Most of the machine learning is focused on building models. But productionizing machine learning takes more than building a model. We need to figure out train the model at scale, how to effectively use GPU and Cloud technologies. And finally how to test, deploy and monitor models.
This series will focus on Machine Learning Engineering (MLE) and DevOps.
This series is organized as weekly sessions. Each session is independent. So you can pick and choose which sessions you want to attend.
This is part of our Learning Series.
This is a live, instructor-led, virtual class.
Can’t attend a live class?
No worries. Sign up and we will email you recordings once they are ready.
Session recordings will be available after each live session.
Click on each session link below to see the recording
(We also made a convenient play-list of session videos for you at the bottom)
Developers, DevOps, Data Scientists
- Some knowledge of machine learning is preferred but not required
- A background in development, data science
What to Bring
- We will provide code snippets for you to learn and experiment
- Please bring a reasonably modern laptop (Corporate laptops with overly restrictive firewalls may not work well; Personal laptops are recommended)
- Want to setup a Machine Learning environment on your laptop?
- Each session is about 2 hours
- lectures + hands-on
Session 1: A Tour of TensorFlow (2020 Apr 09)
Session 2: Speeding up training using GPU and TPU (2020 Apr 16)
Session 4: Training in the cloud using GPU (2020 Apr 30)
Session 5: Model serving (2020 May 14 rescheduled from May 07)
Session 6: Scalable machine learning with Apache Spark (2020 May 21)
Session 7: Scalable model serving architectures (2020 May 28)
Session 7B : Recap session – AUA (Ask Us Anything) (2020 May 28)
More Sessions to come
check back soon
Sujee Maniyam is a seasoned practitioner and founder of Elephant Scale. He teaches and consults in AI (machine learning and deep learning) and Big Data (Hadoop, Spark, NoSQL) and and Cloud technologies.
Session Recordings (On-Demand)