Aegis School of Business, Data Science, Cyber Security & Telecommunication
| Course fee: | 29000 * INR |
| GST: | 18% % |
Machine Learning Operations (MLOps)
Details
| Location: | Online Live interactive |
|---|---|
| Type: | Certificate course |
| Course fee: | 29000 * INR |
| GST: | 18%% |
| Total course fee: | 34220 * INR |
| Enrollment method: | Direct Payment |
| Application deadline: | Sep 30, 2021 |
| Rating: |
Course Details
Machine Learning Operations (MLOps)
What Is MLOps?
Machine Learning Layered on DevOps
MLOps is modeled on the existing discipline of DevOps, the modern practice of efficiently writing, deploying and running enterprise applications. DevOps got its start a decade ago as a way warring tribes of software developers (the Devs) and IT operations teams (the Ops) could collaborate.
MLOps adds to the team the data scientists, who curate datasets and build AI models that analyze them. It also includes ML engineers, who run those datasets through the models in disciplined, automated ways.
It’s a big challenge in raw performance as well as management rigor. Datasets are massive and growing, and they can change in real-time. AI models require careful tracking through cycles of experiments, tuning and retraining.
With Machine Learning Model Operationalization Management (MLOps), we want to provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.
Being an emerging field, MLOps is rapidly gaining momentum amongst Data Scientists, ML Engineers and AI enthusiasts. MLOps capabilities:
- MLOps aims to unify the release cycle for machine learning and software application release.
- MLOps enables automated testing of machine learning artifacts (e.g. data validation, ML model testing, and ML model integration testing)
- MLOps enables the application of agile principles to machine learning projects.
- MLOps enables supporting machine learning models and datasets to build these models as first-class citizens within CI/CD systems.
- MLOps reduces technical debt across machine learning models.
- MLOps must be a language-, framework-, platform-, and infrastructure-agnostic practice.
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
This course is primarily intended for the following participants:
- Data Scientists looking to quickly go from machine learning prototype to production to deliver business impact.
- Software Engineers looking to develop Machine Learning Engineering skills.
- ML Engineers who want to adopt Google Cloud for their ML production projects.
Course Prerequisite:
- Programming experience (preferred Python)
- Basic Linux command and fundamentals
- ML fundamentals