Tools That Help You Grow from an ML Engineer or DevOps Engineer into MLOps
If you are working as a machine learning engineer or a DevOps engineer but want to shift your career path and become an MLOps engineer, this article will help you get started by pointing out which tools and services you should begin learning.
With the rapid development of artificial intelligence technologies worldwide and the construction of data centers, new specializations are emerging that require not only knowledge in areas such as data, but also an understanding of infrastructure designed specifically for processing that data. One such specialization is MLOps.
As someone who works with both data and the infrastructure for processing it, I clearly see that MLOps is a natural next step in the development of skills and knowledge for both DevOps engineers and ML engineers. There are many services that can help these specialists gain the necessary experience and skills to transition into MLOps. These include S3, SageMaker, Step Functions, Kubeflow, Airflow, AWS Lambda, and Amazon Kinesis.
In this article, I would like to share my experience working with AWS Lambda and Amazon Kinesis to solve MLOps-level tasks.
Due to the growth in data volumes and machine learning models, organizations increasingly face the challenge of not only training models but also maintaining them in production. This includes updating data, monitoring performance, and automating processes.
In turn, the use of AWS Lambda and Amazon Kinesis makes it possible to:
- Process data in real time
- Automate the запуск of ML pipelines without constant server management
- Easily integrate models with other AWS services
As mentioned above, Amazon Kinesis enables the collection and processing of large streams of data in real time.
Let’s consider a scenario where IoT devices send a stream of logs, and we want to detect anomalies in real time using a machine learning model.
Create a stream using the AWS CLI:
Example of sending events:
AWS Lambda automatically receives batches of records from Amazon Kinesis. It is important to note that the data is delivered in base64 format.
The model is now deployed via an endpoint in Amazon SageMaker and is invoked from Lambda. For monitoring, use Amazon CloudWatch, where it is essential to configure alerts for Lambda errors, processing delays (IteratorAge), and function execution times. To increase reliability, use Amazon SQS as a Dead Letter Queue. Increase the shard count in Kinesis to improve parallelism. Configure batch size and concurrency in Lambda. Use auto-scaling for the SageMaker endpoint. Monitor load using CloudWatch.
This article clearly shows that using AWS Lambda and Amazon Kinesis enables building scalable real-time MLOps pipelines without managing servers. My advice is to start with a simple pipeline and then gradually add monitoring, scaling, and automation.
This approach will help you move to the MLOps level while also allowing you to see how data and infrastructure work together as a single system.
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