I have built a machine learning model in R for Preventing Application Fraud in Loans using
ensemble of 5 submodels. I am looking to deploy it but I am clueless how to use h2o for this. can anyone explain briefly how to use it?
You can read all about productionizing a model in the H2O User Guide here
Related
My question here is my own follow up of a school project. We implemented a basic train system with 15 cities and when the user inputed 2 cities (aka current location and destination) the app returned the quickest way from A to B as well as the cheapest. We ran A Star algorithms for this.
My question here is: If i wanted to implement this with Spring Boot and Azure how would i do it? I assume MySQL for pathfinding could be bad in performance or even just impossible to simulate a graph. We used guava graph (extended versions) for the "vanilla" app, but how would i do it in a real world environment? How would i be able to run a star searches? I've heared about Cosmos Gremlin and there is a Spring Boot starter and all, but would this be possible? If so how, or even if its reasonably doable with MySQL, how?
Thanks in advance
I am looking to implement a constrained conditional model in H2O. Are there any resources that can help me get started on implementing a new framework in H2O ?
I have been looking at the github repo. Would I start by looking at implementations of hex.Model as a starting point to understand how to implement?
If there are any samples, etc., would be much appreciated.
I am having 4 years of experience in .net I would like to learn new technology, what could be best for me learning Hadoop or SalesForce?
There is no answer to this question. Hadoop and SalesForce are completely different technologies. Hadoop is distributed storage and processing that is great for big data. SalesForce is a cloud based CRM tool.
The question to ask yourself, is what do you want next? Are you looking for a steady job? Are you looking for a career in a specific field where one of these technologies would be more helpful? What do you want?
I'd like to train a model using Spark ML Lib but then be able to export the model in a platform-agnostic format. Essentially I want to decouple how models are created and consumed.
My reason for wanting this decoupling is so that I can deploy a model in other projects. E.g.:
Use the model to perform predictions in a separate standalone program which doesn't depend on Spark for the evaluation.
Use the model with existing projects such as OpenScoring and provide APIs which can make use of the model.
Load an existing model back into Spark for high throughput prediction.
Has anyone done something like this with Spark ML Lib?
Version of Spark 1.4 now has support for this. See latest documentation. Not all models are available (see to be supported (see the JIRA issue SPARK-4587).
HTHs
I have an academic course "Middleware" which covers different aspects of Distributed Software Systems including introduction to topics like [tag:Distributed File system]. This also involves introduction to hbase,hadoop,mapreduce,hiveql,piglatin.
I want to know, can I have a small project which tries to integrate above technologies. For starters, I am aware of vm provided by cloudera for having a feel of hadoop and playing around using Eclipse.
I was thinking on lines of implementing an application which accepts stream of events as an input, Analyses this and gives an output.
I have both windows/linux on my machine with i7 procoessor and 4Gb Ram.
Please let me know how to get started with everything and any suggestions for simple example application are welcome.
Here is a blog post on analyzing Tweets using Hive/HDFS. And here is a blog post on performing Clickstream analytics using Pig and Hive.
Check some of the Big Data use cases here and try to solve an interesting problem.