How to Get Off to a Great Start with MLOps

Machine learning (ML) is more accessible today than ever to businesses across all industries. Data engineering teams can now build, train, and deploy ML models quickly, even without deep AI/ML experience, all through cost-efficient cloud ecosystems. The challenge, of course, lies in using ML effectively and consistently.

Modern organizations have to be agile and flexible to keep up in our ever-changing world. Trends are constantly shifting, and disruption looms around every corner. Given this reality, ML models have to evolve in parallel, especially for businesses that aim to deploy cloud-native applications that depend on ML insights.

However, even with this understanding, many enterprises struggle to deploy ML models to production, and most ML projects ultimately fail. Why? Complicated model migrations, long deployment cycles, and other issues get in the way of companies maintaining top ML performance.

This is why MLOps is essential. MLOps (machine learning + operations) is how enterprises use ML to its fullest potential. It encompasses the technologies and development practices needed to create value over the long term and free up engineering capacity. Without question, MLOps is paramount for enterprises that plan to utilize AI/ML now and in the future.

The Basic Elements of MLOps

What is MLOps? MLOps is the fusion of machine learning, DevOps, and data engineering. Thus, the stakeholders responsible for MLOps success (or lack thereof) are software engineers, data scientists, and data engineers. These roles work together to optimize data management and ML app development through MLOps.

When effective, MLOps eliminates pain points related to ML model lifecycle management, versioning and iteration, monitoring and management, security, governance, and discovery. It brings together IT professionals and data specialists so they can collaborate and extend the power of AI to end-users through mainstream applications.

The reasons why MLOps is so powerful are also why it’s so difficult to get right. MLOps teams have to ensure data is reliable and processed correctly. They have to maintain logs of previous models, keep close tabs on model performance, and retrain when necessary. They also have to make sure MLOps workflows and data management infrastructure can scale to handle massive volumes of raw information.

All the while, MLOps teams are responsible for managing complex cloud architectures that make use of containers, CI/CD best practices, advanced analytics tools, and more. Then, they have to keep all data and infrastructure safe from cybersecurity attacks and in compliance with the latest data regulations.

Put simply, there’s a lot to manage.

MLOps on the Cloud

That’s why it often makes sense to implement MLOps on a cloud platform, like Amazon Web Services (AWS), that comes packed with fully managed solutions specifically built to support ML initiatives.

For instance, on the data collection front, MLOps teams can use AWS tools like Amazon SageMaker Data Wrangler, SageMaker Processing, and SageMaker Ground Truth to prepare data. When it comes time for development, enterprises can use SageMaker Studio, a one-stop IDE through which users can access built-in algorithms, open-source models, and existing Docker images that accelerate the overall process. Furthermore, MLOps teams can train, tune, and manage ML experiments directly in SageMaker, offloading a tremendous amount of work that would otherwise consume data science and engineering resources.

AWS also simplifies model deployments by enabling MLOps teams to configure CI/CD pipelines according to their unique needs and set up continuous monitoring so that ML engineers can stay on top of usage, consumption, and results. Organizations can upgrade deployment further through serverless orchestration and batch transformations that ultimately feed large-scale predictions.

To summarize, AWS gives MLOps teams everything they need to be successful, so long as they understand how the cloud provider’s many tools enhance ML applications and deployments.

What if you’re not ready to go all-in on what AWS has to offer?

ClearScale recently developed a free MLOps Starter Kit that provides immediate access to basic, yet useful ML functionality that exposes users to key MLOps features and technologies. For example, ClearScale’s MLOps Starter Kit allows companies to deploy a pre-trained TensorFlow model in Amazon EKS using Docker, TensorFlow Serving, and Amazon Elastic Container Registry. The pre-trained TensorFlow model was developed using the MNIST Fashion dataset and tuned with the Keras Xception Convolutional Neural Network architecture.

Data engineers, data scientists, and software engineers can use ClearScale’s free MLOps Starter Kit to become familiar with what it takes to build, deploy, and maintain ML models from the ground up. Rather than jump immediately into AWS, MLOps teams can start small and expand as they learn what it takes to be successful.

Check out our FREE MLOps Starter Kit here.

For those ready to take MLOps to the next level, ClearScale is also available to help tackle projects big and small using the latest ML solutions and technologies available from AWS. Our machine learning services enable companies worldwide to train, monitor, and deploy sophisticated ML models at scale so that they can build truly differentiated applications on the cloud.

Get our free MLOps Starter Kit now and start running your own machine learning models.