Build/Buy in MLOPs for R&D - Does “off-the-shelf” exist yet?

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Topic: Build/Buy in MLOPs for R&D - Does “off-the-shelf” exist yet?

Speaker: Ariel Biller, Evangelist at ClearML
Researcher first, developer second. Over the last 5 years, Ariel has worked on various projects; from the realms of quantum chemistry, massively parallel supercomputing to deep-learning computer vision. With AllegroAi, he helped build an open-source R&D platform (Allegro Trains) and later went on to lead a data-first transition for a revolutionary nanochemistry startup (StoreDot). Answering his calling to spread the word, he recently took up the mantle of Evangelist at ClearML. Ariel received his Ph.D. in Chemistry in 2014 from the Weizmann Institute of Science. Ariel recently made the transition to the bustling startup scene of Tel-Aviv, and to cutting-edge Deep Learning research.

Abstract:
What kind of tools and infrastructure does a company need in order to build, train, validate and maintain data-based models as part of products?
The straight answer is - “it depends.” The longer one is: “MLOps.”
It is far too early to determine the “best” patterns and workflows for Data-Science, Machine- and Deep-Learning products. Yet, there are numerous examples of successful deployments from businesses both big and small.
Most have done this by building an internal platform to base their R&D on, which enables “productization” of the model-building process. These platforms are scarcely built “from scratch.” Instead, they are dependent on existing hardware, frameworks, and toolchains.
In this webinar, we will try to answer the following questions:
What capabilities should an internal MLOps platform have?
To what extent does one’s platform can be dependent on open-source infrastructure?
Lastly, how “deep” into R&D can one introduce MLOps practices for productivity and reproducibility?

We will be using a real-world example to help ground the ideas, featuring “lessons learned” from infrastructure at theator: A Surgical Intelligence startup company. theator provides revolutionary personalized analytics on surgical operation videos. For that purpose, theator uses continuous training and inference pipelines, with emphasis on straightforward transitions from R&D to “production”. We will also mention topics such as hybrid orchestration through pipeline design, continuous training, and finally, advanced dataset management complying with the privacy requirements.

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