If you're ready to take your business to the next level, then you need to be thinking about MLOps. Of course, the concept is still fairly new. But, MLOps is the solution to integrating lifecycle management of machine learning.
MLOps isn't a product you buy. Instead, it's a method or way of working. And, one of the most significant benefits of MLOps is its focus on working collaboratively towards delivering value to customers.
Keep reading to learn all about how MLOps works and how Bitstrapped can help you.
Machine learning (ML) is a data analysis technique. It automates analytical model building. ML is a branch of artificial intelligence (AI).
The idea behind ML is that systems can learn from data and so they can identify patterns and make decisions almost all on their own.
The science of machine learning is not new. Yet, the ability to apply complex mathematical calculations to big data is a more recent development.
Self-driving cars, online recommendation offers, and fraud detection are all common examples of ML systems today.
Machine Learning Operations (MLOps) is a set of policies, practices, and governance that includes machine learning, DevOps, and data engineering elements. The theory applies DevOps practices to machine learning workloads.
MLOps was put in place to manage machine learning and artificial intelligence (AI) solutions throughout the lifecycle. It focuses on building a standard set of practices for managing analytics.
Machine learning models combine the development and operations maintenance of ML systems. This includes integration, testing, release, deployment, and infrastructure management.
It requires the integration of:
The outcome is an efficient deployment and maintenance of ML systems. As a result, you avoid common problems data scientists face.
There are several benefits of MLOps your operations will experience. For example, organizations that use MLOps see improved confidence in their model. This leads to increased trust and the ability to gain worthy insights.
MLOps users also see an improvement in reliability because of better quality and consistency.
One notable benefit is rapid innovation. MLOps is for data processing teams, analysis professionals, and IT engineers. It provides self-service environments with access to curated data sets.
As such, it increases the speed of development and deployment. Work moves faster. Data engineers and scientists waste less time with repeat, missing, or invalid data.
Normalized data allows you to use a centralized data environment. All the features are in one place. And the environment comes with data protection. It also follows data handling policies.
All the steps in the machine learning data catalog become automated. The process is repeatable. This includes how the model is trained, evaluated, versioned, and deployed.
Repeatability, thanks to automation, creates maximum efficiency. As a result, data scientists can spend more time on finding insights, researching, and innovating.
Scaling an AI lifecycle is difficult. The decision-making process needs validation tools and transparency. MLOps demonstrates how engineers built the model and where they deployed it.
Automatic reporting makes algorithms transparent. Following strict governance is traceable and leads to the best business practices. The documentation can support:
Moreover, reporting helps with understanding and managing business data. You can enforce policies that protect against bias. Thus, you get improved model quality. With better compliance, you build trust in AI and ML systems.
Developing an MLOps model to achieve your business objectives is the easy part. Then, deploying and operating the model gets more complicated. Models deteriorate over time. So, it would be best if you retrained them. Then, they stay helpful.
Often, real-world data in training pipelines doesn't have the outcome variable contained. So then, the model is fitted with the wrong training data. This leads to poor function.
Code quality checks keep essential functions doing what they are supposed to do. You want to eliminate bugs, and dead code smells before production.
Data can create many problems. ML pipelines need higher-level validation statistical properties of the input. Basic validations aren't enough. Detecting data errors provides better longevity for ML systems.
ML models are harder to test because absolute accurate results aren't possible. Model validation tests need to be statistical instead of having a binary pass/fail status. It would be best if you also tracked several metrics.
Experiment tracking helps with this. But, it would help if you still validated models before promoting them.
As mentioned, different team members come together in MLOps. Collaboration is the key to achieving your business goals.
The team usually includes a data scientist (ML engineer), data engineer, and DevOps engineer. Everyone must come together to succeed using MLOps.
MLOps is driving innovation and constantly evolving. So take the opportunity to experiment. Experiments can help improve the performance of your machine learning models.
There are even tools available to help you track your experiments. This will increase your productivity and make your work reproducible.
Monitoring is the most important in ML systems. Their performance depends on factors you cannot control, like data. Monitoring standard metrics include:
But, you must also monitor model prediction performance and service updates. Prediction performance will ensure you update the model when necessary. You should keep service updates as short as possible.
Service disruption can lead to business losses.
Though still a relatively new field, the benefits of MLOps are undeniable. To remain competitive in the market, you need to implement MLOps into your business practices. It's a leading technology that is developing at lightning speed.
To get started with MLOps, contact us at Bitstrapped. Our engineers can help you with building an MLOps solution for your company.
Here is a breakdown of the MLOps lifecycle and the stages that machine learning developers and data scientists use to deploy a production application.
Learn about the emergence of MLOps, the missing operating model for ML success.