enormous datasets on hardware systems is not feasible; thus, the transition to cloud computing is inevitable. Such instances necessitate the deployment of the entire pipeline on the cloud. The concerns above are all addressed by the procedures governed by MLOps. As expected from the preceding paragraph, MLOps demonstrates notable utility when applied to massive datasets. It works well for enterprise-level processes and real-time business projections. Another rationale for selecting MLOps is that it provides greater organization to the ongoing project, facilitating efficient cooperation between data scientists and IT engineers. Thus, it is imminent that MLOps will become the industry standard.
Certain MLops tools are tailored for specific tasks, whereas others offer comprehensive solutions for overseeing the complete machine learning development process. Now, let’s examine a few of these intriguing tools:
Kuberflow
Data scientists typically utilize Kuberflow to roll out machine learning workflows. Many businesses, from CERN to Uber to Lyft, GoJek to Spotify, to Bloomberg to PayPal, employ Kubeflow. Kubeflow is a machine learning framework built on Kubernetes that turns data science workflow steps into Kubernetes jobs.
One more open-source MLOps tool for managing ML models is MLflow. It comprises four primary parts: a tracking section, a project section, a model section, and a model registry section.
Developing machine learning projects is a breeze using Metaflow. Thanks to this library, they can take on initiatives that data scientists work on. The experiments can be deployed with a single click and minimal coding changes.
DVC is analogous to a repository’s git commit in a machine learning project. It can efficiently process massive volumes of data and neatly store multiple revisions of the same information. It also ensures that all data science team members have quick and easy access to the data. DVC also helps with version control for machine learning projects by keeping data, models, and intermediate files in one place.
Sigopt
SigOpt is a model development and improvement tool that facilitates tracking of runs, visualization of training, and scalability of hyperparameter optimization for models developed with any library on any infrastructure. It provides an intuitive dashboard where users can compare and contrast alternative model variants based on statistical metrics like F1 score, accuracy, and more, all applied to a specific dataset.
ZenML
For MLOPS, ZenML is a free and open-source alternative. It provides an easy method for managing machine learning pipelines. Machine learning pipelines may be made that are easily transportable with the help of this MLOps tool.
MLReef
MLReef is yet another open-source platform for managing ML model development. In the field of machine learning, it provides one of the safest settings for developing models.
