From Machine Learning Theory to the final use case industrialization — WE REPLY with our Machine Learning Incubator

An Overview over the Machine Learning Incubator

Our Machine Learning Incubator has successfully run for several years, now we have updated it to better meet the current challenges our clients regularly face.

The Incubator offers a customized training to entire Business and Data Science teams, as well as C-Level Management - It is for everybody who wants to grasp the business concepts and behind the algorithms and anticipate ongoing trends to build or enrich their ML & AI capabilities.

Based on the client’s industry and the specific challenges the respective organization faces, WE REPLY with tailor-made trainings, where we:

• facilitate the client’s hands-on experience by developing real-life use cases
• offer expert mentoring to ensure the client’s continuous progress over the course of the program
• connect the client with our large ML Incubator alumni network to stay up to date with the latest developments and get access to the best-in-class talents
• adjust the workshop according to the client’s needs and schedule to ensure the best experience - either online or on-site

Our workshop offering is based on ML Reply’s background knowledge in ML and Data Strategy topics, forming the main building blocks of the program - Machine Learning Theory, Machine Learning Tools, AI-Project Management and Data Literacy.

In addition to a thorough introduction to Machine Learning, Artificial Intelligence & Data Science, we examine the client’s respective industry situation with regard to ML & AI business opportunities. Based on the industry trends and the client’s needs different topics such as advanced visualization solutions (e.g. Thoughtspot & Kibana), automated ML solutions (e.g. DataIku & DataRobot) as well as advanced ML approaches (supervised/unsupervised learning, neural networks & deep learning) are introduced. These topics can be combined with the joint development of real-life Use Cases to deepen the knowledge and provide hands-on experience.

By developing these use cases, the ML Incubator offers the opportunity to walk the participants through the whole end-2-end (E2E) use case lifecycle, covering everything the client needs to know about ML & AI - from the basics up to the advanced knowledge.

As part of the re-launch of our Machine Learning Incubator, in the next weeks we focus on a typical End-2-End ML & AI-Project. We cover the four main building blocks of an ML project and consider them from both a strategic and technological perspective. Additionally, we will introduce our best practices such as use case-evolution and implementation frameworks.

WE REPLY with the four core steps of ML & AI use case industrialization:

A: ML & AI strategy development

B: Transformation from strategic use-case conception to use-case industrialization

C: Scaling of ML & AI use cases

D: Collaboration and anchoring the AI mindset within the company