Amazon Rekognition vs. Azure Databricks

Amazon Rekognition is well suited for all image and videos analysis. Also, deep learning projects for image and video can easily be done using this. It is very easy to use so a beginner can also use it.

Incentivized

Suppose you have multiple data sources and you want to bring the data into one place, transform it and make it into a data model. Azure Databricks is a perfectly suited solution for this. Leverage spark JDBC or any external cloud based tool (ADG, AWS Glue) to bring the data into a cloud storage. From there, Azure Databricks can handle everything. The data can be ingested by Azure Databricks into a 3 Layer architecture based on the delta lake tables. The first layer, raw layer, has the raw as is data from source. The enrich layer, acts as the cleaning and filtering layer to clean the data at an individual table level. The gold layer, is the final layer responsible for a data model. This acts as the serving layer for BI For BI needs, if you need simple dashboards, you can leverage Azure Databricks BI to create them with a simple click! For complex dashboards, just like any sql db, you can hook it with a simple JDBC string to any external BI tool.

Incentivized

• Image tagging is very good.
• Object detection is precise.
• Video tagging has become very easy using Rekognition.

Incentivized

• SQL
• Data management
• Data access

Incentivized

• The cost is bit more for small scale companies.
• For text processing, OCR is not available in amazon rekognition.
• Only facial search is available in image search.

Incentivized

• Their pipeline workflow orchestration is pretty primitive. Lacks some common features
• Workspace UI and navigation requires steep learning curve
• Personally, I am not fond of their autosave feature. Its dangerous for production level notebooks scripts

Incentivized

Very much suitable for many applications where the image processing features are secondary and independent of any domain. This makes it a general solution and the recognition features are returned in a JSON object in response to the API called made which is a very simple process.

Incentivized

Based on my extensive use of Azure Databricks for the past 3.5 years, it has evolved into a beautiful amalgamation of all the data domains and needs. From a data analyst, to a data engineer, to a data scientist, it jas got them all! Being language agnostic and focused on easy to use UI based control, it is a dream to use for every Data related personnel across all experience levels!

Incentivized

Support of Amazon is great. So far we are having a great experience using it.

Incentivized

It's easy to implement and its documentation is great compared to CNTK.

Incentivized

Against all the tools I have used, Azure Databricks is by far the most superior of them all! Why, you ask? The UI is modern, the features are never ending and they keep adding new features. And to quote Apple, "It just works!" Far ahead of the competition, the delta lakehouse platform also fares better than it counterparts of Iceberg implementation or a loosely bound Delta Lake implementation of Synapse

Incentivized

• The best thing it made our work fast.
• It reduces the chance of failure of a project which is based on image or video processing.
• We can assign anyone in our team to work on this as it very easy to use.

Incentivized

• Helped reduce time for collecting data
• Reduced cost in maintaining multiple data sources
• Access for multiple users and management of users/data in a single platform

Incentivized