I can't tell if I will be charged to use Model Registry on Vertex AI. Will it cost anything?
Short answer: No.
From the pricing page:
https://cloud.google.com/vertex-ai/pricing#modelregistry
The Vertex AI Model Registry is a central repository which tracks and lists your models and model versions. You can import models into Vertex AI and they appear in the Vertex AI Model Registry. There is no cost associated with having your models in the Vertex AI Model Registry. Cost is only incurred when you deploy the model to an endpoint or perform a batch prediction on the model. This cost is determined by the type of model you are deploying.
Related
My use case is related to multiclass image classification. Deployed CNN Model in production and enabled Model Monitoring for prediction drift detection only which does not require training data. It automatically gets created two buckets- analysis and predict in storage bucket. Then I created and run 1000 instances for model testing purpose(Same request 1000 times through Apache Bench) as it was prerequisite. I kept monitoring job to run for every hour and 100% sampling rate. I am not getting any output or logs in newly created buckets?
What's the error here?
Is Model Monitoring(Prediction Drift Detection) not enabled for Image Data by Vertex AI?
What steps do I need to take in order to check the Model Monitoring is working fine for Image Classification Model. We need evidence in the form of logs generated in two buckets.
Model monitoring is only supported for tabular AutoML and tabular custom-trained models at the moment. It is not support for custom-trained image classification models.
For a more proactive approach that should minimize prediction drift in image classification models, Vertex AI Team would recommend the following:
• Augmenting your data such that you have a more diverse set of samples. This set should match your business needs, and has meaningful transformations given your context. Please refer to [2] for more information about data augmentation.
• Utilizing Vertex Explainable AI to identify the features which are contributing the most to your model's classification decisions. This would help you to augment your data in a more educated manner. Please refer to [3] for more information about Vertex Explainable AI.
[1] https://cloud.google.com/vertex-ai/docs/model-monitoring/overview
[2] https://www.tensorflow.org/tutorials/images/data_augmentation
[3] https://cloud.google.com/vertex-ai/docs/explainable-ai/overview
Suppose you have a customer attribute classified as high, medium and low. Different class of customers are supposed to have different probabilities of state transition. Are you creating micro ML models to get different probabilities or you are just using relative percentages of past transitions based on historical data?
using distributions based on historical data is the thing most typically used if you have transition data... nevertheless depending on the situation you can also use prediction models to know what transition the agent will use, as long as you have independent variables that you can use to predict this...
This prediction can be done with ML models or statistical models, depending on the situation
You can also go further and use artificial intelligence if there is a constant sequential decision making on where to transition, you can do this if you want to optimize the behavior of these customers... reinforcement learning is used for that and you can use your simulation model to generate a policy for the decision making process of these customers.
According to wikipedia: Graph Database
In computing, a graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data.[1] A key concept of the system is the graph (or edge or relationship). The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes.
If a database has a GraphQL API, is this database a Graph database?
Both terms sound very similar.
They are not related. GraphQL is just an API technology that is compared to REST . Think it as another way to implement the Web API and it has nothing to do with where the data is actually stored or the storage technology behind scene. For example, it can be used as a Web API to get the data from PostgreSQL too.
But as GraphQL treats the data as an object graph, in term of API implementation, it may be more matched when working with the Graph database. It may be easier to implement as we may delegate some graph loading problem to the Graph database to solve rather than solve it by ourself.
I am currently working on a project based on graph and I am searching for an algorithm for slicing an dynamic graph. I have already done some research but most algorithms that I have found works only for a static graph. In my environment, the graph is dynamic, it means that users add/delete elements, create/delete dependences at runtime.
(In reality I am working with UML models but UML models can be also represented by typed graphs, wich are composed of typed Vertices and edges)
I also search for the terms graph fragmentation but I did not find anything. And I would like to know if exist such algorithm for slicing a dynamic graph?
[UPDATE]
Sorry for not being clear and I am updating my question.Let me first expose the context.
In MDE (Model Driven Engineering), large-scale industrial systems involve nowadays hundreds of developpers working on hundreds of models representing pars of the whole system specification. In a such context, the approach commonly adopted is to use a central repository. The solution I provide for my project (I am currently working on a research lab), is a solution which is peer-to-peer oriented, that means that every developper has his own replication of the system specification.
My main problem is how to replicate this data, the models.
For instance, imagine Alice and Bob working on this UML diagram and Alice has the whole diagram in his repository. Bob wants to have the elements {FeedOrEntry, Entry}, how can I slice this diagram UML?
I search for the terms of "model Slicing".I have found one paper which gives an approach for slicing UML Class Diagrams but the problem with this algorithm is it only works for a static graph. In our context, developpers add/update/remove elements constantly and the shared elements should be consistent with the other replicas.
Since UML Models can also be seen as a graph, I also search for the terms for "graph slicing" or "graph fragment" but I have found nothing useful.
And I would like to know if exist such algorithm for slicing a dynamic graph
If you make slicing atomic, I see no problem with using algorithm shown in paper you linked.
However, for your consistency constraints, I believe that your p2p approach is incompatible. Alternative is merge operation, but I have no idea how would that operation work. It probably, at least partially, would have to be done manually.
Sounds like maybe you need a NoSQL graph database such as Neo4J or FlockDB. They can store billions of vertexes and edges.
What about to normalize the graph to an adjacent tree model? Then you can use a DFS or BFS to slice the graph?
I am working on a flight scheduling app (disclaimer: it's for a college project, so no code answers, please). Please read this question w/ a quantum of attention before answering as it has a lot of peculiarities :(
First, some terminology issues:
You have planes and flights, and you have to pair them up. For simplicity's sake, we'll assume that a plane is free as soon as the flight using it prior lands.
Flights are seen as tasks:
They have a duration
They have dependencies
They have an expected date/time for
beginning
Planes can be seen as resources to be used by tasks (or flights, in our terminology).
Flights have a specific type of plane needed. e.g. flight 200 needs a plane of type B.
Planes obviously are of one and only one specific type, e.g., Plane Airforce One is of type C.
A "project" is the set of all the flights by an airline in a given time period.
The functionality required is:
Finding the shortest possible
duration for a said project
The earliest and latest possible
start for a task (flight)
The critical tasks, with basis on
provided data, complete with
identifiers of preceding tasks.
Automatically pair up flights and
planes, so as to get all flights
paired up with a plane. (Note: the
duration of flights is fixed)
Get a Gantt diagram with the projects
scheduling, in which all flights
begin as early as possible, showing
all previously referred data
graphically (dependencies, time info,
etc.)
So the questions is: How in the world do I achieve this? Particularly:
We are required to use a graph.
What do the graph's edges and nodes
respectively symbolise?
Are we required to discard tasks to
achieve the critical tasks set?
If you could also recommend some algorithms for us to look up, that'd be great.
Here some suggestions.
In principle you can have a graph where every node is a flight and there is an edge from flight A to flight B if B depends on A, i.e. B can't take off before A has landed. You can use this dependency graph to calculate the shortest possible duration for the project --- find the path through the graph that has maximum duration when you add the durations of all the flights on the path together. This is the "critical path" of your project.
However, the fact that you need to pair with planes makes it more difficult, esp. as I guess it is assumed that the planes are not allowed to fly without passengers, i.e. a plane must take off from the same city where it landed last.
If you have an excessive number of planes, allocating them to the flights can be most likely easily with a combinatorial optimization algorithm like simulated annealing. If the plan is very tight, i.e. you don't have excess planes, it could be a hard problem.
To set the actual take-off times for your flights, you can for example formulate the allowed schedules as a linear programming problem, or as a semi-definite / quadratic programming problem.
Here some references:
http://en.wikipedia.org/wiki/Simulated_annealing
http://en.wikipedia.org/wiki/Linear_programming
http://en.wikipedia.org/wiki/Quadratic_programming
http://en.wikipedia.org/wiki/Gradient_descent
http://en.wikipedia.org/wiki/Critical_path_method
Start with drawing out a domain model (class diagram) and make a clear separation in your mind between:
planning-immutable facts: PlaneType, Plane, Flight, FlightBeforeFlightConstraint, ...
planning variables: PlaneToFlightAssignment
Wrap all those instances in that Project class (= a Solution).
Then define a score function (AKA fitness function) on such a Solution. For example, if there are 2 PlaneToFlightAssignments which are not ok with a FlightBeforeFlightConstraint (= flight dependency), then lower the score.
Then it's just a matter for finding the Solution with the best score, by changing the PlaneToFlightAssignment instances. There are several algorithms you can use to find that best solution. If your data set is really really small (say 10 planes), you might be able to use brute force.