I would add that, after conducting data analysis, the data scientist develops and tunes a machine learning (ML) model or a set of models. They test these models and design the overall ML pipeline to prevent issues like model drift and other undesirable effects. The ML engineer then takes over, building the ML pipeline, including configuration files, and deploying it. They also test the deployment cycle and provide feedback to the data scientist. This process includes monitoring and notification systems as part of the MLOps framework. This description is based on my personal experience, though practices may vary across different industries.
Excellent deep dive! But what if you're designing microservice app solution architectures including OLTP datamodels and data feeds into BI but not beyond that ?
Thank you so much for this! As a young Data Engineer who in the future wants to a great Data/Solution Architect I needed this! Also, just to clarify, is the Data Architect doing Solution Architecture AND Data Modeling? I’m still confused between Data Architect and Solution Architect.
Can you make a video about Databricks for Students who are interested to learn on how much it would cost for learning it using pay as you go and what all the techniques we can use to reduce the cost. There are many videos in youtube unless we do hands on nothing goes into head
This video explains how to get the Free Databricks Community Edition. Note: The screens may have changed a bit but it is still available. ua-cam.com/video/lcI1W2_KUPo/v-deo.html
Another great video, Bryan! Very informative and helpful. Keep up the good work!
Thanks!
I would add that, after conducting data analysis, the data scientist develops and tunes a machine learning (ML) model or a set of models. They test these models and design the overall ML pipeline to prevent issues like model drift and other undesirable effects. The ML engineer then takes over, building the ML pipeline, including configuration files, and deploying it. They also test the deployment cycle and provide feedback to the data scientist. This process includes monitoring and notification systems as part of the MLOps framework. This description is based on my personal experience, though practices may vary across different industries.
Funny and informative~
The most important thing is that it saved my assignment.
Thanks!
The preferred timeframe for completion is always as soon as possible! :D
Amazing content as always. I added more words to the comment for YT algorythms.
great content, keep it up Bryan, thank you so much!
Love your videos! Keep up the good work!
This is awesome and I found this very informative, thanks for sharing.
You're welcome!
really appreciate this breakdown!
I like Brian, your contents always gave me confidence, thank you
You're Welcome!
Excellent deep dive! But what if you're designing microservice app solution architectures including OLTP datamodels and data feeds into BI but not beyond that ?
Sounds like an application architecture rather than a data architecture. Regardless, the architecture should be based on the requirements.
Thank you so much for this! As a young Data Engineer who in the future wants to a great Data/Solution Architect I needed this!
Also, just to clarify, is the Data Architect doing Solution Architecture AND Data Modeling? I’m still confused between Data Architect and Solution Architect.
Good content.
Can you make a video about Databricks for Students who are interested to learn on how much it would cost for learning it using pay as you go and what all the techniques we can use to reduce the cost. There are many videos in youtube unless we do hands on nothing goes into head
This video explains how to get the Free Databricks Community Edition. Note: The screens may have changed a bit but it is still available. ua-cam.com/video/lcI1W2_KUPo/v-deo.html
Thanks, you're really good at explainin these topics!
Thank You!