Data scientists spend at the moment around 80% of their time on tasks that are not related to modelling, trying to collect, prepare, and analyse the data. It takes a lot of time to perform all these activities and the biggest challenge is that they are neither reproducible nor reusable. Thus, very often projects get stuck in the experimentation phase and the return on investment on AI initiatives is much lower. Reports such as the IBM Adoption AI, mention that one of the biggest challenges is the limited AI skills, knowledge or expertise. Upskilling in the are includes both, writing code, but also developing best practices that enable faster project delivery.
MLOps is DevOps, but for machine learning. With a set of practices that aims to deploy, and maintain machine learning models in production reliably and efficiently. MLOps platforms provide data scientists and machine learning engineers with an environment that facilitates collaboration, allowing iterative data exploration, real-time co-working, feature engineering, and model management. It automates tasks and creates reproducible ways to roll out data science solutions.
During the workshop, we will get familiar with MLOps tooling and go through the machine learning lifecycle using it. By taking a real-life dataset from the open source world, we can see how AI eases the life of developers and contributors.
Session author's bio
I am a Product Manager at Canonical, leading the MLOps area. With a background in Data Science in various industries, such as retail or telecommunications, I used AI techniques to enable enterprises to benefit from their initiatives and make data-driven decisions. I am looking to help enterprises get started with their AI projects and then deploy them to production, using open-source, secure, stable solutions.
I am a driven professional, passionate about machine learning and open source. I always look for opportunities to improve, both myself and people within the teams that I am part of. I enjoy sharing my knowledge, mentoring young professionals and having an educational impact in the industry.
|Level of Difficulty||Intermediate|