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Machine Learning and the Finance Industry

 

 

Did you know that the term “artificial intelligence” was first coined in 1956? Even since then the world has rapidly evolved technologically. From the early days of radio, to television, to the internet, and now we have Artificial Intelligence and Machine Learning. Artificial Intelligence (AI) involves many aspects – including processing robotics and the process of automation by robotics. Due to large amounts of data and the increase in demand for understanding data patterns, AI and machine learning (ML) have become very popular (especially among large companies). When it comes to the financial services industry, thousands of organisations are using ML systems to more efficiently identify data patterns to gain target audience insights and much more. 

 

The financial services industry is full of data records. Hence the relevance of using ML to succeed in this domain. There are many use-cases of ML in the financial industry but risk assessment, fraud detection, financial advice, trading, and managing finance are the most popular (or obvious) business functions within the industry for machine learning systems. 

 

 

Risk Assessment 

To determine which customers are eligible for a credit card, banks use credit scores. However, this method of grouping customers is not necessarily efficient for business.  

Machine learning is now being used to scan thousands of personal financial records for loan repayment routines, the number of active loans, and the number of existing credit cards an individual has – to determine customised interest rates. Lenders can now determine those who are credit-worthy options if they do not possess an extensive credit history. For example, some machine learning algorithms use alternative data, such as mobile phone data, to evaluate loan suitability and customised loan rates. In addition to this, ML-powered models are objective – this removes any biases that a human credit officer may make. A vehicle lending company has experienced a 23% annual decrease in losses by implementing an AI system of loan evaluation. 

 

Fraud Detection and Management 

All organisations aim to reduce risk conditions. The financial industry (and banks in particular) take fraud very seriously – seeing as given loans are basically somebody else’s money. ML security and fraud identification systems are used to alert organisations of unusual behaviour by analysing past spending patterns across many transaction tools. A card being used in a faraway area from where it was previously used or a withdrawal of an amount of money that is unusual for the particular account, for example, are all detectable by ML systems. Understandably, these events may not be fraudulent. One of the benefits of ML-based fraud systems is that they can learn. If a regular transaction is marked as irregular by the system, it can be corrected and learn from its mistake. This allows for better informed decisions about what is flagged as fraudulent behaviour and what is not. 

 

Financial Advisory Services 

Pressure on financial institutions to reduce their commission on individual investments has increased. Luckily machines can give financial advice for a single down payment. Another use case for ML in financial advice is automated advisory – combining ML calculations with human insight to provide more efficient investment options to customers. This collaboration is key. An AI system is neither an all-knowing solution nor a simple marketing accessory; it is just as important to consider the human perspective along with the AI decisions when it comes to financial decision-making. 

  

Trading 

It is not such a recent experience that investment companies have been relying on technology and data scientists to predict future patterns in the market. In the trading field, investments are dependent on one’s ability to accurately predict the future. ML systems are highly useful in this regard: they can produce calculations from large data in a short time. Machine learning allows such systems to predict patterns based on past data (even taking into account – and planning for – anomalies such as the 2008 financial crisis). Depending on one’s appetite for risk, ML systems can suggest portfolio combinations: a person with an interest in high-risk shares depend on ML-calculated decisions on when to buy, hold, and sell. Those with a tendency towards low-risk portfolios can make decisions based on alerts from ML systems about when the market is expected to fall. 

 

 

Finance Management 

In an increasingly materialistic and connected world, managing finances can be a challenge to many people. Personal Financial Management (PFM) is one of the more recent ML-based developments. By analysing where consumers are spending their money, ML models build algorithms to help consumers make more informed decisions about their money. The model creates a spending graph which is personalised based on individual data from one’s web footprint. This may upset advocates of privacy breaching, but this is becoming a more popular way to manage personal finances as it removes the need to make length spreadsheets or hand-written budgets. From these small PFM suggestions to bigger investment portfolio suggestions, ML systems can be beneficial time- and money-saving tools to both customers and employees in the financial services industry. 

 

Machine learning is the future of many business functions within the finance industry. Soon enough it will be able to handle more financially sensitive and tedious tasks and provide more efficient solutions. The long-term cost saving benefit of ML systems are encouraging multiple financial service organisations to implement this technology. Although, currently, the implementation of ML systems in the finance industry is still in its infancy, the speed at which it is helping the industry progress predicts losses will be fewer, trading will be smarter, and customer experience will be better.  

Blog Informative News & Articles Use Cases

Machine Learning & Customer Service 

 

Machine Learning (ML) in customer services is viewed by directors, decision-makers, and by employees as a blessing to both the customer and employee experience. While technology is not yet able to perform all the tasks a human customer service representative could, machine learning in customer service teams is expected to increase by 143% over the next 16 months as more teams turn to chatbots, text, voice analytics, and other use cases. Currently, ML models in customer service are being used to both assist and replace human agents. This is primarily to improve customer service experiences and reduce the service costs of having employees handle simple requests and tasks.

Let’s explore how ML can be used to assist customer service agents; rather than completely replace them. 

 

Messaging Assistants 

We already know (and have probably experienced) chat bots are being used to communicate with customers online. However, many customers and companies feel that bots cannot handle all tasks that human agents can. Whilst still using ML technology – but avoiding frustration – “assistant” bots are being deployed to increase employee efficiency. This system allows the bots to deal with the simple questions up until the conversation becomes too complicated: then the chat is handed over to a human. Once the complex task is finished, the bot can continue with the simple details. The results of this system increase efficiency up to a point where human agents have been able to handle up to 6 co-existing chats at once. 

 

 

Organising E-mails 

Reading emails to decipher and respond to customer needs is a time-consuming task. Luckily, ML systems can speed up the process. ML technology is able to scan and tag emails to direct them to the right person along with suggested responses to the respective emails. Companies needing to respond to a large amount of emails in a short period of time have been able to halve the time customers wait to receive a response. 

 

Enhanced Customer Phone Calls 

When it comes to getting answers about financial questions, customers prefer to do so via phone call than online by a rate of 3 to 1. However, from a technical perspective, it is significantly more challenging to deploy ML voice-based communication systems. Background noise, unusual speech patterns, accents, and poor pronunciation make it hard for an ML model to translate voices into text. Yet even with these difficulties, companies are using machine learning to assist phone-call customer service agents. Conversations can be analysed in real-time with deep learning. These ML models listen to changes in volume, pitch, and can detect mimicking for classifying how customers are feelings and how calls are going. Customer service agents are given suggestions simultaneously to improve their calls based on their performance. A trial of this technology showed a 30% improvement in customer survey scores, a 6% improvement in issue resolution, and a significant decrease in requests to speak to a manager. 

 

 

 

The Future of Machine Learning and Customer Support 

New interesting uses for ML in customer service have only recently been deployed. This is why it is hard to tell how much ML will impact customer service and employment. Call centre employment is only one of many areas where this ML technology is being used (there are many retail salespeople, cashiers, and service hosts that will become freed up because up to 30% of what customer service agents do has automation potential). 

Thanks to ML advances, companies are now able to make quicker and cheaper responses to low-level customer interactions and improve more complex customer engagement experiences with human agents. This may lead to a significant increase in demand and standard of service. Millennials now have a 68% higher expectation of customer service than they had one year ago. Almost 80% of young customers expect the contacted customer service agent to already know their contact and product information. 

In the nearby future, ML in customer service will not cause an overall decrease in customer service jobs; companies may increase the number of ML assisted employees to deal with the new demand due to ML system-related efficiency and customer satisfaction.  

 

Concluding Thoughts 

Machine Learning in customer service is being used to improve the quality of service and reduce the cost of employees across industries such as finance, food, retail, and insurance. Companies now trust chatbots and ML speech-analysis systems to handle simple tasks – leading to a current significant investment in these systems. The benefits of ML in customer service is being felt directly by regular customers in a very clear and immediate sense. Customer service is a space where exciting ML applications are being deployed in across industries at a significant rate. While ML systems are being used to fully take on monotonous tasks – making it easier for human agents to deal with the more difficult issues – as they learn, these systems should soon be able to deal with more requests without human involvement. This means the current level of customer service being provided will be provided in the future at a much lower cost. This is not to say that companies will be able to spend less on this part of their business. As customers expect better service as a result of ML technology, more employees may be necessary. 

Blog Informative News & Articles Use Cases

Quick Read: Product Development & Artificial Intelligence

 

 
How Can Artificial Intelligence be Involved in Product Development?  
 

Let’s focus on two main ways that AI can help with Product Development. Firstly, AI can do the repetitive and boring tasks or jobs in this industry that us humans don’t usually enjoy. Secondly, AI can gather the best information relevant to the product development so that better quality products can be produced.  

  
Testing Product Features 
The shortage of quality assurance engineers and analysts in the workplace means that the quality standard of products is not always up to scratch. Testing the features of products can make up 50% of the work being done on a product before it is even approved for production. This is seen as a huge time-consumer – especially when aspects such as customer and competitor research are being overlooked. 

An AI automated approach can help in this regard. By assimilating, machines can meticulously mimic human behaviour. This means that a team of testers can move beyond the traditional route of manually testing products and progressively move forward toward an automated and precision-based continuous testing process. AI models can test the features of the product to find faults (with a higher chance of finding faults that a human product manager may have missed). Not only are product faults lessened by using AI models, but human error is minimised too. Even the most meticulous tester is bound to make mistakes while carrying out monotonous manual testing. By performing the same steps accurately every time, AI models never miss out on recording detailed results. Testers are freed from repetitive manual tests and have more time to create new automated software tests and deal with sophisticated features. 

 
Designing for Ultimate User Experience 
Successful products are generally those with uses that resonate with the users. The product needs to be user-friendly and, in some cases, even fun to use. Product design calls for creativity across the board. The design team must consider the product uses (regular and unusual) and support those uses that embrace the business objectives as well as those that give the user value.  

 Artificial Intelligence can use behavioural data to determine how a product is used and how it can be built to best fulfil its use. The most useful part of this determination is that AI can alert the design team before the manufacturing phase as to which designs will and what won’t be successful. By analysing the intended steps to use a product, AI can determine whether a user will be successful in getting the desired action from the product or not. This means that it is no longer necessary to build different prototypes of a product for testing. Simply run all the different product designs through a simulation and let AI models determine which is best. This is ground-breaking for companies that are heavily reliant on manufacturing: saving them time and money previously spent on product development and research. 

 

 

As Artificial Intelligence has become relevant in the product development cycle, organizations are faced with the decision whether they should adopt it wholly within their practices. Initially, the set-up costs of an AI system may seem unjustifiable in the product development industry, but in time organisations will produce greater testing rewards for less money.  These new savings can be redirected towards quality assurance efforts, testing uncovered areas, or more exciting and creative parts of product testing. In the future, AI in product development won’t only be relevant in product testing but will be applicable to all roles across product development involved in delivering top-quality products to the market. 

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Inside Intellify: Welcome Reza & Evan

 

 

Intellify would like to formally announce the joining of two new members – Reza Sobhani and Evan Crawford. We would like to take this opportunity to welcome them to the Intellify family and get to know them a bit better.

 

Reza Sobhani joins Intellify as our Chief Analytics Officer. With more than fifteen years’ experience working in various innovative disciplines, Reza is ambitious to make the world a safer place by leveraging technology. He has a diverse academic & research​ background and has a strong business acumen. He is delivery oriented and eager to learn & teach. Welcome Reza!

 

Evan Crawford is our new Director of Sales & Marketing. Joining from Amazon, Evan is passionate about the potential that Machine Learning offers to provide elegant, high quality solutions to large and complex problems. He is curious about the latest developments in technology and loves helping customers understand where they can be used to solve important business problems. Welcome Evan!

 

Now that we have a bit of a background, let’s see if we can get to know these two on a personal level with 5 quick questions.

 

 

Q: Outside of Intellify offices, what is one thing you can boast about being good at?

 

EC: I’m pretty good at watching sports – there’s not many I don’t enjoy.

 

RS: Music – I play six different instruments – and languages – I studied in Japan for four years so I try to speak Japanese whenever I can.

 

Q: What was the last book you read?

 

EC: Bad Blood, John Carreyrou (on Audible of course!)

 

RS: The Rational Optimist by Matt Ridley.

 

Q: When you were in school, what did you want to be when you grew up?

 

EC: #9 for the Socceroos.

 

RS: An engineer. I’ve always wanted to build things.

 

Q: What’s the best piece of professional advice you’ve ever received?

 

EC: ‘Google AWS’

 

RS: The world of professional services and engineering is very small. Keeping good relationships and networks is the key. Not only to learn more from others but also to grow and influence.

 

Q: How do you relax after work?

 

EC: Play with the kids!

 

RS: That’s a tough one. I usually don’t (haha). Maybe go to the gym, listen to music, or catch up with friends and family.

 

Thanks Evan and Reza for telling us a bit more about yourselves. We are excited to see what your experience and knowledge can bring to Intellify to keep us growing and thriving as Australia’s leading consultancy for Machine Learning & Artificial Intelligence.

Blog Events

May Deep Learning Sydney: Sponsored by Intellify

 

Deep Learning Sydney is a meetup group for anybody who has skills or an interest in the hottest area in Machine Learning – deep learning! From data scientists, to researchers, to developers, to companies, to investors, and finally to entrepreneurs. May’s Deep Learning Sydney Meetup was sponsored by Intellify and hosted at AWS.

Pizza and ciders, provided by the sponsors, fed the excitement of the attendees and speakers as the room filled up. Once 100 or so attendees were satisfied and seated, the evening kicked off with a few housekeeping words from the meetup co-founder Andy Zeng and (after some encouragement from the audience) Professor Richard Xu gave an opening speech.

Kale Temple, the co-founder and practice director at Intellify, was introduced. Kale has worked in analytics and data science consulting for more than 5 years and has helped a number of the world’s leading corporate and government organisations to deliver high impact data projects. He is currently an Honorary Affiliate of the University of Sydney’s Business School. Kale gave a short presentation on time-series. Time-series, as a field of study, has largely focused on statistical methods that work well under strict assumptions. Specifically, when there is sufficient history, there is little meta-data and a well-formed auto-correlation structure. However, as an applied practitioner I know that most real-world time series problems violate these assumptions. This leaves us with an opportunity to use more modern time series methods, based on machine learning (and deep learning), to overcome these deficiencies. This session is designed to briefly speak about the unique properties of time-series, how statistical methods work and how and why machine learning (and deep learning) methods can be used to improve accuracy.

Next up was Erica Huang, a first-class graduate from the University of Sydney and a current PhD student at the University of Technology Sydney who is specialising in probabilistic Deep Learning Generation. Erica showed a series of demos using TensorFlow 2.0 which included several examples highlighting the new features of TensorFlow 2.0. Her demos compared and contrasted TensorFlow 2.0 to the features of TensorFlow 1.0.

 

 

The evening came to a conclusion with Andy Zeng giving his usual updates about what is new in Deep Learning. Here he updated us on all the new happenings in Deep Learning in the last six months. After the presentations were finished, the attendees and speakers were able to socialise and ask questions about the presentations and the world of Deep Learning in Sydney and beyond. The event was informative as well as entertaining (and very long overdue). Thank you to Intellify for sponsoring the meetup, to AWS for hosting the evening, and the Deep Learning Sydney Meetup Group for enabling people from all industries and places to come together and share a few hours of common interest in one topic: Deep Learning.

Events

Join Us – Deep Learning Sydney Meetup

Tuesday, 28 May 2019
5:45 pm to 7:45 pm

AWS Sydney

Level 37,2 Park St · Sydney
Sign up here: 

 

“The wait is finally over! Thanks to Iman Eftekhari (Intellify) and Koorosh Lohrasbi (AWS) sponsoring our great Deep Learning Sydney Meetup in May!

Andy and I are once again sincerely apologising to all our members for not running a meetup this year yet… So we promise it’s going to be a super fun, informative and exciting event.

The Agenda will be TBA.

Also for AWS security, they do need attendees to register with first and last name.”

 

Blog Informative News & Articles Use Cases

Artificial Intelligence in the Retail Industry – 5 Common Uses

How Can Artificial Intelligence be Implemented in the Retail Industry? 
 

 

If you ask any business leader or expert in the retail industry what the future of shopping looks like, you’re bound to hear something about “bots” shaking up the way things work. Artificial Intelligence (AI) in retail is being used across the entire service and product life cycle – from manufacturing to post-sale interactions with customers. This means that retailers need to change their strategy and thinking in order to include this technology and remain relevant. 

 

In this blog post we will look at 5 ways in which AI is being used in retail:   

 

1) Product Recommendation through Personalisation 

 

Products are now being recommended to customers based on their personal buying habits. Our online buying habits create data categorised into, for example, products bought, preferred brands, money spent and frequency of buying. Machine Learning algorithms use this data to predict which items a customer is likely to buy. Once this technology anticipates the intent of the customer, it provides a recommendation in real-time with incredible accuracy. These recommendations come in many forms – for example, links to other items (“customers who bought that also liked this….”) or marketing emails that contain the chosen items that are suited to your buying patterns. Whatever the form of the recommendation, customers are more likely to buy products or services that require little extra thinking and align with their already formed self-concept. Machine Learning algorithms and personalised product recommendation allows for just this.   

 

2) Customer Service and Complaints Resolution 

 

Artificial Intelligence software can help build a strong sense of brand trust through communication – by answering questions and resolving issues. Brand trust is earned when customers are satisfied by answers to their questions and by the outcome of their complaints. Artificial Intelligence software manage multiple communications at one time – meaning no customers are kept “waiting in the queue”. In addition to less time wasted, Machine Learning “operators” can answer customer queries more accurately by avoiding human mistakes (that a “mere human” operator would make in fetching answers). Artificial Intelligence software is also able to make decisions to solve customer queries or raise customer complaints with higher management immediately – giving customers a solution without delay. 

 

3) Inventory Management – Demand & Forecasting 

 

One item out of stock can bring a business to its knees. Machine Learning time series prediction models can estimate future demand levels for all items in an inventory. Although this is useful for retailers to know, Machine Learning models are even able to make decisions based on the demand information that they produced. This is a more advanced approach and is done by rewarding and punishing the model for acting incorrectly. For example, a model is punished for letting a particular inventory item run out of stock or for stocking a higher value for too long. A model is rewarded when in-demand inventory items are ordered within a safe window before it’s too late. These reinforcement systems produce outstanding results – more than 30% reduction in inventory operations costs in many cases! 

 

4) Pricing Strategy & Product Bundling 

 

Most of time, sales made depend on the prices of the items or services being sold. Customers will wait for prices to drop for the items they want or for items to be bundled at a better price. This leads to times when sales generate high revenue for short bursts and potentially overall unprofitable sales because of special price deals. Artificial Intelligence models can be used to drive sales by targeting a particular set of customers at their “optimal price” – leading to immediate sales. Different customer sets can be targeted by AI systems by determining which product combinations and at which price will lead to immediate or more likely sales. When it comes to non-urgent items, these methods are proven to make sales more consistent. 

 

5) Strategy Testing 

 

Artificial Intelligence allows retailers to test different strategies against each other without having to implement any of them – saving them potentially lost resources. By using AI and Machine Learning models to monitor customer behaviour (through computer vision and activity maps), individual shopping experiences can be created and resources can be better allocated to areas most needed. Retailers can find answers to their common questions, for example: 

 

Which rack is most explored by customers? 
Is it popular because of location or products? 
Can the most profitable products be placed on the most popular rack? 
Will people buy if prices are increased? 
Should high priced products be shown prominently? 

 

 

These 5 ways in which AI an ML are being used in retail situations are only a few of the possibilities that are available to retailers at the moment; and best believe there will be more to come! Despite these new technologies available in the retail industry, it will continue to remain heavily competitive. Retailers need to be aware of this technological shift and consumer trends that could drastically impact their business and the industry as a whole. As always, the key to success in retail is that the customer needs, wants, and expectations are understood and accounted for. As long as this is kept at the forefront of the retailer’s mind, then a changing shopping landscape is no cause for concern.

 

Informative Use Cases

Natural Language Processing in Business – How Is It Useful?  

What is Natural Language Processing? 

 Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding. 

While natural language processing isn’t a new science, the technology is rapidly advancing thanks to an increased interest in human-to-machine communications, plus an availability of big data, powerful computing and enhanced algorithms.  

 

 

While we, as humans, may be able to understand and speak more than one language, most people do not have fluency in “machine code” or “machine language” – millions of ones and zeros that produce logical actions. In the past, early programmers would use punch cards to communicate with computers but now “Siri, what is the temperature today” triggers a real-time response in a human voice. This interaction is made up of multiple sections: device activation from your voice, an understanding of your command, and a response in a fluent human language. This common “conversation” is made possible by NLP thanks to machine learning and deep learning elements. 

 

Common Uses for Natural Language Processing in Business 

Chatbots 

Chatbots are one of the more recent NLP examples. Most business bots are used in Human Resources. Some NLP tools are used to deal with common employee questions, for example, “how many days leave do I have” or even deal with workplace satisfaction requests such as “we need more milk in the breakroom”. Other chatbots are used to improve employee retention and morale – employee chatrooms are monitored by bots and when employees use certain complementary words like “kudos” or “cheers” then staff get rewarded. 

 

Conversational Search 

Other non-bot tools are voice-activated. For example, tools are being used to listen in on company meetings for trigger phrases like “what are” and “I wonder.” When it hears them, a search function activates and retrieves an answer. It would work something like this: “What was the ROI on that last year?” Silently, the tool would scan company financials and display the results on a screen in the room. This can save the 30% of the day that employees spend searching for information and up to US $14,209 per person per year.  

 

Hiring tools 

On the topic of HR, NLP software has long helped hiring managers sort through resumes.  

By using the same techniques as Google search, automated candidate sourcing tools scan applicant CVs to pinpoint people with the required background for a job. This is difficult however, as humans we try to stand out from the crowd and may use creative terms to describe our skills and this can cause CVs to be ignored by NLP scans. More recently, NLP technology has branched to include synonym searching on keywords. In addition to this, as women and minorities use language differently, this synonym-searching keeps qualified candidates from slipping between the cracks.  

 

Call Centres 

Natural Language Processing (NLP) technologies are increasingly active in the labour-intensive world of call centres. Both inbound call centres (usually administer customer and product support or information enquiries) and outbound call centres (used for telemarketing, debt collection, soliciting charitable etc.) are implementing NLP tools. 

The technology used in call centres roughly falls into four categories: real-time speech recognition; intent analysis, which classifies conversations based on context to predict customer intent in real time; conversation management, which ensures simultaneous processing of multi-pass conversations; and conversational analysis, which broadly analyses users’ dialogue. These previously extensively human-intense operations (demanding training, time, and money) are now more efficient and effective – thanks to NLP. 

 

These are only a few examples of ways in which Natural Language Processing can be implemented in business situations every day. While such Artificial Intelligence technology may seem to pose a threat to employee’s jobs; this is not the case. At closer inspection, these tools are simply allowing employees to work more efficiently by having the tedious and time-consuming aspects of their roles sorted out by machines and bots. Thus, ensuring that time spent is well-spent, that employees are satisfied, customers are being heard, and ultimately, businesses save time and money! 

Blog Events

PyData Sydney Meetup: May 2019

May’s Pydata Syndey Meetup was hosted at a new venue: Optiver Asia Pacific – our sponsor for the event. Just one week after the AWS Summit the buzz from winning AWS Partner of the Year in Analytics, Data, and Machine Learning was still very present!

One of the speakers of the evening, Nick Wienholt, is a consulting data engineer and data scientist with a focus on quantitative finance and the sports gaming industry. He spoke about how to quickly scale a local Jupyter notebook using Keras and Tensorflow to train and execute on Google Cloud using specialised GPUs and TPUs.

 

Our second speaker’s, Vikram Patil, seminar mainly revolved on how data from a csv file can be transformed to dynamic web application, mainly for visualization purposes with fewer code lines. This can be achieved from shiny package, built upon R. The seminar explained – in detail – the code structure and rules to follow while developing a visualization application. Additionally, another package was also explained known as Leaflet (this package is powerful to display location data, for example, latitude and longitude details).

 

The seminar had 3 demos:

Demo 1:

The first demo explained the Rshiny visualisation. With a basic example on how histograms can be varied with an input.

 

Demo 2:

This demo explained how the Leaflet Package can be a powerful application to display the locations on a map.

Example below: various schools in Victoria state.

Data:

Below: Map on Rshiny using Leaflet

The number explains the schools inside the regions, for example, around 635 schools around Melbourne region.

 

Demo 3:

The below data in Microsoft Excel is mainly about Coral bleaching across great barrier reef.

 

Below is the web application developed through Rshiny, which displays the different coral bleaching across various sites.

 

Overall, the event was another informative and entertaining one. The pizzas and drinks were a great bonus too! Thanks to our sponsor Optiver for the opportunity and venue.

Don’t miss out on our next PyData Sydney event. Book your place here: https://www.meetup.com/en-AU/PyData-Sydney/events/nvlxxqyzjbhb/.

Look forward to another exciting PyData Event in June!

AWS Blog Events In the Media News & Articles Uncategorized Why Choose Us

Winner of the Differentiation Partner of the Year Award 2019

It happened! Intellify is officially the winner of the APN Differentiation Partner of the Year Award 2019.
It is a great honour to have received this award – being up against so many innovative industry leaders in Australia.

We confidently attribute our success thus far to the hard work of our employees who thrive on providing the most innovative and effective AI and Machine Learning solutions to businesses across multiple industries. We would like to thank our clients and partners for the business and support as well as AWS for this exciting opportunity.

Being a member of the global AWS Partner Network (APN) means we were up against tens of thousands of successful businesses who also use AWS Cloud solutions. The APN awards recognise industry leaders across the local channel that are playing a key role in helping their own customers drive innovation using the vendor’s cloud platform. The event also recognises partners whose business models continue to evolve and constantly drive costs down, while modernising for customers’ benefits. Our award, the Differentiation Partner Award, recognises partners that have developed an innovative capability on or integrated with AWS and this year Intellify has been recognised as the best in this category.

We look forward to keeping up this level of innovation and hard work in the future and hope to be able to participate in many more opportunities like this one going forward!

Take this as an opportunity to check out more about what we do and book a consultation with the official leaders in the industry – http://www.intellify.com.au/contact/.