In one of my last posts, I wrote about the fact that Cloud is more PaaS/FaaS then IaaS already. In fact, IaaS doesn’t bring much value at all over traditional architectures. There still are some advantages, but they remain limited. If you want to go for a future-proove archtiecture, Analytics needs to be serverless analytics. In this article, I will explain why.

What is serverless analytics?

Just as similar with serverless technologies, serverless analytics also follows the same concept. Basically, the idea behind that is to significantly reduce the work on infrastructure and servers. Modern environments allow us to “only” bring the code and the cloud provider takes care about everything else. This is basically the dream of every developer. Do you know the statement “it works on my machine”? With serverless, this is ways easier. You only need to focus on the app itself, without any requirements on operating system and stack. Also, execution is task- or consumption-based. This means that eventually you only pay for what is used. If your service isn’t utilised, you don’t pay for it. You can also achieve this with IaaS, but with serverless it is part of the concept and not something you need to enable on.

With Analytics, we now also march towards the serverless approach. But why only now? Serverless is around for already some time? Well, if we look at the data analytics community, it always used to be a bit slower than the overall industry. When most tech stacks already migrated to the Cloud, analytics projects were still carried out with large Hadoop installations in the local data center. Also back then, the Cloud was already superior. However, a lot of people still insisted on it. Now, data analytics workloads are moving more and more into the Cloud.

What are the components of Serverless Analytics?

  • Data Integration Tools: Most cloud providers provide easy to use tools to integrate data from different sources. A GUI makes the use of this easier.
  • Data Governance: Data Catalogs and quality management tools are also often parts of any solution. This enables a ways better integration.
  • Different Storage options: Basically, for serverless analytics, storage must always be decoupled from the analytics layer. Normally, there are different databases available. But most of the data is stored on object stores. Real-time data is consumed via a real-time engine.
  • Data Science Labs: Data Scientists need to experiment with data. Major cloud providers have data science labs available, which enable this sort of work.
  • API for integration: With the use of APIs, it is possible to bring back the results into production- or decision-making systems.

How is it different to Kubernetes or Docker?

At the moment, there is also a big discussion if Kubernetes or Docker will solve this job with Analytics. However, this again requires the usage of servers and thus increases the maintenance at some point. All cloud providers have different Kubernetes and Docker solutions available, which allows an easy migration later on. However, I would suggest to go immediately for serverless solutions and avoid the use of containers if avoidable.

What are the financial benefits?

It is challenging to measure the benefits. If the only comparison is price, then it is probably not the best way to do so. Serverless Analytics will greatly reduce the cost of maintaining your stack – this will go close to zero! The only thing you need to focus on from now on is your application(s) – and they should eventually produce value. Also, it is easier to measure IT on the business impact. You get a bill for the applications, not for maintaining a stack. If you run an analysis, you will get a quote for it and the business impact may or may not justify the investment.

If you want to learn more about Serverless Analytics, I can recommend you this tutorial. (Disclaimer: I am not affiliated with Udemy!)

Recurrent Neural Network

In the last two posts we introduced the core concepts of Deep Learning, Feedforward Neural Network and Convolutional Neural Network. In this post, we will have a look at two other popular deep learning techniques: Recurrent Neural Network and Long Short-Term Memory.

Recurrent Neural Network

The main difference to the previously introduced Networks is that the Recurrent Neural Network provides a feedback loop to the previous neuron. This architecture makes it possible to remember important information about the input the network received and takes the learning into consideration along with the next input. RNNs work very well with sequential data such as sound, time series (sensor) data or written natural languages.

The advantage of a RNN over a feedforward network is that the RNN can remember the output and use the output to predict the next element in a series, while a feedforward network is not able to fed the output back to the network. Real-time gesture tracking in videos is another important use-case for RNNs.

A Recurrent Neural Network
A Recurrent Neural Network

Long Short-Term Memory

A usual RNN has a short-term memory, which is already great at some aspect. However, there are requirenments for more advanced memory functionality. Long Short-Term Memory is solving this problem. The two Austrian researchers Josef Hochreiter and Jürgen Schmidhuber introduced LSTM. LSTMs enable RNNs to remember inputs over a long period of time. Therefore, LSTMs are used in combination with RNNs for sequential data which have long time lags in between.

LSTM learns over time on which information is relevant and what information isn’t relevant. This is done by assigning weights to information. This information is then assigned to three different gates within the LSTM: the input gate, the output gate and the “forget” gate.

This tutorial is part of the Machine Learning Tutorial. You can learn more about Machine Learning by going through this tutorial. On Cloudvane, there are many more tutorials about (Big) Data, Data Science and alike, read about them in the Big Data Tutorials here. If you look for great datasets to play with, I would recommend you Kaggle.

Working with data is a complex thing and not done in some days. It is rather a matter of several sequential steps that lead to a final output. In this post, I present the data science process for project execution in data science.

What is the Data Science Process?

Data Science is often mainly consisting of data wrangling and feature engineering, before one can go to the exciting stuff. Since data science is often very exploratory, processes didn’t much evolve around it (yet). In the data science process, I group the process in three main steps that have several sub-steps in it. Let’s first start with the three main steps:

  • Data Acquisition
  • Feature Engineering and Selection
  • Model Training and Extraction

Each of the different process main steps contains some sub-steps. I will describe them a bit in detail now.

Step 1: Data Acquistion

Data Engineering is the main ingredient in this step. After a business question was formulated, it is necessary to now look for the data. In an ideal setup, you would already have a data catalog in your enterprise. If not, you might need to ask several people until you have found the right place to dig deeper into it.

First of all, you need to acquire the data. They might be internal sources but you might also combine them with external sources. In this context, you might want to read about the different data sources you need. Once you are done with having a first look at the data, it is necessary to integrate the data.

Data integration is often perceived as a challenging task. You need to setup a new environment to store the data or you need to extend an existing schema. A common practise is to build a data science lab. A data science lab should be an easy platform for data engineers and data scientists to work with data. A best practise for that is to use a prepared environment in the cloud for it.

After integrating the data, there comes the heavy part of cleaning the data. In most cases, Data is very nasty and thus needs a lot of cleaning with it. This is also mainly carried out by data engineers alongside with data analysts in a company. Once you are done with the data acquisition part of it, you can move on with the feature engineering and selection step.

Typically, this first process step can be very painful and long-lasting. It depends on different factors of an enterprise, such as the data quality itself, the availability of a data catalog and corresponding metadata descriptions. If your maturity in all these items is very high, it can take some days to a week, but in average it is rather 2 to 4 weeks of work.

Step 2: Feature Engineering and Selection

In the next step we start with a very important step in the Data Science process: Feature Engineering. Features are very important for Machine Learning and have a huge impact on the quality of the predictions. With Feature Engineering, you have to understand the domain you are in and what to use with it. One need to understand what data to use and for what reason.

After the feature engineering itself, it is necessary to select the relevant features with the feature selection. A common mistake is the overfitting of a model, or also called “feature explosion”. It happens often that too many features are created and thus the predictions aren’t accurate anymore. Therefore, it is very important to select only those features that are relevant to the use-case and thus bring some significance.

Another important step is the development of the cross-validation structure. This is necessary to check how the model will perform in practice. The cross-validation will measure the performance of your model and give you insights on how to use it. Next after that is the Hyperparameter tuning. Hyperparameters are fine-tuned to improve the prediction of your model.

This process is now carried out mainly by Data Scientists, but still supported by data engineers. The next and final step in the data science process is Model Training and Extraction.

Step 3: Model Training and Extraction

The last step in the process is the model training and extraction. In this step, the algorithm(s) for the model prediction are selected and compared to each other. In order to ease up work here, it is necessary to put all your process into a pipeline. (Note: I will explain the concept of the pipeline in a later post). After the training is done, you can go into the predictions itself and bring the model into production.

The following illustration outlines the now presented process:

This image describes the Data Science process in it's three steps: Data acquisition, Feature Engineering and Selection, Model Training and Extraction
The Data Science Process

The Data Science process itself can easily be carried out in a Scrum or Kanban approach, depending on your favourite management style. For instance, you could have each of the 3 process steps as sprints. The first sprint “Data Acquisition” might last longer than the other sprints or you could even break the first one into several sprints. For Agile Data Science, I can recommend you reading this post.