How can i run "Pattern discovery" on my dataset using aws sagemaker?
And of there is a simmiler term to
"Pattern discovery" because i cant find a lot of info about it
The concept lies downstream of Data Wrangling.
As can be seen in the official guide 'Prepare ML Data with Amazon SageMaker Data Wrangler:
Amazon SageMaker Data Wrangler (Data Wrangler) is a feature of Amazon
SageMaker Studio that provides an end-to-end solution to import,
prepare, transform, featurize, and analyze data. You can integrate a
Data Wrangler data preparation flow into your machine learning (ML)
workflows to simplify and streamline data pre-processing and feature
engineering using little to no coding. You can also add your own
Python scripts and transformations to customize workflows.
Here you will find a few tools for pattern recognition/discovery (and general data analysis) that will be used in SageMaker Studio: "Analyze and Visualize".
The data scientist's advice I can give is not to use no-code tools if you don't fully understand the nature of the data you are dealing with. A good knowledge of the data is a prerequisite for any kind of targeted analysis. Try writing some custom code instead, to have maximum control over each operation.
Related
I am trying to a clear picture of how they are interconnected and if the use of one always require the use of the other. If you could give a non-technical definition or explanation of each of them, I would appreciate it.
Please do not paste a technical definition of the two. I am not a software engineer or data analyst or data engineer.
These two paragraphs summarize the difference quite good (from this source)
Spark is a general-purpose cluster computing system that can be used for numerous purposes. Spark provides an interface similar to MapReduce, but allows for more complex operations like queries and iterative algorithms. Databricks is a tool that is built on top of Spark. It allows users to develop, run and share Spark-based applications.
Spark is a powerful tool that can be used to analyze and manipulate data. It is an open-source cluster computing framework that is used to process data in a much faster and efficient way. Databricks is a company that uses Apache Spark as a platform to help corporations and businesses accelerate their work. Databricks can be used to create a cluster, to run jobs and to create notebooks. It can be used to share datasets and it can be integrated with other tools and technologies. Databricks is a useful tool that can be used to get things done quickly and efficiently.
In simple words, Databricks has a 'tool' that is built on top of Apache Spark, but it wraps and manipulates it in an intuitive way which is easier for people to use.
This, in principle, is the same as difference between Hadoop and AWS EMR.
I can't understand the difference between SageMaker instance count and Data parallelism. As we already have a feature that can specify how many instances we train model when we write a training script using sagemaker-sdk.
However, in 2021 re:Invent, SageMaker team launched and demonstrated SageMaker managed Data Parallelism and this feature also provides distributed training.
I've searched a lot of sites for letting me know about that, but I can't find really clear demonstration. I share some stuffs explaining the concept I mentioned closely. Link : https://godatadriven.com/blog/distributed-training-a-diy-aws-sagemaker-model/
Increasing the instance count will enable SageMaker to launch those many instances and copy data to the instances. This will only enable parallelization at the infrastructure level. To really carry out distributed training we need support at framework/code level where the code should know how to aggregate/send gradients across all the GPU's/instances within the cluster. In some case how to distribute data as well usually when using DataLoaders. To achieve this SageMaker has Distributed Data Parallelism feature built into it. This is similar to other alternatives like Horovod, Pytorch DDP etc...
I have been using zeppelin for a couple of years, now superset is gaining more attention for better Visualization features etc. so I am trying to understand exact differences and also help if someone is looking to select a BI tool.
I have listed a few unique features based on initial reading on superset, it would be really appreciated if anyone can contribute more to the list .
Most big data cluster integration support (Spark, flink etc)
Inline code execution using paragraphs
Multi language supports
As I am not a proper user of superset,I would like to know more unique features of Zeppelin and these features are not possible or hard to do in Superset.
Also I got below details from apache wiki, but I don't see these can be unique factor except leveraging notebooks style
Apache Zeppelin is an indirect competitor, but it solves a different use case.
Apache Zeppelin is a web-based notebook that enables interactive data analytics. It enables the creation of beautiful data-driven, interactive and collaborative documents with SQL, Scala and more. Although a user can create data visualizations using this project, it leverages a notebook style user interfaces and it is geared towards the Spark community where Scala and SQL co-exist
Fundamentally, Zeppelin and Superset take significantly different viewpoints on the data workflow.
Zeppelin is centered around the [computational notebook interface][1], which enables you to write code fragments, run them and internalize the output, and iterate & expand. Zeppelin notebooks then focus on working with 20+ programming [languages and interpreters][2]. Zeppelin can also query popular databases using the JDBC connector.
Superset is centered around the BI use case and ships with a SQL IDE and a no-code chart builder. The important difference here is that Superset can only query data from SQL speaking databases. Superset, unlike Zeppelin, doesn't enable you to run arbitrary code from a variety of programming languages.
The use cases, workflows, and design choices are very different between both of these tools. Superset wants to enable end-users & analysts and SQL ninjas to create dashboards (that others in an organization may consume). Zeppelin wants to level up data scientists & programmers to analyze data, and is less focused on building dashboards for the rest of the organization to consume.
[1]: https://en.wikipedia.org/wiki/Notebook_interface#:~:text=A%20notebook%20interface%20(also%20called,and%20text%20into%20separate%20sections.
[2]: https://zeppelin.apache.org/supported_interpreters.html
I want to avoid using sagemaker notebook and preprocess data before training like simply changing the from csv to protobuf format as shown in the first link below for the built-in models.
https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-preprocess-data-transform.html
In the following example it explains preprocessing by using sklearn pipelines with the help of sagemaker python-sdk
https://aws.amazon.com/blogs/machine-learning/preprocess-input-data-before-making-predictions-using-amazon-sagemaker-inference-pipelines-and-scikit-learn/
What are the best practices if you just need to do format like changes and you don't need to use sklearn way of processing.
It's not necessary to use SageMaker Notebook instances to perform pre-processing or training. Notebooks are way to explore and carry out experiments. For production use cases, you can orchestrate tasks in a ML pipeline such as pre-processing, data preparation (feature engineering, format conversion etc.), model training and evaluation using AWS Step Functions. Julien has covered it in his recent talk here.
You can explore using AWS Glue for pre-processing either using Python script (via Python Shell) or Apache Spark (Glue job). Refer this blog here for such use case
https://aws.amazon.com/blogs/machine-learning/ensure-consistency-in-data-processing-code-between-training-and-inference-in-amazon-sagemaker/
i wanna know what are the pros and cons using Fusion instead of regular Solr ? can you guys give some example (like some problem that can be solved easily using Fusion)?
First of all, I should disclose that I am the Product Manager for Lucidworks Fusion.
You seem to already be aware that Fusion works with Solr (or one or more Solr clusters or instances), using Solr for data storage and querying. The purpose of Fusion is to make it easier to use Solr, integrate Solr, and to build complex solutions that make use of Solr. Some of the things that Fusion provides that many people find helpful for this include:
Connectors and a connector framework. Bare Solr gives you a good API and the ability to push certain types of files at the command line. Fusion comes with several pre-built data source connectors that fetch data from various types of systems, process them as appropriate (including parsing, transformation, and field mapping), and sends the results to Solr. These connectors include common document stores (cloud and on-premise), relational databases, NoSQL data stores, HDFS, enterprise applications, and a very powerful and configurable web crawler.
Security integration. Solr does not have any authentication or authorizations (though as of version 5.2 this week, it does have a pluggable API and an basic implementation of Kerberos for authentication). Fusion wraps the Solr APIs with a secured version. Fusion has clean integrations into LDAP, Active Directory, and Kerberos for authentication. It also has a fine-grained authorizations model for mananging and configuring Fusion and Solr. And, the Fusion authorizations model can automatically link group memberships from LDAP/AD with access control lists from the Fusion Connectors data sources so that you get document-level access control mirrored from your source systems when you run search queries.
Pipelines processing model. Fusion provides a pipeline model with modular stages (in both API and GUI form) to make it easier to define and edit transformations of data and documents. It is analogous to unix shell pipes. For example, while indexing you can include stages to define mappings of fields, compute new fields, aggregate documents, pull in data from other sources, etc. before writing to Solr. When querying, you could do the same, along with transforming the query, running and returning the results of other analytics, and applying security filtering.
Admin GUI. Fusion has a web UI for viewing and configuring the above (as well as the base Solr config). We think this is convenient for people who want to use Solr, but don't use it regularly enough to remember how to use the APIs, config files, and command line tools.
Sophisticated search-based features: Using the pipelines model described above, Fusion includes (and make easy to use) some richer search-based components, including: Natural language processing and entity extraction modules; Real-time signals-driven relevancy adjustment. We intend to provide more of these in the future.
Analytics processing: Fusion includes and integrates Apache Spark for running deep analytics against data stored in Solr (or on its way in to Solr). While Solr implicitly includes certain data analytics capabilities, that is not its main purpose. We use Apache Spark to drive Fusion's signals extraction and relevancy tuning, and expect to expose APIs so users can easily run other processing there.
Other: many useful miscellaneous features like: dashboarding UI; basic search UI with manual relevancy tuning; easier monitoring; job management and scheduling; real-time alerting with email integration, and more.
A lot of the above can of course be built or written against Solr, without Fusion, but we think that providing these kinds of enterprise integrations will be valuable to many people.
Pros:
Connectors : Lucidworks provides you a wide range of connectors, with those you can connect to datasources and pull the data from there.
Reusability : In Lucidworks you can create pipelines for data ingestion and data retrieval. You can create pipelines with common logic so that these can be used in other pipelines.
Security : You can apply restrictions over data i.e Security Trimming data. Lucidworks provides in built query-pipeline stages for Security Trimming or you can write custom pipeline for your use case.
Troubleshooting : Lucidworks comes with discrete services i.e api, connectors, solr. You can troubleshoot any issue according the services, each service has its logs. Also you can configure JVM properties for each service
Support : Lucidworks support is available 24/7 for help. You can create support case according the severity and they schedule call for you.
Cons:
Not much, but it keeps you away from your normal development, you don't get much chance to open your IDE and start coding.