Machine Learning Models in Python – How long does it take

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We keep hearing from people about all the computing resources needed for machine learning. Sometimes it can put people off from trying it as they will think I don’t have those kind of resources.

This is another blog post in my series on ‘How long does it take to create a machine learning model?

Check out my previous blog post that used data sets containing 72K, 210K, 660K, 2M and 10M records.

There was some surprising results in those these.

In this test, I’ll be using Python and SciKitLearn package to create models using the same algorithms. There are a few things to keep in mind. Firstly, although they maybe based on the same algorithms, the actual implementation of them will be different in each environment (SQL vs Python).

With using Python for machine learning, one of the challenges we have is getting access to the data. Assuming the data lives in a Database then time is needed to extract that data to the local Python environment. Secondly, when using Python you will be using a computer with significantly less computing resources than a Database server. In this test I used my laptop (MacBook Pro). Thirdly, when extracting the data from the database, what method should be used.

I’ve addressed these below and the Oracle Database I used was the DBaaS I used in my first experiment. This is a Database hosted on Oracle Cloud.

Extracting Data to CSV File
This kind of depends on how you do this. There are hundreds of possibilities available to you, but if you are working with an Oracle Database you will probably be using SQL Developer. I used the ‘export’ option to create a CSV file for each of the data sets. The following table shows how long it took for each data set.

As you can see this is an incredibly slow way of exporting this data. Like I said, there are quicker ways of doing this.

After downloading the data sets, the next step is to see how load it takes to load these CSV files into a pandas data frame in Python. The following table show the timings in seconds.

You can see that Python is very efficient at loading these data sets into a pandas data frame in my Python environment.

Extracting Data using cx_Oracle Python package
As I’ll be using Python to create the models and the data exists in an Oracle Database (on Oracle Cloud), I can use the cx_Oracle package to download the data sets into my Python environment. After using the cx_Oracle package to download the data I then converted it into a pandas data frame.

You can see that using cx_Oracle to download the data is a very efficient way of accessing the data.  But if the data already exists in CSV files, then the previous method would be quicker to use.

I had the array fetch size set to 10,000.  I also experimented with smaller and larger numbers for the array fetch size, but 10,000 seemed to give a quickest results.

How long to create Machine Learning Models in Python
Now we get onto checking out the timings of how long it takes to create a number of machine learning models using different algorithms and using the default settings. The algorithms include Naive Bayes, Decision Tree, GLM, SVM and Neural Networks.

I had to stop including SVM in the tests as it was taking way too long to run. For example I killed the SVM model build on the 210K data set after it was running for 5 hours.

The Neural Network models created had 3 hidden layers.

In addition to creating the models, there was some minor data preparation steps performed including factorizing, normalization and one-hot-coding. This data preparation would be comparable to the automatic data preparation steps performed by Oracle, although Oracle Automatic Data Preparation does a bit of extra work.

At the point I would encourage you to look back at my previous blog posts on timings using Oracle DBaaS and ADW.  You will see that Python, in these test cases, was quicker at creating the machine learning models. But with Python the data needed to be extracted from the database and that can take time!

A separate consideration is being able to deploy the models. The time it takes to build models is perhaps not the main consideration. You need to consider ease of deployment and use of the models.


Machine Learning on Oracle Autonomous Data Warehouse

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Last week I wrote a blog post about how long it took to create machine learning models on Oracle Database Cloud service. There was some impressive results and some surprising results too.

I decided to try out the exact same tests, using the exact same data on the Oracle Autonomous Data Warehouse Cloud service (ADW).

When creating the ADW service I took the basic configuration and didn’t change anything. The inbuilt machine learning for the Autonomous service will magically workout my needs and make the necessary adjustments, Right? It can handle any data volume and any data processing requirements, Right?

Here are the results.


* You will notice that there is no time given for creating a SVM model for the 10M record data set. After waiting for 4 hours I got bored and gave up waiting (I actually did this three time to make sure it wasn’t a once off)

[I also had a 50M record data set. I just didn’t waste time trying that.]

[Neural Networks algorithm hasn’t been ported onto ADW at this point in time]

If you look back at the results from using the DBaaS you will see it was significantly quicker than the ADW. (for some it would be quicker using Python on my laptop)

Before you believe the hype, go test it yourself and make sure it measures up.

I re-ran my test cases over a number of days to see if the machine learning aspect of the Autonomous kicked in to learn from the processing and make any performance improvements. Sadly the results were basically the same or slightly slower. Disappointing.

When some tells you, you should be using this, ask them have they actually used and tested it themselves. And more importantly, don’t believe them. Go test it yourself.


How long does it take to build a Machine Learning model using Oracle Cloud

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Everyday someone talks about the the processing power needed for Machine Learning, and the vast computing needed for these tasks. It has become evident that most of these people have never created a machine learning model. Never. But like to make up stuff and try to make themselves look like an expert, or as I and others like to call them a “fake expert”.

When you question these “fake experts” about this topic, they huff and puff about lots of things and never answer the question or try to claim it is so difficult, you simply don’t understand.

Having worked in the area of machine learning for a very very long time, I’ve never really had performance issues with creating models. Yes most of the time I’ve been able to use my laptop. Yes my laptop to build models large models. In a couple of these my laptop couldn’t cope and I moved onto a server.

But over the past few years we keep hearing about using cloud services for machine learning. If you are doing machine learning you need to computing capabilities that are available with cloud services.

So, the results below show the results of building machine learning models, using different algorithms, with different sizes of data sets.

For this test, I used a basic cloud service. Well maybe it isn’t basic, but for others they will consider it very basic with very little compute involved.

I used an Oracle Cloud DBaaS for this experiment. I selected an Oracle 18c Extreme edition cloud service. This comes with the in-database machine learning option. This comes with 1 OCPUs, 7.5G Memory and 170GB storage. This is the basic configuration.

Next I created data sets with different sizes. These were based on one particular data set, as this ensures that as the data set size increases, the same kind of data and processing required remained consistent, instead of using completely different data sets.

The data set consisted of the following number of records, 72K, 660K, 210K, 2M, 10M and 50M.

I then created machine learning models using Decisions Tree, Naive Bayes, Support Vector Machine, Generaliszd Linear Models (GLM) and Neural Networks. Yes it was a typical classification problem.

The following table below shows the length of time in seconds to build the models. All data preparations etc was done prior to this.

Note: It should be noted that Automatic Data Preparation was turned on for these algorithms. This performed additional algorithm specific data preparation for each model. That means the times given in the following tables is for some data preparation time and for building the models.


Converting the above table into minutes.


It is clear that the Neural Network model takes a lot longer to build than all the other algorithms. In this test the Neural Network model had only one hidden layer.
When we chart the build timings, leaving out Neural Networks, we get.
We can see Naive Bayes, Decision Tree, GLM and SVM algorithms have very similar model build timings, but as the data volumes increase the Decision Tree algorithm become less efficient.
Overall it doesn’t take a long time to build models. In a way it is a very trivial task!
I mentioned at the start of this post I had created a data set of 50M records. Unfortunately I wasn’t able to get models build for this data set using this cloud instance. It used used so much TEMP tablespace that the file volumes on my cloud instance ran out of space!
I suppose if I wanted to go bigger with my data, I needed a bigger boat!
I haven’t included any timings for model scoring using these models. Why? the scored data is immediately returned event for large the largest data sets.


Changing the markers for Google Maps and centering map

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In some recent work, I’ve been integrating with Google Maps and some of the other Google API’s a lot. This post is just a reminder for myself on how to change the format, colour, and other properties of the map pointers.

cluster_0_gmap = gmaps.symbol_layer(
    map_locations_c0, fill_color='red',
    stroke_color='red', scale=5 )

cluster_1_gmap = gmaps.symbol_layer(
    map_locations_c1, fill_color='green',
    stroke_color='green', scale=5 )

cluster_2_gmap = gmaps.symbol_layer(
    map_locations_c2, fill_color='purple',
    stroke_color='purple', scale=5 )

cluster_3_gmap = gmaps.symbol_layer(
    map_locations_c3, fill_color='blue',
    stroke_color='blue', scale=5 )

And now for the map initial settings, centred on Athlone town in the middle of Ireland.

fig = gmaps.figure()

figure_layout = {
'width': '950px',
'height': '730px',
'border': '1px solid black',
'padding': '1px',
'margin': '0 auto 0 auto'

ireland_coord = (53.42, -7.94)
fig=gmaps.figure(center=ireland_coord, zoom_level=7.5, layout=figure_layout)



Understanding, Building and Using Neural Network Models using Oracle 18c

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I recently had an article published on Oracle Developer Community website about Understanding, Building and Using Neural Network Machine Learning Models with Oracle 18c. I’ve also had a 2 Minute Tech Tip (2MTT) video about this topic and article. Oracle 18c Database brings prominent new machine learning algorithms, including Neural Networks and Random Forests. While many articles are available on machine learning, most of them concentrate on how to build a model. Very few talk about how to use these new algorithms in your applications to score or label new data. This article will explain how Neural Networks work, how to build a Neural Network in Oracle Database, and how to use the model to score or label new data. What are Neural Networks? Over the past couple of years, Neural Networks have attracted a lot of attention thanks to their ability to efficiently find patterns in data—traditional transactional data, as well as images, sound, streaming data, etc. But for some implementations, Neural Networks can require a lot of additional computing resources due to the complexity of the many hidden layers within the network. Figure 1 gives a very simple representation of a Neural Network with one hidden layer. All the inputs are connected to a neuron in the hidden layer (red circles). A neuron takes a set of numeric values as input and maps them to a single output value. (A neuron is a simple multi-input linear regression function, where the output is passed through an activation function.) Two common activation functions are logistic and tanh functions. There are many others, including logistic sigmoid function, arctan function, bipolar sigmoid function, etc. Continue reading the rest of the article here.

Data Science, Machine Learning (and AI) 2019 watch list/predictions

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Data Science and Machine Learning have been headline topics for many years now. Even before the Harvard Business Review article, ‘Sexist Job of the 21st Century‘, was published Bach in 2012. The basics of Data Science, Machine Learning and even AI existing for many decades before but over recent years we have seem many advances and many more examples of application areas.

There are many people (Futurists) giving predictions of where things might be heading over the next decade or more. But what about issues that will affect us who are new to the area or for those that have been around doing it for way too long.

The list below is some of the things I believe will become more important and/or we will hear a lot more about these topics during 2019. (There is no particular order or priority to these topics, except for point about Ethics).

Ethics & privacy : With the introduction of EU GDPR there has been a renewed focus on data privacy and ethics surrounding this. This just doesn’t affect the EU but every country around the world that processes data about people in EU. Lots of other countries are now looking at introducing similar laws to GDPR. This is all good, right? It has helped raise awareness of the value of personal data and what companies might be doing with it. We have seen lots of examples over the past 18 months where personal data has been used in ways that we are not happy about. Ethics on data usage is vital for all companies and greater focus will be placed on this going forward to ensure that data is used in an ethical manner, as not doing so can result in a backlash from your customers and they will just go elsewhere. Just because you have certain customer data, doesn’t mean you should use it to exploit them. Expect to see some new job roles in this area.

Clearer distinction between different types of roles for Data Science : Everyone is a Data Scientist and if you aren’t one then you probably want to be one. Data Scientists are the cool kids, at the moment, but with this comes confusion on what is a data scientist. Are these the people building machine learning algorithms? Or people who were called Business Intelligence experts a few years ago? Or are they people who build data pipelines? Or are they problem solvers? Or something else? A few years ago I wrote a blog post about Type I and Type II Data Scientists. This holds true today. A Data Scientist is a confusing term and doesn’t really describe one particular job role. A Data Scientist can come in many different flavours and it is impossible for any one person to be all flavours. Companies don’t have one or a small handful of data scientists, but they now have teams of people performing data science tasks. Yes most of these tasks have been around for a long time and will continue to be, and now we have others joining them. Today and going forward we will see clearer distinction between each of these flavours of data scientists, moving away from a generalist role to specialist roles to include Data Engineer, Business Analyst, Business Intelligence Solution Architect/Specialist, Data Visualization, Analytics Manager, Data Manager, Big Data/Cloud Engineer, Statisticians, Machine Learning Engineer, and a Data Scientist Manager (who plugs all the other roles together).

Data Governance : Do you remember when Data Governance was the whole trend, back five to eight years ago. Well it’s going to come back in 2019. With the increased demands on managing data, in all it’s shapes and locations, knowing what we have, where it is, and what people are doing with it is vital. As highlighted in the previous point, without good controls on our data and good controls over what we can do (in an ethical way) with out data, we will just end up in a mess and potentially annoying our customers. With the expansion of ML and AI, the role of data governance will gain greater attention as we need to manage all the ML and AI to ensure we have efficient delivery of these solutions. As more companies embrace the cloud, there will be a gradual shifting of data from on-premises to on-cloud, and in many instances there will be a hybrid existence. But what data should be stored where, based on requirements, security, laws, privacy concerns, etc. Good data governance is vital.

GDPR and ML : In 2018 we saw the introduction of the EU GDPRs. This has had a bit of an impact on IT in general and there has been lots of work and training on this for everyone. Within the GDPR there are a number of articles (22, 13, 14, etc) that impact upon the use of ML outputs. Some of this is about removing any biases from the data and process, and some is about the explainability of the predictions. The ability to explain a ML prediction is proving very challenging for most companies. This could mean huge rework in how their ML predictions work to ensure they are compliant with EU GDPR. In 2019 (and beyond) we will start to see the impact of this and work being done to address this. This also related to the point on Ethics and Privacy mentioned above.

More intelligent use of Data (let’s call AI for now) : We have grown to know and understand the importance of data within our organisations. Even more so over the past few years with lots of articles from Harvard Business Review, the Economist, and lots of others. The importance of data and being able to use it efficiently and effectively has risen to board room level. We will continue to see in 2019 an increase in the intelligent use of data. Perhaps a better term for this is AI driven development. AI can mean lots of things from a simple IF statement to more complex ML and other algorithms or data processing techniques. Every application from now on needs to look at being more intelligent, more smarter than before. All processing needs to be more tightly integrated and more automation of processing (see below for more on this). This allows us to build smarter applications and with that smarter organisations.

Auto ML : The actual steps of doing the core ML tasks are really boring. I mean really boring. It typically involves running a few lines of R, Python, etc code or creating some nodes in a workflow tool. It isn’t difficult or complicated. It’s boring. What makes it even more boring, is the tuning of the (hyper) parameters. It’s boring!. I wish all of this could be automated! Most of us have scripts that automate this for us, but in 2019 we will see more of this automated in the various languages, libraries and tools. A number of vendors will be bring out new or upgraded ML solutions that will ‘Automate the Boring Stuff’ for ML. Gartner says that by 2020 over 40% of data science tasks will be automated.

Automation : Building upon Auto ML (or Automated ML), we will see more automation of the entire ML process, from start to end. More automation on the data capture, data harvesting, data enrichment, data transformations, etc. Again automating the boring stuff. Additionally we will see more of the automation of ML into production systems. Most ML discussed covers up to creating and (poorly) evaluating a model. But what happens after that. We can automate the usage of the ML model (see next point) but not only that but we can automation of the whole iterative process of updating the models too. There are many example of this already and some are called Adaptive Intelligent applications.

Moving from back office to front of house : Unfortunately when most people talk about ML they are very limited to only creating a model for a particular scenario. But when you want to take such models out of the back room (where the data scientists live) and move it into production there are a number of challenges. Production can mean backend processing as well as front end applications. A lot has been covered on the use of ML for large bulk processing (back end applications). But we will see more and more integration of ML models into the every day applications our company uses. These ML models will all us to develop augmented analytic applications. This is similar to the re-emergence of AI application, whereby ML and other AI methods (eg. using an IF statement), can be used to develop more functionally rich applications. Developers will move beyond providing the required functionality to looking at how can I made my application more intelligent using AI and ML.

ML Micro-Services : To facilitate the automation tasks with putting ML into more production front end applications, an efficient approach is needed for this. With most solutions to-date, this has required a lot of development effort or complicated plumbing to make it work. We are now in age of containerisation. This allows the efficient rollout of new technology and new features for applications without any need for lots of development work. In a similar way for ML we will see more efficient delivery of ML using ML Scoring Engines. These can take an input data set and return the scored the data. This data set can consist of an individual record or many thousands. For ML to score or label new data, it is performing a simple mathematically calculation. Computers can perform these really quickly. By setting up and using ML Micro-services allows for many applications to use the ML model for scoring.

Renewed interest in Citizen Data Scientist : Citizen data scientist was a popular topics/role 3-5 years ago. In 2019 we will see a renewed interest in Citizen Data Scientists. Although there might be a new phrase used. Following on from the points above on automation of ML and to the point near the beginning about clearer distinctions of roles, and with greater education on core ML topics for everyone, we will see a lot more employees using ML and/or AI in their everyday jobs. In addition to this, with the integration of ML and AI in all applications (and not just front end applications), including greater use in reporting and analytic tools. We are already seeing elements of this with Chatbots, Analytics tools, Trends applications, etc.

Slight disillusionment for Deep Learning & renewed interest in solving business problems : It seemed that every day throughout 2018 there was hundreds of articles about the use of Deep Learning and Neural Networks. These are really great tools but are they suitable for everyone and for every type of problem. The simple answer is no they aren’t. Most examples given seemed to be finding a cat or a dog in an image or other noddy examples. Yes deep learning and neural networks can give greater accuracy for predictions, but this level of accuracy comes at a price. In 2019 we will see a tail off on the use of ‘real’ deep learning and neural networks for noddy examples, and see some real use cases coming through. For example I’m working on two projects that uses these technologies to try and save lives. There will be a renewed focus on solving real business problems, and sometimes the best or most accurate solution or tool may not be the best or most efficient tool to use.

Big Data diminishes and (Semi-)Autonomous takes hold : Big Data! What’s big data? Does Big Data really matter? Big data was the trendy topic for the past few years and everyone was claiming to be an expert and if your weren’t doing big data then you felt left behind. With big data we had lots of technologies like Hadoop, Map-Reduce, Spark, HBase, Hive, etc and the list goes on and on. During 2018 there was a definite shift away from using any of these technologies and toward the use of cloud solutions. Many of the vendors had data storage solutions for your “Big Data” problem. But most of these are using PostgreSQL, or some columnar type of data storage engine. What the cloud gave us was a flexible and scalable architecture for our Data Storage problem. Notice the way I’ve dropped the “Big” from that. Data is Data and it comes in many different formats. Most Databases can store, process and query data is these formats. We’ve also seen the drive towards serverless and autonomous environments. For the majority of cases this is fine, but for others a more semi-automonous environment would suit them better. Again some of boring work has been automated. We will see more on this, or perhaps more correctly we will be hearing that everyone is using autonomous and if you aren’t you should be! It isn’t for everyone. Additionally, we will hearing more about ML Cloud Services and this has many issues that the vendors will not talk about about! (See first point on data privacy)

Oracle Machine Learning notebooks

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In this blog post I’ll have a look at Oracle Machine Learning notebooks, some of the example notebooks and then how to create a new one.

Check out my previous blog posts on ADWC.

Create an Autonomous Data Warehouse Cloud Service

Creating and Managing OML user on ADWC

On entering Oracle Machine Learning on your ADWC service, you will get the following.


Our starting point is to example what is listed in the Examples section. Click on the Examples link. The following lists the example notebooks.


Here we have examples that demonstrate how to build Anomaly Detection, Association Rules, Attribute Importance, Classification, Regression, Clustering and one that contains examples of various statistical function.

Click on one of these to see the notebook. The following is the notebook demoing the Statistical Functions. When you select a notebook it might take a few seconds to setup and open. There is some setup needed in the background and to make sure you have access to the demo data and then runs the notebook, generating the results. Most of the demo data is based on the SH schema.


Now let us create our first notebook.

From the screen shown above lift on the menu icon on the top left of the screen.


And then click on Notebooks from the pop-out menu.


In the Notebooks screen click on the Create button to create your first notebook.


And give it a meaningful name.


The Notebook shell will be created and then opened for you.

In the grey box, just under the name the name of your Notebook, is where you can enter your first SQL statement. Then over on the right hand side of this Cell you will see a triangle on its side. This is the run button.


For now you can only run SQL statements, but you also have other notebooks features such as different charting options and these are listed under the grey cell, where your SQL is located.


Here you can create Bar, Pie, Area, Line and Scatter charts. Here is an example of a Bar chart.


Warning: You do need to be careful of your syntax, as minimal details are given on what is wrong with your code. Not even the error numbers.

Go give it a good and see how far you can take these OML Notebooks.