The exciting wave of developments in AI and machine learning applications might lead you to believe that organizations are also advancing rapidly in their ability to deliver ML products. The reality is, ML internal processes are having trouble catching up with the overall evolution of the industry… but there’s hope in the form of MLOps!
MLOps, which stands for machine learning operations, is built on a set of processes and best practices for delivering ML products with both agility and real-time collaboration between data scientists and operations. Its goal is to automate parts of the ML building process as much as possible to enable continuous delivery. With this new wave of operationalization, all of the engineering pieces come together to create AI at scale.
An important note here: you may have heard the term AIOps and assumed these two can be used interchangeably. That isn’t the case: AIOps is a narrower domain that applies machine learning to the automation of IT operations. You can find more about AIOps in our summary article.
Why the Push for MLOps?
Developing machine learning products remains incredibly challenging due to the siloed, slow nature of internal ML processes. Here’s a brief rundown of the internal problems that hold organizations back from building ML:
- There’s very little automation of internal processes.
- Data scientists and operations teams operate in silos despite the need for collaboration.
- Very few clear pipelines exist.
- Retraining models post-production isn’t happening to the extent models require, leading to poor performance over time.
- Insufficient oversight on regulatory and compliance issues.
These factors can lead to the lack of reproducibility, scalability, and agility needed to facilitate effective AI development. But this is where MLOps can help. With the right infrastructure and processes in place, MLOps can overcome these challenges and produce a number of benefits, such as:
- Combines expertise for efficiency: MLOps prompts communication between teams that are traditionally isolated from each other. It combines the business sense of your operations team with the ML-specific knowledge of your data scientists, looping them together for collaborative endeavors. At the same time, each team can focus on what they do best.
- Defines ownership of regulatory processes: Your operations team can oversee regulatory and compliance issues, keeping abreast of any changes in these areas and ensuring the data science team is immediately aware.
- Reduces waste: With the current way ML development is done, there’s a ton of waste in the form of time, money, and opportunity cost. Data scientists spend much of their time focused on repetitive tasks they weren’t hired for, for instance. MLOps leverages the skillset of each team so they’re working on what they do best, it automates pipelines to enable speedy delivery and reproducibility.
- Enables rapid iteration: Through continuous integration, delivery, and pipeline automation, MLOps enables teams to iterate quickly. This means shorter time-to-market for successful deployments as well as more deployments overall.
- Produces more enriching products: By leveraging best practices across the ML lifecycle, MLOps ensures your team is using advanced tools and infrastructure to support deployments. With the additional ability to rapidly integrate, teams have time to experiment more to achieve greater accuracy in their products. As an end result, the user experiences a more enriching, high-quality product.
If you employ MLOps in your organization, you’ll be able to deliver more innovative AI solutions at scale. Not only that, you’ll be able to do it again, and again.
How to Implement MLOps in Your Organization
At a high level, it’s evident how MLOps can create powerful, positive changes in ML development. But how do you practically implement MLOps in your own organization? Let’s simplify by breaking this down by the various parts of the ML lifecycle:
The data portion of a project involves several key pieces:
- Data collection: Whether you source your data in-house, open-source, or from a third-party data provider, it’s important to set up a process where you can continuously collect data, as needed. You’ll not only need a lot of data at the start of the ML development lifecycle, but also for retraining purposes at the end. Having a consistent, reliable source for new data is paramount to success.
- Data cleansing: This involves removing any unwanted or irrelevant data, or cleaning up messy data. In some cases, it may be as simple as converting data into the format you need, such as a CSV file. Some steps of this may be automatable.
- Data annotation: Perhaps the most time-consuming and challenging, but critical, stages of the ML lifecycle is the process of labeling your data. Companies that attempt to take this step in-house are often faced with limited resources and spend far too much time doing so. Other options include hiring contractors to do the work or crowdsourcing, broadening the options to a more diverse set of annotators. Many companies choose to work with external data providers, who can give access to large crowds of annotators, platforms, and tooling for whatever your annotation needs are. Parts of the annotation process can also be automated, depending on your use case and quality needs.
Setting up a continuous data pipeline is an important step in MLOps implementation. It’s helpful to think of it as a loop, because you’ll often realize you need additional data later in the build process, and you don’t want to have to start from scratch to find it and prepare it.
In the model build stage, you’ll complete the following tasks:
- Model training: Use your labeled data to create a training set and a test set. The training set is used at this step to teach the model what features it needs to learn to recognize. There are many methods of model training in machine learning (from fully supervised, to semi-supervised, to unsupervised, and everything in between). The method you choose will depend on your use case, resources available, and what metrics are important to you. Certain methods can include automation.
- Model testing and validation: The model’s performance should be evaluated against the test set to see if it achieves the desired KPIs. Before deployment, the overall system must be validated to ensure it’s working properly and as intended.
- Model deployment: The model is deployed into production; the system is online.
Keep in mind that while building the model is in the purview of the data scientists, the operations team should be kept in the loop on each stage of development. Developing a repository for your models that includes their entire history can help with needed transparency here.
After you deploy your model, you’ll need a continuous testing process set in place. This includes:
- Monitoring: Continuously monitor the model against your KPIs. Have alerts and plans in place if the model fails to meet any KPIs.
- Retraining: A critical but often missed step of ML development is retraining. Models must be consistently retrained on new data as their external environment changes.
Determine who will take ownership over post-production monitoring and retraining. This is where it’s essential to leverage an automated data pipeline to handle required retraining.
While that was a brief outline of the ML lifecycle, the point is that there are many opportunities to automate the process and employ feedback loops and pipelines to improve speed and reproducibility. The goal of MLOps should really be to avoid redundancy, maximize collaboration, and ultimately scale and deliver innovative AI.
What We Can Do For You
At Appen, we provide high-quality annotated training data to power the world’s most innovative machine learning and business solutions. We can help your organization with data collection, data annotation, as well as model retraining and improvement in the post-production phase. Machine learning assistance is built-in to our industry leading annotation tools to save you time, effort, and money—accelerating the ROI on your AI initiatives.
We understand the complex needs of today’s organizations. For over 25 years, Appen has delivered the highest quality linguistic data and services, in over 235 languages and dialects, to government agencies and the world’s largest corporations.