Many times, when an organization is asked to add more ML-based features or products, they immediately start with hiring ML experts or experienced data scientists.
After finding the best talent in the market, the new data scientists are facing the challenge of access to data and no existing infra to support their needs.
The new data scientists team is now facing a data and MLOps challenge rather than a pure data science one. They spend 70% of their time collecting, cleaning, and preparing the data for ingesting them to the machine learning algorithms.
Later they face the challenge of validating the models and communicating the outcomes to the engineering teams to code the model and deploy to production.
This full cycle can take months, and by the time the ML model is being deployed to productions, the competitors already developed a better one, and the company losses its technical edge.
This is why data scientists need a platform to create and build their state of the art machine learning models.
Microsoft Azure is offering Azure Machine Learning to help with this task.
Here is a free 1 hour course for you to learn about it:
To understand the importance of such a platform, we've met with Yaron Haviv, Co-founder and CTO of Iguazio for a chat on how they work with Microsoft and the evolving world of productionizing Machine Learning.