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Apache Airflow for Machine Learning Operations
This Apache Airflow for Machine Learning Operations training course teaches machine learning (ML) engineers how to build and validate training models, upload models to a model registry, and deploy...
Read MoreOverview
This Apache Airflow for Machine Learning Operations training course teaches machine learning (ML) engineers how to build and validate training models, upload models to a model registry, and deploy models in a reproducible manner.
Attendees learn machine learning operations and the complexities of creating a reproducible CI/CD pipeline for ML models. Next, students explore options to reduce this gap with Apache Airflow for batch training scenarios (which are the majority). In addition, attendees learn the foundations of Airflow and how it creates reproducible and trustworthy pipelines via DAGs (Directed Acyclic Graphs).
This course focuses on real-world applications of ML using both traditional machine learning algorithms and deep learning algorithms, such as sentiment prediction in a stream of tweets.
Throughout the course, students tackle diverse machine learning problems by creating reproducible pipelines with Airflow.
Skills Gained
- Migrate machine learning training workflows to scalable pipelines in Apache Airflow
- Start with a raw dataset and a model architecture and take the project from beginning to end, culminating in deploying it in the cloud
- Enforce reusability and modularization of pipelines for easy collaboration
Prerequisites
Students must have basic Python knowledge or object-oriented programming experience. Knowledge of machine learning is helpful but not required.
Course Details
Training Materials
All Apache Airflow for Machine Learning training attendees receive comprehensive courseware.
Software Requirements
This course is taught using:
- Python 3.5 or later
- Apache Airflow 2.1 or later
- scikit-learn 1.1 or later
- PyTorch 1.8 or later
On request, we can provide either a remote VM environment for the class or directions for configuring this environment on your local PCs.
Outline
- Introduction
- The Scalable Problem of Machine Learning Pipelines
- What problems arise when trying to create a machine learning model?
- The components of a machine learning platform
- Introducing Apache Airflow
- Airflow architecture
- How do we represent a machine learning pipeline?
- Our first DAG
- Tasks, TaskFlows, and Operators
- First Pipeline
- Cresting the datasets for training
- Creating our Machine Learning Pipeline
- Using custom operators
- Creating a Train Operator
- Creating TaskGroups vs subDAGs
- Sharing data with xCOMs
- Branching and Triggers
- Sensors and SmartSensors
- Adding a sensor to validate enough new data
- Adding training, validation, and delivery steps to our pipeline
- Mastering Scheduling
- execution_date, start_date, and schedule_interval
- Handling non-default schedule_intervals
- Playing with time
- Using Sensors with a correct schedule_interval
- Enabling Concurrency and Scalability
- Abandoning SQLite to PostgreSQL
- Executors: Debug, Local, Celery
- Concurrency and parallelism
- Concurrency with Celery
- Hackathon: Sentiment Prediction from Twitter
- Conclusion
Schedule
FAQ
Does the course schedule include a Lunchbreak?
Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.
What languages are used to deliver training?
Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.
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GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.
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Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.
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As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.
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Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.
Do you provide hands-on training and exercises in an actual lab environment?
Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.
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Reviews
The exit certified aws course provided a good introduction to the tools available on aws.
Overall experiance is very nice. the online training plateform is very advance.
vary good online learning. instructor is vary good the way he explained every thing.
Course was great and the instructor had an answer for anything that was asked during the course.
Good training. A lot to take in for the short amount of time we have though
