What is MLOps?
The enthralling pace of development in AI and machine learning technologies may cause us to think that companies are growing rapidly in their capacity to offer ML products. However, ML internal processes aren't catching up to the rapid advancements in the field... however, there is some hope with MLOps! MLOps is a term used to describe machine learning operations. It was based on a set process and best practices for delivering ML products that have both speeds and real-time interaction between operations and data scientists. It aims to automatize parts of the ML creation process to the greatest extent possible to allow continuous delivery. With this latest technology, the engineering components' operationalisation is put together to build AI on a large scale. A key point to note: Many of us might have heard of AIOps, and thought they are interchangeable. It's not the case. AIOps is a narrower domain that applies machine learning to automated IT operations.
Why the Push for MLOps?
Making machine learning-related products remains difficult because of the internal siloed and slow ML processes. Here's a brief overview of the internal issues that prevent organizations from establishing ML:
- There isn't much automatization of the internal procedures.
- Operations teams and data scientists work in silos despite the need to collaborate.
- There are very few clear pipelines.
- The process of retraining models after production isn't taking place in the way that models need and can result in low performance over time.
- Insufficient oversight of compliance and regulatory concerns.
These issues can result in the absence of reproducibility, flexibility, and scalability required for efficient AI development. However, this is the area where MLOps can come in. With the appropriate process and infrastructure, MLOps can overcome these obstacles and provide a variety of benefits. For instance:
- Combining expertise to improve efficiency: MLOps allows for collaboration between teams normally separated from one another. It brings together the business savvy of our operations staff with the specific knowledge of our ML data scientists, bringing them to collaborate on projects. In the meantime, the teams can concentrate on the things they excel at.
- Defines the ownership of the regulatory process: Our operations team can supervise compliance and regulatory issues, staying abreast of any developments in these areas and ensuring that our data science team is always aware.
- Reduces waste: In the present methods: ML development is carried out. It's not easy to eliminate wasted time, money, and cost. For instance, data scientists tend to spend a lot of their time working on routine tasks they weren't hired to do. MLOps makes use of the expertise of every team, ensuring they can focus on what they excel at and automates pipelines to speed up delivery and repeatability.
- Facilitates rapid iteration through continuous integration and delivery as well as an automated pipeline. MLOps enables teams to iterate rapidly. This means a shorter time to market for successful deployments and more deployments in general.
- Produces more enriching products. Utilizing best practices throughout the lifecycle of ML, MLOps ensures that our team is using cutting-edge tools and infrastructure for deployments. With the ability to integrate quickly, teams can play around with the idea of achieving greater quality with their product. The result is that the end user will experience a more satisfying, top-quality product.
If we use MLOps within our company, it will allow us to offer more innovative AI solutions in a large size. In addition, we'll also be able to repeat the process repeatedly.
How to Implement MLOps in Our Organization
In the grand scheme, it's clear that MLOps can bring about significant, positive changes within ML development. However, how do we implement MLOps within our company? Let's break this into various components of the ML lifecycle.
The data component of a project consists of various key components:
- Collection of Data: No matter if it's our data internally, through open-source or through a third-party data supplier, it's essential to create a procedure that we are able to collect data, as required continuously. There's no reason only to require lots of data at the beginning phase of the ML development process and for retraining purposes at the development's close. Being able to have a reliable, consistent source for data that is up-to-date is essential to achieving success.
- Data cleansing: eliminates any irrelevant or unneeded data or cleans up messy data. Sometimes, it could be as easy as converting data to the format we require, for example, the CSV file. Certain steps may be automated.
- Data annotation: One of the most difficult and time-consuming yet crucial phases of the ML lifecycle involve labelling our data. Resources typically constrain companies who attempt to complete this step internally and spend too long doing it. Another option is to hire contractors to complete the task or crowdsourcing, extending the possibilities to a broader set of annotations. A lot of companies prefer working with data providers outside of their own, which can provide access to huge numbers of annotation platforms, annotators and tools for whatever our requirements for annotation are. Certain aspects of the annotation process could also be automated according to our usage and requirements for quality.
Establishing an ongoing data pipeline is a crucial stage in MLOps implementation. It is helpful to imagine the process as a loop because we'll likely discover the need for additional data during the building process, which is why we don't want to begin from scratch to search for it and create it.
In the build stage of our model, we'll have to be required to complete each of the tasks listed below:
- Model training: Use the labelled data to build an exercise set and the test set. Training sets are used during this stage to instruct the model on the attributes it should be able to recognize. There are various ways of training models for machine learning (from fully supervised to semi-supervised, unsupervised and all between). The choice of the method we pick is based on our specific use situation, available resources and the metrics that are crucial to we. Certain methods could be automated.
- Test and validation of the model: Model testing and validation: The model's performance must be assessed against an assessment set in order to determine whether it meets the intended KPIs. Before deploying, the entire system should be tested to confirm that it's operating correctly and in the way, it was intended.
- Model deployment: The model has been put into production, and the system is up and running.
Be aware that, while creating the model is the domain of Data Scientists, it is the operational team must be kept informed at every stage of the development. Creating an archive for our models that includes all their backgrounds can provide transparency in this area.
We'll require a continuous testing system when we've set up the design. This includes:
- Monitoring: Continuously checking models against KPIs. Set up alerts and plans for when the model does not meet any KPIs.
- Retraining: A crucial yet often overlooked step in ML development is to retrain. Models need to be continuously trained with new data when their environment alters.
Decide who will be responsible for post-production monitoring and retraining. This is the time to implement the power of an automatic data pipeline in order to take care of the necessary retraining. Although that's a short description of the ML lifecycle, the main point is there's a myriad of possibilities to automatize the process and utilize feedback loops, pipelines, and feedback in order to increase speed and reliability. The aim of MLOps should be to reduce redundant work, increase collaboration, and scale up and deliver new and innovative AI.