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What is MLOps & why is it important for today’s companies now?

Do you know any data scientists or machine learning (ML) engineers who wouldn’t want to increase the pace of model development and production? Are you aware of teams who are collaborating with pure ease when enlisting continuous integration and deployment practices on ML/AI models? We don’t think so.

What-is-MLOps-and-why-it-is-important

MLOps, which stands for Machine Learning Operations, is being used to help streamline the workflow of taking machine learning models to production, as well as maintaining and monitoring them. MLOps is all about facilitating collaboration among data scientists, DevOps engineers, and IT professionals.

MLOps helps to speed up the pace of innovation for organizations.  MLOps allows teams to launch new projects more easily, assign data scientists to different projects more smoothly, helps with experiment tracking, infrastructure management; and simply implement best practices for machine learning.

MLOps is especially important for companies as they transition from running individual artificial intelligence and machine learning projects to using AI and ML to disrupt their businesses at scale. MLOps principles are based on considering the specific aspects of AI and machine learning projects to assist professionals in speeding up delivery times, reducing potential defects, as well as making for more productive data science.

What is MLOps made up of?

While MLOps may vary in its focus based on different machine learning projects, the majority of companies are using these MLOps principles.

  • Exploratory data analysis (EDA)
  • Data Prep and Feature
  • Engineering
  • Model training and tuning
  • Model review and governance
  • Model inference and serving
  • Model monitoring
  • Automated model retraining

What’s the difference between MLOps and DevOps?

You’re likely to be familiar with DevOps, but maybe not MLOps. MLOps basically consists of a set of engineering practices specific to machine learning projects, but that do borrow from DevOps principles in software engineering. DevOps brings about a quick, continuous, and iterative approach to shipping applications. MLOps then takes the same principles to bring machine learning models to production. The idea for both is to bring about higher software quality, quicker patching and releases, and of course, greater customer experiences.

Why is MLOps necessary and vital?

It should come as no surprise that productionizing machine learning models are easier said than done. The machine learning lifecycle is made up of many components, including data ingestion, preparation, model training, tuning and deployment, model monitoring, and more. It can be difficult to maintain all of these processes synchronously and ensure they’re aligned. MLOps essentially makes up the experimentation, iteration, and improvement phases of the machine learning lifecycle.

Explaining the benefits of MLOps

If efficiency, scalability, and the ability to reduce risk sounds appealing, MLOps is for you. MLOps can help data teams with quicker model development. It can help them provide higher quality ML models, as well as deploy and produce much faster.

MLOps provides the opportunity to scale. It makes it easier to oversee tons of models which need to be controlled, managed and monitored for continuous integration, delivery, and deployment. MLOps offers more collaboration across data teams, as well as removes conflict which often arises between DevOps and IT. It can also speed up releases.

Finally, when dealing with machine learning models, professionals also need to be wary of regulatory scrutiny. MLOps offers more transparency and quicker response times for regulatory asks. It can pay off when a company must make compliance a high priority.

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Examples of MLOps offerings

Companies looking to deliver high-performance production ML models at scale are turning to offerings and partners to assist them. For example, Amazon SageMaker is one which helps with automated MLOps and ML/AI optimization. It’s assisting companies as they explore their ML infrastructure, ML model training, ML profiling, and much more. For example, ML model building is an iterative process that is well supported by Amazon SageMaker Experiments. It allows teams and data scientists to track the inputs and outputs of these training iterations or model profiling to improve the repeatability of trials and collaboration. Others are also turning to ML Flow to assist them as it provides an open source platform for the ML lifecycle. Hystax provides a trusted MLOps open source platform as well.

Regardless of the platform or cloud you’re using, professionals can enlist MLOps on AWS, MLOps on Azure, MLOps on GCP, or MLOps on Alibaba cloud; it’s all possible. When companies do manage ML/AI processes and enlist strategies for their governance, they will surely see the results. Professionals should consider MLOps for infrastructure management, take on MLOps for data management, get buy-in for MLOps for model management and the list goes on.

Machine Learning offers some exciting MLOps capabilities, including model optimization and model governance. It can help by creating reproducible machine learning pipelines to help outline repeatable and reusable methods for data preparation, training, and scoring. It can also craft reusable software environments for training and deploying models.

Professionals can also now register, package, and deploy models from anywhere. They can have access to governance data for the full ML lifecycle. They can also keep track of information on who is publishing the models and why changes are being made.

Similarly to DevOps, MLOps can be used to notify professionals and alert them on occurrences in the machine learning lifecycle. Whether it’s experiment completion, model registration, or data drift detection, these alerts can be set up. Finally, in addition to providing monitoring and alerts on machine learning infrastructure, MLOps allows for automation. Professionals can benefit greatly from automating the end-to-end machine learning lifecycle. They can quickly update models, as well as test out new models.

How great is it that your teams can continuously release new machine learning models along with your other applications and services?

If you have questions on anything MLOps or are in need of ML infrastructure management information, feel free to reach out to Hystax. With Hystax, users can run ML/AI on any type of workload with optimal performance and infrastructure cost. Our MLOps offerings will help you reach the best ML/AI algorithm, model architecture, and parameters as well. Contact us today to learn more, as well as receive some ML/AI performance improvement tips and cost-saving recommendations.

Hystax OptScale offers the first-ever open source FinOps & multi-cloud cost management solution that is fully available under Apache 2.0 on GitHub → https://github.com/hystax/optscale

👆🏻 Storing objects in AWS S3 public buckets could threaten the security of your company data.

💡 Discover the recommendations, which help you manage public access to AWS S3 resources properly and ensure that all required buckets and objects have their public access blocked  → https://hystax.com/the-quickest-way-to-get-a-list-of-public-buckets-in-aws-to-enhance-your-security

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FinOps and MLOps

A full description of OptScale as a FinOps and MLOps open source platform to optimize cloud workload performance and infrastructure cost. Cloud cost optimization, VM rightsizing, PaaS instrumentation, S3 duplicate finder, RI/SP usage, anomaly detection, + AI developer tools for optimal cloud utilization.

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FinOps and cloud cost optimization for ML/AI workloads

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