Machine learning
Are you in charge of developing and implementing machine learning solutions in your organisation?
The process of identifying the organizations’ challenges, translating the technical problem that needs solving and communicating the solution to users can be complex.
IDAs peer group offers valuable insights for data scientists who work with optimization of machine learning models, finding, and testing algorithms and converting data to knowledge.
Share knowledge with like-minded machine learning specialists
Even though your tasks are set inside the organization, you may not have many colleagues who can give you competent feedback on your work with machine learning. As a member of IDAs peer group, you gain insights into projects from other companies that can help better your own practices.
At the peer group meetings, you can get feedback on your data models and suggestions for improvement to optimize output. You will become part of productive discussions with likeminded professionals who understand your technical competencies and the need for communicative skills when a new product is embedded in the organization.
Price and information
- IDA members:
6.475 DKK .pr. year (exempt from VAT) - Non-members
7.475 DKK pr. year (exempt from VAT)
The peer group
- Max 15 members
- 5 meetings pr. year
- The meetings are physical
- Themes and dates decided by the members
- Professional peer group facilitator plans the meetings
You choose the themes
Your interests serve as the foundation for the peer group, and you can contribute with ideas for subjects, cases or presentations that facilitate a constructive exchange of knowledge and experiences at the group meetings. Our tools are mentoring and discussion, case work and visits from external experts who can contribute with new insights and methods.
Examples of subjects you can discuss in the group
- Quality control of models – also without knowing the data (e.g. if others are using the model)
- Communication and matching expectations with users/clients
- How to chose a model/family of algorithm
- Best-practice: What works, and why?
- Transparency in the model: cause analysis/mutual impact
- Application of deep-learning/neural networks
- Data quality
- Software operationalisation and productionalisation
- Platform architecture
Because you are all specialists, you can have discussions about very specific challenges related to machine learning. Your common professionalism ensures that valuable time is not lost on explaining models and algorithms.
"I had a comprehensive project in mind, which I was actually advised against by the other group members. They believed that the gains were too small, and that I could do something simpler that could provide the same precision. I was very happy about their input."Andreas Alberg-Fløjborg,
Data scientist in Fjernvarme Fyn and member of the peer group for machine learning