What is MLOps?

MLOps, or Machine Learning Operations, is a novel term referring to the field focused on linking the development and operation facets of Machine Learning. The objective of MLOps is to incorporate practical procedures that foster scalability, assurance, governance, and agility, following the already well-established method of DevOps. However, it's important to note that MLOps, while having similarities with DevOps, encompasses a few unique aspects.

Conceiving MLOps as an instrumental approach to supervise the Machine Learning Lifecycle can be insightful, as it deals with numerous challenges teams encounter. As quoted by Nvidia - "MLOps might resemble a name of a friendly one-eyed beast, but in essence, it's an abbreviation that signifies triumph in enterprise AI."

How to Initiate with MLOps?

Despite appearing challenging to incorporate, the issues with MLOps platforms often narrow down to the commitment of time and initial implementation costs. Although the beginning may seem financially intimidating, MLOps can prove to be cost-efficient and time-saving for organizations in the long run.

Aside from investments linked to MLOps, few important steps and best practices can help in mastering it.

  1. Converting Goals into Machine Learning Problems: The establishment of ML systems begins with a clear objective. Based on KPIs, performance metrics, these objectives can vary significantly.
  2. Developing ML & Data Solutions to these Problems: After converting the objectives into ML problems, the next step is to find suitable input data and models. Optimization comes into play once the model is put into the production environment.
  3. Data Preparation and Processing: This includes actions such as formatting, feature engineering, and cleaning data. A solid MLOps pipeline is required to generate clean data for the ensuing model development process.
  4. Data Science Application: After data preparation, the ML model training phase starts. Determining the most effective solution using precision, accuracy, and recall measures is crucial. Plus, it includes a qualitative analysis of the model.
  5. Constructing and Automating ML Pipelines, and Model Deployment: Deployment methods can be static or dynamic. Dynamic Deployment involves deploying the model through a web framework that responds to user requests via an API endpoint. In contrast, Static Deployment pertains to deploying the model through installable software.
  6. Optimization, Maintenance, and Monitoring of ML Model: This encompasses monitoring the performance of deployed models and ensuring effective governance.

Why MLOps is Vital?

MLOps bears critical importance because it addresses numerous bottlenecks and issues generally faced by teams. The key challenges that can be mitigated by MLOps are:

  1. Shortage of Data Scientists: It’s challenging to find proficient developers capable of deploying scalable web apps. This gap is filled by adopting MLOps and new ML Engineers.
  2. Change in Business Goals and Maintenance of ML Model Performance: Adapting to changing business objectives and constantly training the ML model can be challenging.
  3. Communication Issues: Breakdowns in communication between technical and business departments can lead to project failure. MLOps platforms assist in resolving this issue.
  4. Risk Assessment of ML Systems: A significant challenge in Machine Learning is evaluating the risk of any deployed model deviating from its assigned task.

MLOps Versus DevOps

Although MLOps and DevOps share common grounds in scalability and maintenance of APIs, infrastructure and services for deployed Machine Learning models, they are not identical. MLOps mainly differentiates due its continuous testing (CT) process, and the requirement for MLOps engineers to have more analytical and statistical skills compared to a DevOps engineer.

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