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Gold Coast Excellence - Queensland

Gold Coast Excellence Awards 2020

Gold Coast Excellence Awards 2020 - IT and Digital

The Gold Coast Business Excellence Awards launched in 1996 and in 2020 are celebrating our 25th year! During this time the Awards have grown to be recognised as the region’s most comprehensive and prestigious business awards scheme, offering specific and meaningful benefits to the wider Gold Coast business community.

TechConnect is super proud to support the Gold Coast community, growing locally, nationally and internationally.  With a presence in Brisbane, Melbourne, Perth and South Africa we have grown from our humble base in the Gold Coast, Queensland.

This was the first year that TechConnect has entered this award and are thrilled to have won the category of IT & Digital Business. We look forward to the annual awards and competing with some of the amazing businesses on the Gold Coast.

Gold Coast Excellence Awards 2020 Queensland

 

Pictured below is Mike Cunningham – CEO of TechConnect (left) and Fabrizio Carmignani – Professor of Economics Griffth University (right).

Gold Coast Excellence Awards

TechConnect IT Solutions_Data

Digital Transformation with Data

TechConnect IT Solutions_Data

Harnessing Data to drive effective digital transformation

The COVID-19 pandemic has made clear that businesses need to be prepared for flexible, remote working practices.

As lockdowns forced offices to close and people headed home to limit the potential spread of the virus, many organisations found they weren’t prepared to provision the necessary work from home (WFH) technology and processes for their staff to continue with business as usual.

As a result, businesses have been required to undertake (or accelerate) a significant digital transformation journey to get up to speed. As these transformation journeys roll out, the need to harness data effectively becomes more critical than ever for a successful, long term change. Here’s what you need to consider.

A strategic approach

Before beginning a digital transformation, it’s critical to have a strategy in place to explain how you will manage, store, secure and use your data. Yet, this is a step that’s often forgotten in the rush to transform and digitise processes.

A data strategy should be driven by the needs of your business. Your strategy will also define how to make decisions about the use of data, more capably manage data flow, and secure information effectively.

Any successful plan will identify realistic goals along with a road map for rolling it out. This ensures that you’re properly prepared for every step of the journey.

Beginning the journey

A digital transformation unshackles an organisation from the past. It empowers you to move into the future, free of outdated technology and slower manual processes.

For example, take mobile and cloud technology. While we were once restricted to an office environment for productive working, it’s now possible for geographically diverse teams to collaborate as efficiently as they would in a traditional office setting. Files, apps, and other resources can all be accessed remotely, and meetings held virtually, giving workplaces and workforces the ability to be truly flexible.

However, the reality of a digital transformation is that with staff spread across locations, there are a range of new infrastructure management issues to consider. Chief among these is data security.

Keeping data safe is vital as users access business networks and devices remotely, often without the protection provided by robust on-site architecture. It’s important to decide how you’ll service and secure company devices, and how to make sure users and the data they handle and generate will be protected, and implementing those systems early.

Harnessing the power of data

With a clear data strategy in place and your digital journey underway, you can start to take advantage of the power of your data and use it to drive improved decision-making internally and externally.

Artificial intelligence (AI) and machine learning (ML) can be used to sort your unstructured data, learning as they go to uncover valuable insights. Once the data has been cleansed, you can enrich it by adding third-party data or public datasets to uncover more hidden insight.

The adoption of AI and ML also frees your people for bigger picture tasks. Instead of manually sorting through stacks of data, they can concentrate on delivering valuable and creative work powered by the insights you’ve identified – ultimately working towards the goals outlined in your data strategy.

Gathering data helps to deliver external benefits to your business too. It can improve customer service by identifying current pain points or uncover new customer segments for targeting – the possibilities are endless!

The lesson in the journey

Businesses shouldn’t underestimate the change that needs to be undertaken in digital transformation journeys. They require significant planning and thought before beginning. However, while the challenge is large, data can make the journey less difficult and more successful.

Accessible, accurate and relevant data enables businesses to make better informed decisions and deliver actionable insights. And by establishing a data strategy up front, you can better understand, apply and secure your data to meet the needs of your organisation.

If you’re asking yourself questions such as “Are we doing things the right way?” or “Can we do this better?” why not get in touch and let’s explore how TechConnect can deliver results for your business as you undertake a digital transformation.

TechConnect achieves AWS Data and Analytics Competency

TechConnect Directors - Amazon Web Services - Data and Analytics Competency

AWS Data and Analytics Competency

Proves technical proficiency, operational excellence, security, reliability and 360-degree customer delivery capability; Cites major client projects with Virgin’s Velocity Frequent Flyer and IntelliHQ.

TechConnect IT Solutions (TechConnect), a leading provider of cloud services and an Amazon Web Services (AWS) Advanced Consulting Partner, today announces it has been awarded AAWS Data and Analytics Competency certification; the only Advanced Consulting Partner in Australia to achieve this prestigious competency level.

The AWS Competency Program recognises partners who demonstrate technical proficiency and proven customer success in specialised solution areas. TechConnect undertook a rigorous partner validation process to be awarded the certification, including an independent audit of its technical, organisational, governance and customer capabilities; along with scrutiny of large scale, in-production customer deployments.

Customer case studies that were reviewed as part of the certification process include a customer insights project with Velocity Frequent Flyer; a predictive medicine data platform for IntelliHQ that uses heart rate variability to predict patient outcomes; and a big data analytics project with Kamala Tech that gave technical users data to form insights across the business including areas such as data science, machine learning, marketing systems, reporting and self-service capabilities.

As healthcare comes under more and more pressure to deliver quality personalised care under constrained budgets the healthcare industry is seeing innovation with the use of data to drive efficiencies and better patient outcomes. As Machine Learning and Artificial Intelligence (AI) emerge as a driver for businesses to do more with less, healthcare can deliver better care with the same resources using data to drive out those efficiencies.

“We partner with industry in AI as we need as many talented and gifted people in this space as possible. said Dr Brent Richards. is the Medical Director of Innovation – Gold Coast Hospital and Health Service (GCHHS). “There is a lot that the industry can bring that healthcare specifically does not have in terms of hardware, software and talent.” Dr Richards played a key role in the project with IntelliHQ which is a partnership between Gold Coast Health, industry and universities to transform healthcare through AI, enhancing patient outcomes and improving quality of care, while maximising cost-effectiveness.

Oliver Rees, Chief Analytics Officer with Virgin’s Velocity Frequent Flyer program has commended TechConnect’s expertise and cites the direct benefits for member experience. “Velocity have always been a company that is passionate about using insights to understand and improve on their members’ experience. Velocity worked with TechConnect to build a platform that would allow Velocity to combine member insights in a single location to make it easier for members to then receive relevant program offers,” said Oliver Rees, Chief Analytics Officer – Velocity Frequent Flyer.

“In achieving this level of competency with AWS, TechConnect has demonstrated our ability to help customers solve their most challenging data problems within large scale production deployments. We proved that we have deep expertise in designing, implementing, and managing Data and Analytics applications on the AWS platform and have delivered solutions seamlessly in the AWS Cloud environment,” said Clinton Thomson, Director of TechConnect IT Solutions.

TechConnect is a fast-growing Australian company, headquartered in Queensland and serving clients around Australia and worldwide. TechConnect helps customers extract business value from data and it has plans to grow its team to 100+ people over the next three to five years, creating graduate employment and professional development opportunities in Queensland and throughout Australia. The company has offices in Brisbane and the Gold Coast and has a graduate pathways program for top students in the STEM fields.

CRN Fast50 Award

CRN Fast50 for 2019 Award

CRN Fast50 - Number 15TechConnect was listed in the CRN Fast50 for 2019, settling into the 15th position in our debut year. A day after receiving the Deloitte’s Technology Fast 50 awards we were again presented a great result. Read about the Deloitte’s Technology Fast 50 here if you missed it.

The CRN Fast50 award, now in its 11th year, recognises the fastest-growing companies in the Australian IT channel, based on year-on-year revenue growth. “The 2019 CRN Fast50 put up astounding numbers: they grew at least 15 times faster than Australia’s economy.” – Simon Sharwood, Editorial Director – CRN. CRN Fast50 Award

This was the first year that TechConnect has entered this award and are thrilled to be listed 15th, with a growth rate of 67%. Simon Sharwood also stated, “It’s a huge achievement to have made it into the CRN Fast50. Your company’s growth not only outpaced most others in our industry, it also vastly exceeds Australia’s current overall growth rate!”.

This is a great achievement for us, we would like to thank our customers and team, without them this would not have been possible. Team TechConnect would also like to extend a congratulations to all the other winners. TechConnect looks forward to expanding our growth and moving up the list in coming years.CRN Fast50 - Clinton Thomson

Deloittes Technology Fast50

Deloitte – Technology Fast 50 Australia 2019

Deloittes Fast50 AwardWe are extremely excited and honoured to have been listed as one of Deloitte’s Technology Fast 50 Australian companies. Ranking 43rd on the list with a growth rate of 161%, in our debut year. This is a huge achievement not only for the company but also for our team and the hard work they have put in to provide solutions for our customers.

“Now in its 18th year in Australia, the Deloitte Technology Fast 50 program ranks fast growing technology companies, public or private, based on percentage revenue growth over three years.” – DeloitteDeloittes Technology Fast50

“More than ever, this year showcases world-leading Australian business innovation and it is a tremendous achievement for any company to be named among the Deloitte Technology Fast 50,” Deloitte Private Partner and Technology Fast 50 Leader, Josh Tanchel.

With our significant growth over the years, our hard-working team and most importantly our valued customers, we were able to achieve this amazing outcome. TechConnect is getting ready for exponential growth, through expanding into new markets and deep data specialisations.

The awards night was held on Wednesday, 20th of November in Sydney at the Museum of Contemporary Art. Clinton, our Director, was there to accept the award on behalf of TechConnect. “It was a great night; Well done to all the other recipients and thank you to Deloitte’s for putting it all together.” Clinton Thomson, TechConnect IT Solutions.Deloitte Fast50 - Clinton Thomson

Machine Learning using Convolutional Neural Networks

Machine Learning with Amazon SageMaker

Computers are generally programmed to do what the developer dictates and will only behave predictably under the specified scenarios.

In recent years, people are increasingly turning to computers to perform tasks that can’t be achieved with traditional programming, which previously had to be done by humans performing manual tasks.   Machine Learning gives computers the ability to ‘learn’ and act on information based on observations without being explicitly programmed.

TechConnect entered the recent Get 2 the Core challenge on Unearthed’s crowd sourcing platformThis is TechConnect’s story, as part of the crowd sourcing approach, and does not imply or assert in any way that Newcrest Mining endorse Amazon Web Services or the work TechConnect have performed in this challenge.

Business problem

Currently a team at Newcrest Mining manually crop photographs of drill core samples before the photos can be fed into a system which detects the material type. This is extremely time-consuming due to the large number of photos. Hence why Newcrest Mining used crowd sourcing via the Unearthed platform, a platform bringing data scientists, start-ups and the energy & natural resources industry together.

Being able to automatically identify bounding box co-ordinates of the samples within an image would save 80-90% of the time spent preparing the photos.

Input Image

Machine Learning using Convolutional Neural Networks

Expected Output Image

Machine Learning using Convolutional Neural Networks

 

Before we can begin implementing an object-detection process, we first need to address a variety of issues with the photographs themselves, being:

  • Not all photos are straight
  • Not all core trays are in a fixed position relative to the camera
  • Not all photos are taken perpendicular to the core trays introducing a perspective distortion
  • Not all photos are high-resolution

In addition to the object-classification, we need to use an image-classification process to classify each image into a group based on the factors above. The groups are defined as:

Group 0 – Core trays are positioned correctly in the images with no distortion. This is the ideal case
Group 1 – Core trays are misaligned in the image
Group 2 – Core trays have perspective distortion
Group 3 – Core trays are misaligned and have perspective distortion
Group 4 – The photo has a low aspect ratio
Group 5 – The photo has a low aspect ratio and are misaligned

CNN Image Detection with Amazon Sagemaker

Solution

We tried to solve this problem using Machine Learning. In particular, we used supervised learning. When conducting supervised learning the system is provided with the input data and the classification/label desired output for each data point. The system learns a model that when provided a previously seen input will reliably output the correct labelling or the most likely label when an unseen input is provided.

This differs from unsupervised learning. When utilising unsupervised techniques, the target label is unknown and the system must group or derive the label from the inherent properties within the data set itself.

The Supervised Machine Learning process works by:

  1. Obtaining, preparing & labelling the input data
  2. Create a model
  3. Train the model
  4. Test the model
  5. Deploy & use the model

There are many specific algorithms for supervised learning that are appropriate for different learning tasks. The object detection and classification problem of identifying core samples in images is particularly suited to a technique known as convolutional neural networks. The model ‘learns’ by assigning and constantly adjusting internal weights and biases for each input of the training data to produce the specified output. The weights and biases become more accurate with more training data.

Amazon SageMaker provides a hosted platform that enabled us to quickly build, train, test and deploy our model.

Newcrest Mining provided a large collection of their photographs which contain core samples. A large subset of the photos also contained the expected output, which we used to train our model.

The expected output is a set of four (X, Y) coordinates per core sample in the photograph. The coordinates represent the corners of the bounding box that surrounds the core sample. Multiple sets of coordinates are expected for photos that contain multiple core samples.

The Process

We uploaded the supplied data to an AWS S3 bucket, using a separate prefix to separate images which we were provided the expected output for, and those with no output. S3 is an ideal store for the raw images with high durability, infinite capacity and direct integration with many other AWS products.

We further randomly split the photos with the expected output into a training dataset (70%) and a testing dataset (30%).

We created a Jupyter notebook on an Amazon SageMaker notebook instance to host and execute our code. By default the Jupyter notebook instance provides access to a wide variety of common data science tools such as numpy, tensorflow and matplotlib in addition to the Amazon SageMaker and AWS python SDKs. This allowed us to immediately focus on our particular problem of creating SageMaker compatible datasets with which we could build and test our models.

We trained our model by feeding the training dataset along with the expected output into an existing Sagemaker built object detection model to fine tune it to our specific problem. SageMaker has a collection of hyperparameters which influence how the model ‘learns’. Adjusting the hyperparameter values affects the overall accuracy of the model and how long the training takes. As the training proceeded we were able to monitor the changes to the primary accuracy metric and pre-emptively cancel any training configurations that did not perform well. This saved us considerable time and money by allowing us to abort poor configurations early.

We then tested the accuracy of our model by feeding testing data – data it has never seen – without the output, then comparing the model’s output to the expected output.

After the first round of training we had our benchmark for accuracy. From there we were able to tune the model by iteratively adjusting the hyperparameters, model parameters and by augmenting the data set with additional examples then retraining and retesting. Setting the hyperparameter values is more of an artform than a science – trial and error is often the best way.

We used a technique which dynamically assigned values to the learning rate after each epoch, similar to a harmonic progression:

Harmonic Progression

This technique allowed us to start with large values to allow the model to converge quickly initially, then reduce the learning rate value by an increasingly smaller amount after each epoch as the model gets closer to an optimal solution.  After many iterations of tuning, training and testing we had improved the overall accuracy of the model compared with our benchmark, and with our project deadline fast approaching we decided that it was accurate as possible in the timeframe that we had.

We then used our model to classify and detect the objects in the remaining photographs that didn’t exist in the training set.  The following images show the bounding boxes around the cores that our model predicted:

CNN Bounding
CNN Bounding

Lessons Learned

Before we began we had an extremely high expectation of how accurate our model would be. In reality it wasn’t as accurate as our expectations.
We discussed things that could have made the model more accurate, train faster or both, including:

  • Tuning the hyperparameters using SageMakers automated hyperparameter tuning tooling
  • Copying the data across multiple regions to gain better access to the specific machine types we required for training
  • Increasing the size of the training dataset by:
    • Requesting more photographs
    • Duplicating the provided photographs and modifying them slightly. This included:
      • including duplicate copies of images and labels
      • including copies after converting the images to greyscale
      • including copies after changing the aspect ratio of the images
      • including copies after mirroring the images
  • Splitting the problem into separate, simpler machine learnable stages
  • Strategies for identifying the corners of the cores when they are not a rectangle in the image

During these discussions we realised we hadn’t defined a cut-off for when we would consider our model to be ‘accurate enough’.

As a general rule the accuracy of the models you build improve most rapidly in the first few iterations, after that the rate of improvement slows significantly. Each subsequent improvement requires lengthier training, more sophisticated algorithms and models, more sophisticated feature engineering or substantial changes to approach entirely. This trend is depicted in the following chart:

Learning accuracy over time

Depending on the use case, a model with an accuracy of 90% often requires significantly less training time, engineering effort and sophistication than a model with an accuracy of 93%. The acceptance criteria for a model needs to carefully balance these considerations to maximise the overall return on investment for the project.

In our case time was the factor that dictated when we stopped training and started using the model to produce the outputs for unseen photographs.

 

Thank you to the team at TechConnect that volunteered to try Amazon Sagemaker to address the Get 2 the Core Challenge posted by Newcrest Mining on the Unearthered portal.  Also big thanks for sharing lessons learned and putting this blog together!

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