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Tag: AWS

Blog Informative

Preparing for Your Machine Learning and Artificial Intelligence Transformation

Machine Learning (ML) and Artificial Intelligence (AI) has use cases across multiple industries and business functions, but how do organisations ensure that they get the most from the implementation of ML and AI? Companies who reap the rewards of these tools usually undergo a common process to prepare for the changes that come with the application of new technologies and practices.  

 

This post aims to briefly cover the important steps that organisations undergo before realising the many benefits of Machine Learning, in other words, these are the critical aspects in a strategy for organisations starting their AI transformation journey.  

 

First: Where Are You? 

How will you know where to go if you don’t even know from where you are starting? 

 

 

Organisations wanting to implement Machine Learning and AI solutions need to clarify what their AI goals are – realistically. By assessing current skills of employees (both in IT and other departments), facilities or infrastructure, available budget, and free time to complete end-to-end ML solution delivery, organisations can develop a strategy. This strategy will include aspects such as: 

 

State of Data 

Organisations will need to assess current processes related to data – how is it being collected, cleaned, organised, accessed, and processed? In order to for ML models to produce accurate and useful outcomes, data needs to be in good shape and properly supported. 

 

State of Infrastructure 

85% of organisations are ill-equipped to begin their AI transformation. Artificial Intelligence and Machine Learning models process large amounts of data in real-time, therefore the infrastructure supporting it needs to have fast and modern processing abilities. 

 

Company Culture 

With the implementation of a revolutionary technology, employee mindsets need to be on-par. Leaders within the organisation need to be able to shift their decision-making to embrace data-driven thinking. Teams need to be open to adopting this new technology as well as educated about its usefulness to them (not just to the company at large). 

 

Internal Expertise 

Once experts have delivered the sought Machine Learning capabilities, there may need to be internal team members to continue maintaining the quality of data and observing data patterns for ongoing success. Most organisations do not have the internal capabilities to ensure ongoing success of the technology. This means that organisations need to consider support from external experts (or Managed Services). 

   

Second: Goals 

Now you know where you are, where to next?  

In order to determine what needs to be implemented, organisations need to decide what they want to achieve from the technology. A successful AI and ML implementation journey includes setting goals – what needs to be optimised, automated, or improved? Once these goals have been determined, the most important aspect about bringing them alive is: ensuring the process behind achieving these goals is optimised, rather than the goal (or job) itself. This may sound confusing at first but ultimately it means automating the process (for example, segmenting customers into target groups for marketing) rather than automating the job (the marketing co-ordinator’s job of deciding on targeted campaigns).  

  

Third: Enterprise Support 

 

Just as implementing AI and ML in an organisation cannot be done by one person, the AI strategy mapping cannot either. Keeping the core goals of the company in focus whilst determining AI and ML goals requires interdepartmental planning and collaboration. This usually means leaders from different teams and departments get together as do data engineers and data scientists (internal and external) in order to establish the state of all processes across the company for better goal setting.  

 

Final Notes 

An AI and ML transformation journey can only begin with a deep and guided company strategy – including the current state of things and the goal state of things – and this needs to be all-encompassing and all-inclusive in order to prepare an organisation properly for the start a new technological achievement. Skipping this step will likely result in time, resources, and money wasted – leaving all those involved feeling confused and frustrated.  

 

Being such a vital step of reaching AI and ML goals, the company’s AI and ML strategy needs to be done well. This is best achieved by ensuring those in charge of the strategy are experienced and experts. Intellify is Australia’s leading AI and ML consultancy – delivering end-to-end machine learning solutions from strategy, to enablement, to deployment, and ongoing support. Contact us to begin your AI transformation and stay ahead of the curve.   

AWS In the Media News & Articles Why Choose Us

Intellify – now an AWS Advanced Tier Partner

We are proud to announce that we are officially APN Advanced Tier Partners
Intellify is dedicated to constantly improving and upgrading our skills to ensure our consultants are always up to date with the latest that AWS has to offer. Watch the video below to find out more about what this means for your organisations and for our customers.

Contact us here to learn how our award-winning Machine Learning solutions and support can optimise your business functions.

Blog Use Cases

Use Cases of Machine Learning in the Telecommunications Sector 

 

Machine Learning (ML) and Artificial Intelligence (AI) are becoming a key implementation in every industry with high value and efficiency being provided across the board. The telecommunications sector is no exception and is at the forefront of adopting Machine Learning to optimise operations, increase revenue, transfer and visualise data, enhance customer relations, and improve marketing and sales strategies.  

 

Data transfer, exchange, and analysis is key in the telecommunications sector with the amount of data increasing every day. This is why all systems and methods relating to data need to be relevant and accurate.  

Below are a few of the use cases where Machine Learning and Artificial Intelligence have provided benefit in the telecoms industry: 

 

Product & Service Recommendation Engines 

 Recommendation engines in many industries come up in our digital life. It would not be effective to market product recommendations without taking customer preferences into consideration. By analysing historical customer data (their behaviour and preferences), Machine Learning algorithms are able to predict which products a particular customer will most likely purchase.  

This is done through filtering. Collaborative filtering makes assumptions about a customer’s preferences based on behavioural similarities to other customers (assuming that people with similar profiles have similar interests). Content-based filtering makes assumptions based on previous customer product history – recommending items similar to those they have already purchased. Customers are more likely to upgrade or repurchase products that align with their needs or preferences. This is why telecom companies have implemented this ML method to increase revenue.  

 

Anomaly Detection & Fraud 

 Due to the fact that the industry attracts a significant number of users every day, this brings high chances of fraudulent opportunities. Fraud, such as fake profiles, theft, illegal authorisation, and account cloning, have a negative impact on the customer-company relationship.  

This is why telecommunications companies are implementing unsupervised Machine Learning algorithms to spot abnormal characteristics or activity in customer behaviour. 

By analysing past data of customer behaviour, ML algorithms are able to visualise and present these anomalies to analysts in real time. This is especially efficient as it allows telecommunications companies to alert their customer of suspicious activity almost immediately thus protecting the customer-company trust relationship. 

 

Customer Segmentation for Effective Marketing 

Successful marketing in any industry relies on accurately segmenting and targeting groups according to their preferences and characteristics.  

 

The key to success for the telecommunication companies is to segment their target markets and target marketing content accordingly to each group. This golden rule is relevant to the various areas of business. Machine Learning algorithms can create highly accurate customer segments based on shared customer characteristics. This allows telecommunications companies to better strategise and reach customers with more relevant marketing material (and increase their chance of a sales conversion)  

 

(Did you know that ML customer segmentation can be done in real-time. As customers change preferences and patterns, they are re-segmented to different groups based on whichever characteristics are most prominent. This means targeting the customer with irrelevant marketing material is less likely to happen – and saves time for those who once needed to re-segment groups manually).  

 

 

Optimised Product Development 

Companies in the telecommunications industry need to provide their customers with products that satisfy their needs and wants in order to continue to keep customers loyal and to generate revenue. Machine Learning algorithms can analyse data across different departments to ensure customer feedback is taken into account to provide the most customer fulfilling products are created. By analysing data such as customer feedback, marketing intelligence, and product stock, ML algorithms can present the most data-driven product suggestions for development. 

 

Conclusion 

Data science and its benefits in the telecommunications industry is not a new occurrence. However, Machine Learning has taken efficiency within the industry to the next level. Those companies that have implemented ML in their processes are experiencing fewer issues to resolve, control or occur from happening at all. 

With the right partner and expertise, telecommunications companies are experiencing these benefits for themselves. Intellify is AWS’ Partner of the Year in Data, Analytics, & Machine Learning and has worked with telecommunications companies in the past on bringing Machine Learning capabilities and culture to their organisation. Contact us to learn more about how we could help you do the same based on your own requirements.   

 

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.

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/.

 

AWS Blog Events In the Media

Visit Intellify at the AWS Summit Sydney 2019

Intellify is proud to announce that we will be exhibiting at this year’s AWS Summit at the International Convention Centre (ICC), Sydney. The Summit aims to bring together the cloud computing community to enhance and celebrate knowledge about creating future-ready businesses.

Explore how cloud technology can help businesses innovate, reduce costs and promote efficiency at large. The event is open to cloud users of all levels! It’s a free event designed to inspire new skill and application development as well as educate all those interested about the newest technologies and experts who have built their solutions on AWS.

 

What to Expect:

Day 1 (30th of April) is Amazon Innovation Day. The agenda focuses on first-hand experience and insights on how Machine Learning, Robotics, and AI are changing the way we do business an life our daily lives.

Day 2 & 3 (1st and 2nd of May) is the AWS Summit. These days include exciting workshops, networking, and presentations about the latest in cloud vision.

 

Intellify will be exhibiting on all three days so come ask us some questions and join the cloud computing conversation. See you there!

Book your ticket for the Summit before its too late: https://pages.awscloud.com/anz-summit-2019.html

For more information about the Summit follow this link: https://aws.amazon.com/events/summits/sydney/

Also, keep up to date with Intellify: http://www.intellify.com.au/

AWS Blog

re:Invent 2018 – Machine Learning and AI Take Centre Stage

This year’s AWS re:Invent was full of new releases and updates, cutting-edge tools, innovative products, and valuable resources to help take advantage of the rapid pace of growth and development in cloud services. For us at Intellify, we were most excited by the focus on building out Machine Learning tools and environments. SageMaker has gotten many new updates, making optimisation, recommendation systems, reinforcement learning (RL) and forecasting models deployable by more users than ever. Tellingly, much of Andy Jassy’s keynote was dedicated to announcing the massive new focus on Machine Learning and AI from AWS, and we are gearing up to help our customers take full advantage of the benefits.

5 Key Takeaways

While we continue to digest the content coming from re:Invent – it is a huge week after all – and begin to work with the expansions to SageMaker, Reinforcement Learning, Elastic Inference, Marketplace, and other ML-based toolkits, here are some of the major takeaways we had from the keynote presentations:

New SageMaker offerings: Ground Truth, Neo, Reinforcement Learning. We have been developing and deploying for our customers on SageMaker (see here, and here) since its first release and have found it a powerful environment for ML projects. AWS is expanding SageMaker even more, including:

Ground Truth: adding the capability to outsource data labelling to either human agents through Mechanical Turk, third parties, or in-house; or a combination of human and intelligent systems. Accurate labelling of data is crucial to successful implementation and integration of data sets for ML, and we can see ourselves taking advantage of this new service to ensure solution accuracy.
Reinforcement Learning (RL): a new environment dedicated to RL algorithms and compute power allowing deployment of RL-based ML solutions, which have much different requirements to supervised and unsupervised learning techniques.

 

Allowing ML vendors to share their cutting-edge ML algorithms and solutions in AWS Marketplace. Not only will this greatly expand the options available to those seeking pre-built ML solutions, it will also enable us to share some of our expertise on the platform, and more rapidly deploy flexible customer solutions using an array of customisable tools.

Significant expansions on Elastic Inference, and the arrival of AWS’ specialised ML chip: Inferentia. This is particularly exciting for us to see the expansion of flexible compute power for ML. Inference requires significant power when running our models, but for relatively sparse periods of time. The advantage of elastic inference will be in providing that high compute when it is needed, rather than paying for the availability of high compute all month. We’re sure this will make our future inference-based ML projects more cost-effective, while retaining the compute power we need for successful deployment.

The Introduction of DeepRacer and DeepRacer League.We loved this! Based on the new RL environment in AWS, watch out for an Intellify team flexing their ML and data science muscles in the League!

Amazon Textract, Personalize, and Forecast. This past year, customers have shown a lot of interest in document recognition/parsing; recommender systems, especially in ecommerce and customer experience-focused businesses; and time series modelling and forecasting. There are so many vital applications of these ML-based tools, and we can’t wait to get on board with Textract, Personalize, and Forecast to take them to a whole new realm of customers seeking the benefits of AI/ML.

Our Thoughts

AI and ML are hugely on the rise for both organisations and individuals. AWS’ new suite of releases and expansions to SageMaker, RL, Inference, and Marketplace will only help this field grow at an even faster rate. We’ve been deploying on SageMaker since its release, and these tools will only help us expand what we can offer to customers in terms of cutting edge, competitive AI/ML solutions.

Get in Contact

If you or your organisation are looking to take advantage of AI/ML and its enormous opportunities to boost revenue, create operational efficiency, and enhance competitiveness, please get in contact via phone (02 8089 4073) or email (info@intellify.com.au). We are AWS Consulting partners for Machine Learning, with a range of projects already completed across SageMaker and AWS cloud services.

Blog In the Media

In the Media: [This is My Code] on AWS

Our very own Kale Temple was featured in an AWS segment on their Youtube channel. This video featured an in-depth discussion on Particle Swarm Optimisation using Amazon SageMaker. In an age where companies are looking for better ways to optimise pricing, discounts, and offers across their product portfolios, environments like SageMaker are game changing. Take a look at the video below for a detailed look at this incredible process.

We’re thrilled to be a part of the conversation involving machine learning and AWS tools, including SageMaker. We look forward to the exciting new things that will come out of re:Invent 2018.