Author: Amandeep Singh, Machine Learning Engineer
Deploying a Machine Learning model in production can be challenging. So challenging, in fact, that 87% of data science projects don’t even make it to production. Problems that teams face include a lack of collaboration across teams; a lack of standardisation of processes; and a lack of visibility across the entire end-to-end workflow.
Over the last couple of years, a set of practices has emerged specifically to combat these issues. This is known as MLOps, which combines Machine Learning, DevOps, and Data Engineering practices to deploy and maintain ML systems reliably and efficiently.
This guide is the first of five in a series to help you choose the right tool for every step of your ML Journey to implement MLOps practices on Google Cloud Platform. Google Cloud Platform is an excellent environment for MLOps due to its presence of managed resources such as BigQuery, AI Platform and Cloud Run. This allows Data Scientists to focus more on the data and machine learning and not in solving infrastructure or operations problems. Each of our upcoming blogs will compare two tools for a crucial section of the ML Journey, highlighting some of the stages that are often overlooked.
This blog will be covering Data Transformation, which involves changing the structure or format of the data to another so it is Machine Learning ready. More specifically we will be taking a look at Data Preprocessing and Feature Engineering on Google Cloud Platform, with a deep dive into two tools, BigQuery and Cloud Dataflow, more explicitly using TensorFlow Transform for preprocessing. In the image below, we have a diagram of the end-to-end Machine Learning workflow.
Data Preprocessing is the process of transforming raw data into more meaningful data which can be understood by models. Raw data or real-world data is often riddled with errors, incomplete and inconsistent. To transform the raw data, we can apply multiple preprocessing steps that will help us train our models such as Normalization, Feature Construction and Data Cleansing.
Feature Engineering is the process of extracting and creating features from the raw dataset that will most impact the performance of the model. This is done by having in-depth knowledge of your raw data and the algorithm you will be using for training. By doing this, you remove the features that are not relevant and don’t contribute to your model performance, thus making your hypothesis space smaller, allowing your model to learn faster with ease. This step also includes the splitting of the data in training, test and validation for training.
Now that we know what both Data Preprocessing and Data Featuring is, let’s see what is the best way for you to complete these tasks on Google Cloud Platform. As mentioned above, we will be looking at two different tools; BigQuery, and Cloud Dataflow with the usage of TensorFlow Transform. To compare the following tools, we will mainly look at the three different types of preprocessing granularity explained below.
BigQuery (see more here) is a scalable, serverless and fully-managed data warehouse, developed by Google Cloud, which allows you to perform super-fast SQL queries over petabytes of data. It is offered as a Platform as a Service (PaaS) and is built upon Google infrastructure. It also has many other features such as apply machine learning models to your data through SQL and to allow streaming inserts straight into your data warehouse.
AVG, but it doesn’t cover everything. For some statistical analysis, you may be able to solve it by creating custom User Defined Functions, but that comes at the cost of implementing your logic and ensuring that it is correct and works the way you want it to.
We recently chose BigQuery for a customer, who is an industry leader in payment processing, and we were tasked with showing them the benefits of Google Cloud Platform for their machine learning model development. We chose BigQuery for data transformation for multiple reasons. Firstly, the client required aggregated windows to track user activity over a period of time, and BigQuery handles this task with ease. Second, we wanted to utilise User Defined Functions so that we could do additional statistical data transformations. Third, we only wanted to use one tool for the whole transformation, including cleaning, all transformations and splitting of the data so we could have it ready for the machine learning model. Finally, we didn’t want to manage resources and have to keep track of them. With BigQuery being fully-managed, we were able to focus solely on the SQL code.
Cloud Dataflow (see more here) is a managed service with the Google Cloud Platform ecosystem to allow you to execute Apache Beam pipelines at scale. It provides the ability to process data in batches or real-time efficiently and also be cost-effective. It has multiple SDKs in which pipelines can be created, for example, the Python and Java SDK. Now even though we can perform preprocessing of data using Apache Beam, and have it managed with Cloud Dataflow, we will be talking about using the TensorFlow library, TensorFlow Transform (tf.Transform) within Dataflow.
Tf.Transform is a library specifically developed to preprocess data. There are two main outputs when the library is applied: the first is the training data, ready to be read by your machine learning model; the second is a graph consisting of all your logic applied to the training data, which can then be used to apply to your prediction data. This allows you to save time and not have to duplicate your preprocessing logic when it comes to predictions.
PCA. It even has some complicated, machine learning ready processes such as
compute_and_apply_vocabulary, which generates a vocabulary for a categorical feature and maps it to an integer; and
tfidf, which maps the terms in x to their term frequency multiplied by the inverse document frequency.
TensorFlow Transform was recently used for data transformation for a client for their MLOps solution. The client, a leader in the telecommunications industry, wanted to build a common framework for development to production of machine learning models. They used TFX end-to-end for their NLP use case to enforce best practice on production. Therefore TensorFlow Transform was used at this part of the process. TensorFlow Transform also allows you to avoid code duplication, given that the transformations you do during training are the same you are doing during prediction. As mentioned above, tf.Transform produces a graph which records all the calculations done and can be applied to the prediction data. This was done to avoid training skew.
There are many differences between the two tools and a lot of things you need to first ask yourself before making your choice. The most significant advantage of choosing BigQuery over tf.Transform is that BigQuery is serverless. On the other hand, with tf.Transform, which runs on Dataflow, you need to configure everything yourself such as the clusters, workers and RAM. For BigQuery all you need to worry about is the SQL code that you are writing, not about managing your python code and the library versions. On top of that, tf.Transform is still under development so that some functions may be a little buggy. If you want something reliable, then I would recommend you go with BigQuery. Finally, if you would like to do Window Aggregations, then BigQuery is a clear choice here as tf.Transform does not support it yet.
For extra reference on data transformation and preprocessing, you may also want to have a read of this fantastic article written by the folks over at Google.
We hope you enjoyed our first MLOps tool comparison blog and that it makes choosing the transformation tool for your task easier. Check out our next blog, comparing Kubeflow with Composer for orchestration!
Know exactly where and how to start your AI journey with Datatonic’s
three-week AI Innovation Jumpstart *.
* Duration dependent on data complexity and use case chosen for POC model
With your own data sets, convince your business of the value of migrating your data warehouse, data lake and/or streaming platform to the cloud in four weeks.
With your own data, see how Looker can modernise your BI needs
with Datatonic’s two-week Showcase.