Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

The motor industry has always been right at the forefront of innovation, and this is also true when it comes to embracing machine learning and AI. This week’s guest on the Data Futurology podcast is Leonard Aukea, the Head of Machine Learning, Engineering & Operations at Volvo, who shares with us insights into what the global vehicle giant is doing to bring value to the operations chain across the company.

For Aukea, it has been a story of establishing best processes across the organisation. He said that one of his first priorities was to bring the various data science teams together to minimise the impact of siloing, and encourage the machine learning practitioners to adopt software engineering principles. This might not be immediately comfortable to them, but as Aukea said, ML experts are smart people working on complex problems, and facilitating an open-minded approach across the organisation is key to driving long-term success.

“You need to start simple,” he said. “Think about processes, ways of working, and the cultural aspects, and try to fit tooling and infrastructure along that kind of endeavour. You don’t need to choose the most extreme state-of-the-art tools.”

At one point, Aukea noted, things being pushed into production were becoming unmanageable, so he and his teams took a step back and reset. “We went back and decided to focus on first principles,” he said. “We evangelised these first principles to develop good ways of working, and then adopted the infrastructure and tooling towards building AI on top of that.”

Ultimately, Aukea said, quality comes from the processes, rather than the technology. There are, of course, technical challenges, but for anyone aiming to get true value out of machine learning, the focus needs to be on the processes.

Aukea then explains how, with those processes in place, he and his team have been able to start delivering deep and valuable insights. For more on how Aukea was able to structure Volvo for success with machine learning in operations, tune in to the podcast!

Enjoy the show!

Thank you to our sponsor, Talent Insights Group!

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Read the full podcast summary here.

In today’s episode, we have Romina Sharifpour, Machine Learning Specialist at Amazon Web Services (AWS).

Operationalising machine learning models, particularly scaling MLOps capability across teams within an organisation is a difficult feat.

Join Romina to find out how you can easily accelerate your MLOps journey using Amazon SageMaker Pipelines. You'll gain insights into how AWS customer Carsales keeps up with increased demand in building and productionising AI models, and their strategy to democratise AI across the whole development teams. This allows any developer to be a citizen data scientist and ML engineer by leveraging Amazon SageMaker.

Enjoy the show!

If you want to learn more about building modern applications on AWS and attend a virtual conference, just google “AWS Innovate” or click the link below.

https://aws.amazon.com/events/aws-innovate/apj/modern-apps/

Thank you to our sponsor, Talent Insights Group!

Join us in Melbourne for Scaling AI with MLOPS:  https://www.datafuturology.com/mlops

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

Read the full podcast episode summary here.

This week on the Data Futurology podcast, we have the special privilege of talking to the top analytics leader in Australia, according to IAPA (the Institute of Analytics Professionals of Australia).

Brad Petry, the Executive Director – Operations, Insights, and Digital Channels at the Department of Jobs, Precincts and Regions in the Victorian Government, was awarded this accolade for his work in leveraging AI and machine learning to overcome biases in the recruitment process. He spends time on the podcast this week talking about what that means for the department, and the implications it has for recruitment more broadly.

Over the past 18 months, Petry has been driving a digital transformation program across the department, something that has been made even more challenging because it has happened through the pandemic and because the data that he and his team handle is needed on a daily basis. There was no room for downtime or mistakes while the transformation was executed.

At the same time, there was an enormous opportunity within the department to leverage automation and AI with data – in many cases for the first time – to improve the reliability of the data and productivity across the department.

As Petry says, the key to success is to remember that it’s the data that’s the important element, rather than the software or context that the data is held and analysed within. “When we started, we said to ourselves that the thing we knew, and what was going to persist, was the data,” he said. “The technology and programs will come and go, but the data is something that will always be there and everything comes back to the data.”

For a deep dive into driving a transformation agenda with data, tune in to this week’s podcast!

Enjoy the show!

Thank you to our sponsor, Talent Insights Group!

Connect with Brad  https://www.linkedin.com/in/brad-petry/

See Brad’s presentation at Scaling AI with MLOPS:  https://www.datafuturology.com/mlops

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

Read the full podcast summary here.

This week on the Data Futurology podcast, we talk transformation and the importance of having data engineers to guide the strategy and agenda. To provide expert insights into this topic, we have the pleasure of hosting Richard Glew, Chief Technology Officer, and Natalia Dronova, Senior Data Analyst from Aginic.

Aginic is a consultancy that assists organisations with their transformation goals, providing expertise in analytics, agile, and the digital experience.

Transformation remains a challenging goal, with research showing that most projects fail. Glew and Dronova discuss some of the reasons for this, which are many and varied, but according to Dronova, one of the big ones is that organisations make mistakes in their haste to transform quickly.

“One of the challenges with transformation are the people that want everything done within six or eight months,” she said. “They want it now, and they’re finding shortcuts to try and make it happen that are hurting them in the long run. Then, a few years later, when you look at their stack, it’s all over the place.”

Dronova and Glew then go in-depth in discussing the structural problems that can affect transformation efforts, as well as the cultural problems across organisations – the impact that a focus on data governance can have on projects, for example, and why organisations need to move to a position of data enablement.

Finally, the two also discuss the role of the data engineer. As Glew said, traditionally the role has lagged behind that of the software engineer, but with more focus being placed on their role in transformation, the rapidity with which the role is evolving, and the relative scarcity of engineers resulting in higher salaries, now is a great time to consider a career in data engineering. “With the state of data engineering today, it’s the best time to get into it, because it’s still evolving and innovating really quickly.” Glew said.

Tune in to this deep and insightful discussion to learn more about the dynamics behind transformation and the role of the data engineer.

Enjoy the show!

Thank you to our sponsor, Talent Insights Group!

Connect with Richard  https://www.linkedin.com/in/rlglew/

Connect with Natalia  https://www.linkedin.com/in/nataliadronova/

Learn more about Aginic  https://aginic.com/

Join us in Melbourne for Scaling AI with MLOPS:  https://www.datafuturology.com/mlops

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

Read the full podcast episode summary here.

It’s safe to say that most people in data science want to do the right thing. However, AI ethics cannot just be an afterthought done in the service of regulatory obligations. It needs to be baked into the way the organisation looks at data, at every level.

How organisations can achieve that is the focus of our latest podcast, with Natalie Rouse, General Manager of Eliiza and Brendan Nicholls, the Practice Lead, Machine Learning Engineering, joining us to discuss the topic. Eliiza is a data consultancy, and Rouse and Nicholls are right in the trenches with their customers.

There are many questions that organisations should be asking of their data, particularly with regards to how to ensure that it’s free of bias and that it’s being used accurately. As Nicholls and Rouse discuss on the podcast, the questions range from how the data’s being collected, where it came from, whether it accurately reflects demographics, and what the range of uses of the data is, based on the collection policy.

These are all relatively straightforward things to think about, but nonetheless, they’re often overlooked, especially within teams that are highly technically orientated. As the Eliiza team acknowledge, one of the challenges in data science is that teams are technical and want to “reduce” everything to numbers that can be measured. However, to fully embrace ethical AI, it becomes important to embrace the ambiguities and the non-measurable side of the discussion as well.

Eliiza is deeply engaged in helping its customers achieve this understanding of ethical AI, and regularly hosts monthly MLOps meetups in Melbourne through the MLOps Community, which hosts meetups around the world - 22 different locations - to facilitate knowledge exchange. It will also be holding a hackathon around healthcare shortly to encourage ethical AI in that area.

Finally, Nicholls will be presenting at the Scaling AI with MLOps event in Sydney on October 25 on why Ethical AI matters. Tune into this podcast and drop into his presentation to gain a deep understanding on why ethical AI is not just an obligation but, when done right, an opportunity.

Enjoy the show!

Thank you to our sponsor, Talent Insights Group!

Links

Eliiza https://eliiza.com.au/

Hackathon: https://www.intellihq.com.au/medical-datathon/

AI Australia Podcast: https://eliiza.com.au/learn/ai-australia-podcast/

MLOPs community: https://www.meetup.com/en-AU/melbourne-mlops-community1/

Connect with Natalie: https://www.linkedin.com/in/natalie-rouse-7115b15a/

Connect with Brendan: https://www.linkedin.com/in/nichollsbrendan/

Read the full podcast episode summary here.

Healthcare is an industry that stands to benefit a great deal from data and analytics. At the same time, the sensitivity of the data in the sector is extreme and how organisations manage that data is critical.

Yalchin Oytam, the Head Of Clinical Insights And Analytics at South Eastern Sydney Local Health District (SESLHD) is right in the thick of the discussion. He joins us on the Data Futurology podcast to talk through both the challenge and opportunity.

One of the big challenges that the Australian health system faces, Oytam said, was that primary healthcare was handled by the federal government and secondary care was handled by the states. How the sharing of data between these two is handled is critical to maintaining the customer experience with their health care. More importantly, if data can be leveraged to improve outcomes in primary care, it can reduce the burden on secondary care. Oytam gives the example of diabetic patients being diagnosed and accurately cared for by their GPs have a much lower risk of an unplanned hospital visit.

“When you keep people out of hospital, it also means that they are generally healthier, more productive, and happier. In human terms, the benefit of this goes beyond money,” he said.

The other big opportunity in healthcare is the use of data modelling to personalise healthcare services. Modelling can be used to detect warning signs and risk factors, and more proactively communicate with patients. In the longer term this can result in earlier diagnosis and better risk management – and it’s just one area where this approach to data can lead to meaningful change. “The question is how do we best manage our climate, while also maximizing the quality of life for human beings, and other life forms,” Oytam said.

“A better world certainly is possible.”

Tune in to the podcast for an in-depth discussion on how data can deliver better health and lifestyle outcomes for us all.

Enjoy the show!

Join us in Melbourne for Scaling AI with MLOPS:  https://www.datafuturology.com/mlops

Thank you to our sponsor, Talent Insights Group!

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

Read the full podcast episode summary here.

Kevin Ross has had more than 20 years of experience in using data, science and analytics to lead decision-making. Now, as the CEO at Precision Driven Health, and Advisory Board Chair at the NAOI (Natural, Artificial and Organisational Intelligence) Institute, he is placed right at the heart of the data discussion in New Zealand.

He joins us on the Data Futurology podcast this week to discuss the evolving role of data in healthcare, and how it has broadened to really start to embrace personalisation. “We have this fantastic opportunity other there where we know that health doesn’t make use of all the data that’s out there,” he said. “Imagine what you could achieve if you added the computational power of AI into diagnosis and healthcare. The potential is amazing for guiding people to understand themselves and their outcomes.

“It is being driven by consumers looking for health to provide them the same services that they can get elsewhere.”

Ross acknowledges that change is coming slowly, but as it is being driven by customer demand, the change is inevitable. Those healthcare organisations that can adjust will prove to be the disruptive forces in the years ahead.

Elsewhere in this wide-ranging podcast, Ross also discusses the advances in data capture technique, and the implication that has for better analytics and AI. He also talks through the privacy and ethical implications of data use in healthcare, and how data outcomes can be understood and measured within healthcare.

Healthcare is one of the most fascinating sectors when it comes to data, analytics, and patient outcomes, and we’re only scratching the surface of it. Tune in to this in-depth conversation with Ross to get a sense for what’s coming next.

Enjoy the show!

To learn more about Precision Driven Health:  https://precisiondrivenhealth.com/

Join us in Melbourne for Scaling AI with MLOPS:  https://www.datafuturology.com/mlops

Thank you to our sponsor, Talent Insights Group!

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

Read the full podcast episode summary here.

NVIDIA is best known for its production of GPUs and APIs that enable high-performance computing, supercomputing, and power some of the most intense applications across the world. Unsurprisingly, the company is deeply involved in AI, and on this week’s podcast, the company’s VP of Solutions Architecture and Engineering, Marc Hamilton, joins us to share the company’s unique insights into the field.

Hamilton explains how NVIDIA’s innovative AI Factory concept allows it to introduce efficiencies into the data gathering process. He uses the example of self-driving cars to just how effective the NVIDA approach is. To manually collect all the data on all the roads in the world, the researchers would need to travel 11 billion miles. However, NVIDIA can leverage simulations of roads to “teach” the AI powering these cars synthetically.

Hamilton then describes the fascinating advancements of digital twins – a technology idea that has been around for decades but only just now supported by powerful enough technology to handle the AI and other processing requirements for it. This, Hamilton says, can be used for everything from workplace layout simulations that “test” an environment to make sure it’s safe to work in before real humans do so, through to creating a digital “twin” of the earth as a way of testing the impact of climate change “700 or 7,000” days down the track.

“It's going to be many years before we're done, but we're already making some interesting project progress and seeing some interesting early signs of future success,” he said.

With AI certain to be critical to how humanity grapples with the increasingly complex challenges facing it into the future, it is companies like NVIDIA that will at the forefront of our response. Tune in to learn more about the very cutting edge of AI.

Enjoy the show!

About NVIDIA

Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics and ignited the era of modern AI. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry. More information at https://nvidianews.nvidia.com/

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Join us in Melbourne for Scaling AI with MLOPS:  https://www.datafuturology.com/mlops

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Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

Read the full podcast episode summary here.

This week we are thrilled to welcome Harjot Singh, who had been the CDO of RAC in WA until very recently. Singh is a true expert of and champion for transformation in the workplace, and has deep insights on data strategy and data governance to share.

“Data drives a digital transformation in any organisation.” Singh said. “Everyone wants to jump on the bandwagon around digital transformation, but most organisations struggle to understand where to start.

As Singh explains on the podcast, there is a specific order with which organisations need to approach transformation. It starts with data, which organisations should already be investing in because monetising data is one of the biggest opportunities in business.

From there the data can be leveraged into machine learning and, eventually, AI.

Related to this, however, Singh also mentions that organisations need to better understand the business drivers behind what they are doing with data and the digital transformation journey. “I wrote an article on LinkedIn that was around the five common mistakes to make in digital transformation,” Singh said. “If people think that digital transformation is only about technology transformation, it’s going to fail.”

From there, Singh and host, Felipe Flores, discuss the impact of regulation in Australia on innovation, how companies are working within those challenges, and how various highly regulated sectors – including insurance and financial services – are finding new opportunities.

Ultimately, however, as Singh says, it all comes back to data. “I say data and digital in the same sense, because I treat them as two sides of the same coin – one is incomplete without the other. Data is the bullet and digital is the gun to launch the bullet – without both you’re not going to have much of an effect.”

For deep insights on the strategy and opportunity behind digital transformation, and the deep role of data in it, check out the full podcast!

Enjoy the show!

Join us in Melbourne for Scaling AI with MLOPS:  https://www.datafuturology.com/mlops

Thank you to our sponsor, Talent Insights Group!

Read the full podcast episode summary here.

GEEQ (Geeks With EQ) is a non-for-profit with an important mission: helping women get into IT and boosting the diversity of IT companies in Australia. To this day less than 20 per cent of Australia’s IT workforce are women, and this has far-reading implications, from the bias that gets built into technology itself through to the depth of innovative thinking available in the space.

The two founders of GEEQ, Azadeh Khojandi and Katrin Schmidt, join us on this special podcast to discuss the work that they’re doing, and the traction that diversity is getting across Australian corporate spheres.

“It’s important for us that we’re not only bringing women into the workforce, but helping them to grow and get the promotions, more responsibilities, and the fulfilment they deserve,” Khojandi says in the podcast. GEEQ is more than an advocacy group. It focuses heavily on skills and mentoring, providing women with books on leadership and managing events to assist with knowledge transfer.

On the side of advocacy, the two are focused on helping the Australian business community recognise biases in the hiring process and how to mitigate against that, Katrin says on the podcast. “It’s really difficult to have awareness of your own unconscious bias,” she says. “It’s like stopping and thinking to yourself, ‘what did I just do?’. The first step is a change in awareness. You don’t have to jump to conclusions, but you do need to watch and be aware.”

Azadeh and Katrin are sponsors at the upcoming Data Engineering Summit and will be hosting a luncheon. It will be a rare opportunity to talk directly to some of the speakers from the summit and discuss how to tackle the ongoing challenge of diversity.

In the meantime, tune into this in-depth podcast, and hear from the experts about why diversity is a $60 billion opportunity for Australian businesses.

Learn more about GEEQ: https://www.linkedin.com/company/geeq-australia/

Thank you to our sponsor, Talent Insights Group!

Read the full podcast episode here.