MetaDAMA - Data Management in the Nordics
This is DAMA Norway's podcast to create an arena for sharing experiences within Data Management, showcase competence and level of knowledge in this field in the Nordics, get in touch with professionals, spread the word about Data Management and not least promote the profession Data Management.
-----------------------------------
Dette er DAMA Norge sin podcast for å skape en arena for deling av erfaringer med Data Management, vise frem kompetanse og kunnskapsnivå innen fagfeltet i Norden, komme i kontakt med fagpersoner, spre ordet om Data Management og ikke minst fremme profesjonen Data Management.
MetaDAMA - Data Management in the Nordics
2#13 - Xiaopeng Li - The Path to MLOps (Eng)
«MLOps is a set of practices that bring people, process and platform together into a stream-aligned process to manage End-2-End Machine Learning lifecycles.»
MLOps is taken about a lot, so I asked an expert what we are actually talking about. Xiaopeng Li is AI business lead at Microsoft for the Western European market, located in Oslo. Xiaopeng is a passionate influencer in the field of Data and AI/ML, who was nominated as AI influencer of the Year at last years DAIR-awards in Stockholm.
Here are my key takeaways:
Patterns in AI adoption
- AI adoption projects are quite diverse, but with some patterns that are visible across. Here are use-cases that a lot of industries are working with:
- Business Process Automation as an AI use case
- Adopting AI to process documents automatically and extract key-values
- Natural Language understanding and processing, but also Natural Language generation
- Chat GPT
- Knowledge Mining
- Unstructured data analysis
«Nordic countries are at the forefront when it comes to adopting AI and ML»
- Some of the most advanced search capabilities used in Microsoft are developed in Norway
- Nordic countries are typically quite tach-savvy
- Nordic countries have very good infrastructure
What is MLOps?
- MLOps is about agility, productivity, consistency and quality
- It is about creating scalability for your Data Science work
- MLOPs is a vage concept and you can probably find a variety of different definitions. Is MLOps at the intersection between DevOps, ML and Software Engineering?
- Scale ML development and deployment with constancy, with quality, with speed
The three elements that are most important are people, process and platform
- People:
- 5 particularly important roles: Stakeholder, Cloud Infrastructure Architect, Data Engineer, Data Scientist, Machine Learning Engineer
- There are many different roles involved in MLOps, from cleaning data to testing a model an implementing it. These roles need to be orchestrated
- Domain experts and stakeholders play a critical role in defining the challenge in the first place. They can formulate what to achieve and what is good enough
- Change Management is important, especially if your ML implementation triggers behavioral change
- Platform:
- You are in need of a secure, scalable infrastructure to would your models on
- Mature organizations who do ML at scale, have most an integrated architecture for Data Management, Analytics and Machine Learning
- Process:
- Data collection,. Data processing and data management are processes you need to focus on in MLOps
- You need a process and the right competencies to gather use-cases in the first place
- Build a backlog of initiatives and then go through prioritization based on eg. Data availability, feasibility of solution given current etch-landscape, value for business, cost, time to marked,..
Path to MLOps
- Always start with assessing your current landscape and maturity
- Start by assessing your platform capabilities
- Ensure you have the right competencies and people
- If you want to operationalize MLOps, don’t look at it as a technological problem, but something that includes the entire organization
- Key is to bring key stakeholders as early as possible into the discussion
Oslo AI:
https://www.linkedin.com/company/oslo-ai/
https://www.meetup.com/oslo-ai/
Link to MS learning:
