MLOps Study Opportunities in 2022
For those seeking to gearshift into a new realm of expertise on the cutting edge of innovative tech, there are endless opportunities to upskill in exciting topics and one of these is MLOps. The team from FourthBrain are offering regular courses and, coordinated by a team including Andrew Ng, these are sure to be a hit. To quote their latest marketing email,
We are different because we:
Combat bias and promote equity
We incorporate practice in spotting bias and working to mitigate it, in both interpersonal settings and in your technical work.
Develop communication and collaboration skills
Employers value engineers who have strong communication skills, so we incorporate practice in written and verbal communication, with an emphasis on collaborating remotely throughout the program.
Integrate career support through the program
Our program will prepare you with the technical and soft skills desired by employers. We will also provide 1:1 career coaching and other sessions with industry experts to help you put your best foot forward.
Curate curriculum and build custom projects
Our curriculum is curated from the best online content including deeplearning.ai, which has educated thousands of engineers. The content supports your learning to build custom projects developed by the FourthBrain team to apply and demonstrate your skills.
We hope we can help you on your journey into machine learning!
-The FourthBrain Team
For links to the syllabi so you can directly assess which is best suited to your needs, see here for Machine Learning Engineering and here for MLOps Applications & Systems. Some highlights from those, and for each at least one link to content I’ve personally found to be hugely informative:
Predictive Data Analytics
- Given: Historic data regarding customer online purchase behavior.
- Goal: Predict quarterly sales and customer purchases, analyze categories of customer behavior, and gauge customer response for new products or automated product categorization.
Deep Learning and Computer Vision
- Given: Images and videos annotated for object detection for cars, pedestrians, bicyclists, lane markers, street signs.
- Goal: Design optimized end-to-end and explainable models for a) object detection, b) tracking, and c) semantic segmentation.
Generative Adversarial Networks
- Given: A finite image/video annotated dataset for semantic segmentation.
- Goal: Automatically increase training image data volumes and training image dimensionality, to reuse annotations and enable robust object detection models.
Time Series Modelling (Recurrent Neural Networks)
- Given: Volumes of web analytics data, with session-based information regarding consumer purchase behavior for viewing, adding and removing products from the cart, and purchasing.
- Goal: Create a stochastic prediction of customer purchase probability per session that will enable the creation of marketing nudge models.