MLOps practices prove useful when an organization wishes to transform itself by deploying machine learning and data science projects. Since it takes a good amount of resources and aid to get these deployments going, adopting AWS MLOps practices help. But what is AWS MLOps? Let's understand this combination in this blog.
What Is AWS MLOps?
MLOps or machine learning operations is rapidly getting adopted in data science. In fact, it's a combination that amalgamates the best of software engineering, DevOps and data science. MLOps refers to a set of practices that enable the deployment of ML models into production. This job generally falls under the roles and responsibilities of ML engineers, software engineers, data scientists and data engineers. Amid this, AWS MLOps pertains to the management and integration of ML pipelines on the AWS ML services. Consequently, data science reaches the customers.
Amazon SageMaker for MLOps
The Amazon SageMaker service presents a suite of purpose-built tools for MLOps. The goal is to aid the organization in automating and standardizing all the processes spanning across the ML lifecycle. The tools for SageMaker MLOps are employed for easily training, deploying, governing, testing and troubleshooting ML models on a large scale. This leads to enhanced productivity showcased by ML engineers and data scientists. The model's performance in production is also maintained.
Benefits of SageMaker MLOps
Employing SageMaker MLOps benefits organizations in a plenty of ways. These are a few on the list.
Standardize Data Science Environments
Standardizing ML development environments leads to enhanced productivity of data scientists. This enhances innovation pace by implementing ML best practices and easily launching new projects. With SageMaker templates, environments are easily standardized along updated and tested libraries and tools.
Automate CI/CD Workflows
SageMaker onboards CI/CD practices to machine learning. This includes maintaining parity between A/B testing, end-to-end automation, production & development environments and version & source control. Built-in safeguards facilitate maintenance of endpoint availability and minimizing deployment risks.
Track & Manage Model Versions Centrally
Developing data pipelines, validation tests, models and training pipelines is a part of building an ML app. With SageMaker Model Registry, track models versions, model performance metrics and their metadata. It also logs approval workflows for compliance and audit automatically.
Automate ML Training Workflows
Automating training workflows enable creation of repeatable processes for orchestrating model development steps. This aids in rapid model re-training and experimentation. The complete model build workflow can be automated. This includes feature engineering, model tuning, model validation, model training and data preparation.
Define ML Infrastructure via Code
SageMaker Projects enables writing IaC with pre-built template files. The IaC approach is adopted mainly to provision ML infrastructure & implement solution architecture as mentioned CI/CD pipelines.
Final Words AWS MLOps
MLOps is here to stay because an increasing number of businesses are looking forward to adopting machine learning. Since the practice of MLOps is all about employing the finest tools, technologies and platforms, its amalgamation with AWS is brilliant. A future in AWS MLOps seems bright and it's still expanding. Course Schedule
