The tf.data.Dataset API is a very efficient pipeline builder. Time Series Tasks can be a bit tricky to implement properly. In this article, we are going to dive deep into common tasks:
With the dataset api this is simple to do. Assume the following configuration. input feature is
a and label is
Each row can be described by a tensor shaped
(2,) . …
Named inputs and outputs are essentially dictionaries with string keys and tensor values.
Most machine learning pipelines read data from a structured source ( database, CSV files/ Pandas Dataframes , TF Records), perform feature selection, cleaning, (and possibly) preprocessing, passing a raw multidimensional array (tensor) to a model along with another tensor representing the correct prediction for each input sample.
Reorder or rename input features in production? → Useless results or the client — side breaks in production
Absent Features? Missing Data? Bad output value interpretation? Mixing up integer indices by mistake? → Useless Results or the client — side breaks in…
You all know what this game is about. This is the best service-offline-sorry page in the world. People have made simple bots that time the dino’s jump to beat the game to reinforcement learning agents with CNN state encoders.
This thing is hard to play.
localexecution mode and a
deploymentexecution mode. This ensures the creation of 2 separate running configurations, with the first being used for local development and end-to-end testing and the second one used for running in the cloud.
Reuse codeacross pipeline variants if it makes sense to do so
CLI interfacefor executing pipelines with different
A correct implementation also ensures that tests are easy to incorporate in your workflow.
In this article we will demonstrate how to run a TFX pipeline both locally and on a Kubeflow Pipelines installation with minimum hassle. …
If this production e2e ML pipelines thing seems new to you, please read the TFX guide first.
On the other hand, if you’ve used TFX before, or planning to deploy a machine learning model, you’re in the right place.
The current version of ML Metadata by the time this article is being published is v0.22 (tfx is also v0.22). The API is mature enough to allow for mainstream usage and deployment on the public cloud. Tensorflow Extended uses this extensively for component — component communication, lineage tracking, and other tasks.
We are going to run a very simple pipeline that is just going to generate statistics and the schema for a sample csv of the famous Chicago Taxi Trips dataset. …
The fully end to end example that tensorflow extended provides by running
tfx template copy taxi $target-dir produces 17 files scattered in 5 directories. If you are looking for a smaller, simpler and self contained example that actually runs on the cloud and not locally, this is what you are looking for. Cloud services setup is also mentioned here.
We are going to generate statistics and a schema for the Chicago taxi trips csv dataset that you can find by running the
tfx template copy taxi command under the
Generated artifacts such as data statistics or the schema are going to be viewed from a jupyter notebook, by connecting to the ML Metadata store or just by downloading artifacts from simple file/binary storage. …
Hi there. I’m Theodoros, a Computer Engineering Student here in Greece and I love deep learning.
Welcome to the Understanding Machine Learning in Production. In this article we are going to go over what the main objective of this series is all about and a rough outline of what is going to be covered.
I’m creating these articles because I feel that although the tensorflow ecosystem and high level APIs like keras along with all these free (and non free) tools and services that big companies provide online, like the famous google colab, lower entry barriers to machine learning, the whole ecosystem on the other hand has got so big and it is hard to get a grasp of it. …
Apache Beam got incubated at Google. It’s an evolution of MapReduce in some sense. You see, Map Reduce has changed the way big data processing works. There are both open source solutions supporting it as (Hadoop, Spark) and cloud solutions provided as a service (GCP Dataflow).
Technologies have been made specifically for different workloads. Apache Flink for strictly stream processing. Spark for batch loads.
Beam is not an execution engine like Spark or Dataflow. It’s an API to define streaming or Batch processing workloads that run as a Directed Acyclic Graph independent of the execution engine and programming language. …
This is a quick review on the various data sources and formats that are commonly used or recommended in the tensorflow ecosystem.
The Apache Beam Pipeline runs each component as a different task that receives inputs and outputs, as a standalone workload. It can be thought as a Directed Acyclic Graph.