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.
How many languages do you know? (i am not talking about Sql. python or spark) :D Jokes aside, I really like how judgemental you are and your content is very crisp and to the point. Please keep sharing your learning!
Thanks. I'd guess I've learned about 30 or so languages over the years. Now there's a lot of spin off languages like Power BI has several sub languages.
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. th-cam.com/video/lcI1W2_KUPo/w-d-xo.html
Another great video, Bryan! Very informative and helpful. Keep up the good work!
Thanks!
Funny and informative~
The most important thing is that it saved my assignment.
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.
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!
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.
This is awesome and I found this very informative, thanks for sharing.
You're welcome!
Love your videos! Keep up the good work!
I like Brian, your contents always gave me confidence, thank you
You're Welcome!
really appreciate this breakdown!
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.
How many languages do you know? (i am not talking about Sql. python or spark) :D Jokes aside, I really like how judgemental you are and your content is very crisp and to the point. Please keep sharing your learning!
Thanks. I'd guess I've learned about 30 or so languages over the years. Now there's a lot of spin off languages like Power BI has several sub languages.
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. th-cam.com/video/lcI1W2_KUPo/w-d-xo.html
Thanks, you're really good at explainin these topics!
Thank You!