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That makes rolling new versions much more complex socially than with conventional software. Quite frankly, because of the wider gap in skills between data scientists and software or ops engineers, we need to allow for the fact that models will typically not be implemented with as much software engineering rigor as we might like. We also must allow for the fact that the framework is going to need to provide for a lot more data rigor than most software does in order to satisfy the data science part of the team.
These problems could be addressed by building a new kind of load balancer and depending heavily on the service discovery features of frameworks such as Kubernetes, but there is a much simpler path. That simpler path is to use a stream-first architecture such as the rendezvous architecture.
As What Matters in Model Management explains, message streams in the style of Apache Kafka, including MapR Streams, are an ideal construct here because stream consumers control what and when they listen for data pull style.
That completely sidesteps the problem of service discovery and avoids the problem of making sure all request sources send all transactions to all models. Receiving requests via a stream makes it easy to distribute a request to all live models, but we need more machinery to get responses back to the source of requests. But we immediately run into the question that if we put the requests into a stream, how will the results come back?
On the other hand, if we send the requests into a stream and evaluate those requests with lots of models, the insertion into the input stream will complete before any model has even looked at the request. These additional challenges motivate the rendezvous design. Rendezvous Style We can solve these problems with two simple actions.
First, we can put a return address into each request. Second, we can add a process known as a rendezvous server that selects which result to return for each request. The return address specifies how a selected result can be returned to the source of the request. Even better, it can be the name of a message stream and a topic.
Whatever works best for you is what it needs to be. Note Using a rendezvous style works only if the streaming and processing elements you are using are compatible with your latency requirements. For persistent message queues, such as Kafka and MapR Streams, and for processing frameworks, such as Apache Flink or even just raw Java, a rendezvous architecture will likely work well—down to around single millisecond latencies.
Conversely, as of this writing, microbatch frameworks such as Apache Spark Streaming will just barely be able to handle latencies as low as single digit seconds not milliseconds. That might be acceptable, but often it will not be. At the other extreme, if you need to go faster than a few milliseconds, you might need to use nonpersistent, in-memory streaming technologies. The rendezvous architecture will still apply. Note The key distinguishing feature in a rendezvous architecture is how the rendezvous server reads all of the requests as well as all of the results from all of the models and brings them back together.
In the system shown, we assume that the return address consists of a topic and request identifier and that the rendezvous server should write the results to a well-known stream with the specified topic.
The result should contain the request identifier to the process sending the request in the first place since it has the potential to send overlapping requests. The core rendezvous design. There are additional nuances, but this is the essential shape of the architecture. Internally, the rendezvous server works by maintaining a mailbox for each request it sees in the input stream.
As each of the models report results into the scores stream, the rendezvous server reads these results and inserts them into the corresponding mailbox. Based on the amount of time that has passed, the priority of each model and possibly even a random number, the rendezvous server eventually chooses a result for each pending mailbox and packages that result to be sent as a response to the return address in the original request.
Related to this, the rendezvous can make guarantees about returning results that the individual models cannot make. You can, for instance, define a policy that specifies how long to wait for the output of a preferred model.
If at least one of the models is very simple and reliable, albeit a bit less accurate, this simple model can be used as a backstop answer so that if more sophisticated models take too long or fail entirely, we can still produce some kind of answer before a deadline.
Message Contents The messages between the components in a rendezvous architecture are mostly what you would expect, with conventional elements like timestamp, request id, and request or response contents, but there are some message elements that might surprise you on first examination. The messages in the system need to satisfy multiple kinds of goals that are focused around operations, good software engineering and data science.
If you look at the messages from just one of these points of view, some elements of the messages may strike you as unnecessary. All of the messages include a timestamp, message identifier, provenance, and diagnostics components. The timestamp should be in milliseconds, and the message identifier should be long enough to be confident that it is unique.
The one shown here is bits long.
The provenance section provides a history of the processing elements, including release version, that have touched this message. It also can contain information about the source characteristics of the request in case we want to drill down on aggregate metrics. This is particularly important when analyzing the performance and impact of different versions of components or different sources of requests. Including the provenance information also allows limited trace diagnostics to be returned to the originator of the request without having to look up any information in log files or tables.
The amount of information kept in the provenance section should be relatively limited by default to the information that you really need to return to the original caller. You can increase the level of detail by setting parameters in the diagnostics section.
If tracing is enabled for a request, the provenance section will contain the trace parent identifier to allow latency traces to be knit back together when you want to analyze what happened during a particular query. Depending on your query rates, the fraction of queries that have latency tracing turned on will vary.
It might be all queries or it might be a tiny fraction.
The diagnostics section contains flags that can override various environmental settings. These overrides can force more logging or change the fallback schedule that the rendezvous server uses to select different model outputs to be returned to the original requestor.
If desired, you can even use the diagnostics element to do failure injection. The faults injected could include delaying a model result or simulating a fault in a system component or model. Fault injection is typically only allowed in QA systems for obvious reasons.
Request Specific Fields Beyond the common fields, every request messages includes a return address and the model inputs. These inputs are augmented with the external state information and are given identically to every model. Note that some model inputs such as images or videos can be too large or complex to carry in the request directly. In such cases, a reference to the input can be passed instead of the actual input data. The reference is often a filename if you have a distributed file system with a global namespace, or an object reference if you are using a system like S3.
The return address can be something as simple as topic name in a well known message stream. Using a stream to deliver results has lots of advantages, such as automatically logging the delivery of results so it is generally preferred over mechanisms such as REST endpoints.
Output Specific Fields The output from the models consists of score messages that include the original request identifier as well as a new message identifier and the model outputs themselves. The rendezvous server uses the original request identifier to collect together results for a request in anticipation of returning a response. The result message has whatever result is selected by the rendezvous server and very little else other than diagnostic and provenance data. The model outputs can have many forms depending on the details of how the model actually works.
The cost of moving messages in inefficient formats including serializing and deserializing data is typically massively overshadowed by the computations involved in evaluating a model. That being said, there is huge value in consensus about messaging formats. It is far better to use a single suboptimal format everywhere than to split your data teams into factions based on format.
Pick a format that everybody likes, or go with a format that somebody else already picked. Either way, building consensus is the major consideration and dominates anything but massive technical considerations. Stateful Models The basic rendezvous architecture allows for major improvements in the management of models that are pure functions, that is, functions that always give the same output if given the same input.
Some models are like that. Machine translation and speech recognition systems are similar. Only deploying a new model changes the results.
Other models are definitely not stateless. In general, we define a stateful model as any model whose output in response to a request cannot be computed just from that one request. Card velocity is a great example of internal state. Many credit card fraud models look at where and when recent transactions happened. This allows them to determine how fast the card must have moved to get from one transaction to the next.
This card velocity can help detect cloned cards. Stateful data like this can depend on a single entity like a user, website visitor, or card holder, or it can be a collective value such as the number of transactions in the last five minutes or the expected number of requests estimated from recent rates.
These are all examples of internal state because every model could compute its own version of internal state from the sequence of requests completely independently of any other model.
External state looks very different.
For instance, the current temperature in the location where a user is located is an example of external state. Outputs from other models are also commonly treated as external state. In a rendezvous model, it is a best practice to add all external state to the requests that are sent to all models. This allows all external state to be recorded. For internal state, on the other hand, you have a choice. You can compute the internal state variables as if they were external state and add them to the requests for all models.
This is good if multiple models are likely to use the same variables. Alternatively, you can have each model compute its own internal state. This is good if the computation of internal state is liable to change.
With stateful models, all dependence on external state should be positioned in the main rendezvous flow so that all models get exactly the same state. The point here is that all external state computation should be external to all models.
Forms of internal state that have stable and commonly used definitions can be computed and shared in the same way as external state or not, according to preference. As we have mentioned, the key rationale for dealing with these two kinds of state in this way is reproducibility.
Dealing with state as described here means that we can reproduce the behavior of any model by using only the data that the decoy model has recorded and nothing else. The idea of having such a decoy model that does nothing but archive common inputs is described more fully in the next section.
The Decoy Model Nothing is ever quite as real as real data. As a result, recording live input data is extraordinarily helpful for developing and evaluating machine learning models.
The fact is, however, that all kinds of distressingly common events can conspire to make reconstructed input data different from the real thing. Just as unit tests and integration tests in software engineering are used to isolate different kinds of error to allow easier debugging, recording real data can isolate data errors from modeling errors.
The simplest way to ensure that you are seeing exactly what the models are seeing is to add what is called a decoy model into your system. Instead, it just archives the inputs that it sees. In a rendezvous architecture, this is really easy, and it is also really easy to be certain that the decoy records exactly what other models are seeing because the decoy reads from the same input stream. It just archives inputs that all models see, including external state. A decoy model is absolutely crucial when the model inputs contain external state information from data sources such as a user profile database.
When this happens, this external data should be added directly into the model inputs using a preprocessing step common to all models, as described in the previous section. Bring Efficiency and Automation to Enclosure Customization. Reducing labor cost and improving delivery times on enclosure modifications is key for any successful integrator or OEM.
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Learn how to move your infrastructure to the Edge for tighter security and lower operational costs in the Rittal This page eBook is your guide to ball screws and details how to get the most out of them for industrial motion control. Sign up for the Machine Design Today newsletter. Sign Up. Leave this field blank. This eBook will cover six major challenges facing modern designers, and will introduce five companies that are reaping the benefits of innovating their design process.
Focus On: Dealing With Vibration in Aviation Applications.
For design and safety engineers, vibration in aviation vehicles can cause severe headaches.