According to a recent report by Harnham, a leading data and analytics recruitment agency in the UK, the demand for ML engineering roles has been steadily rising over the past few years.

Recently, there’s been a shift towards MLOps professionals who possess the skills to bridge the gap between data scientists and data engineers, thereby optimising the deployment of ML models.

Harnham’s report provides comprehensive insights into the salaries and day rates of various data science roles across the UK.

Technical leads/managers in computer vision, data science, deep learning & AI, ML engineering, MLOps, and natural language processing are earning annual base salaries ranging from £44,000 to £120,000, depending on experience and location.

In addition to competitive compensation, data science professionals are seeking specific benefits to enhance their job satisfaction.

The top five desirable benefits include remote working options, bonuses, health insurance, flexible working hours, and shares. These perks play a crucial role in attracting and retaining top talent in the data science sector.

The report also sheds light on some critical trends and statistics in the industry.

25 percent of professionals cited a non-competitive salary/rate as the top reason for leaving a role, followed closely by a lack of career progression (24%) and a “better opportunity” coming along (22%).

The number of female professionals in the field has increased from 22 percent last year, indicating a positive shift towards greater gender diversity in data science.

While the field of data science continues to evolve rapidly, professionals are keen to explore new opportunities.

One finding from the report reveals that data science professionals are the most likely to leave their current roles if the right opportunity arises. The ongoing talent shortage means that relevant expertise is in high demand and many opportunities are available.

Advancements in AI and ML are transforming the landscape and creating exciting new job opportunities. As the demand for data professionals continues to surge, companies must adapt to remain competitive in attracting and retaining top talent in this thriving field.

For more information and in-depth data on data science salaries and trends in the UK, refer to the Harnham Data & AI Salary Guide for 2023.

(Photo by Ben Rosett on Unsplash)

See also: Universities want to ensure staff and students are ‘AI-literate’

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

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This strategic partnership aims to empower AI teams with powerful computing resources on-demand, enhancing their capabilities and streamlining their operations.

The collaboration combines the strengths of three industry leaders: Graphcore, renowned for its Intelligence Processing Units (IPUs) hardware; UbiOps, a powerful machine learning operations (MLOps) platform; and Gcore Cloud, known for its robust cloud infrastructure.

By leveraging these cutting-edge technologies, Gcore Cloud presents AI teams with a seamless experience, making it effortless to utilise IPUs for specific AI tasks while benefiting from UbiOps’ MLOps features such as model versioning, governance, and monitoring.

Andre Reitenbach, CEO at Gcore, commented:

“The collaboration between Gcore, Graphcore, and UbiOps brings a seamless experience for AI teams. This enables effortless utilisation of Gcore’s cloud infrastructure with Graphcore’s IPUs on the UbiOps platform. This means that users can take advantage of the exceptional computational capabilities of IPUs for their specific AI tasks. Also, users can leverage UbiOps’ out-of-the-box MLOps features such as model versioning, governance, and monitoring.

These features help teams to accelerate time to market with AI solutions, save on computing resource costs, and efficiently use them with on-demand hardware scaling. We’re thrilled about this partnership’s potential to enable AI projects to succeed and reach their goals.”

To showcase the significant advantages of IPUs over other devices, Gcore conducted benchmarking tests on three different compute resources: CPUs, GPUs, and IPUs.

Gcore trained a Convolutional Neural Network (CNN) – a model designed for image analysis – using the CIFAR-10 dataset containing 60,000 labelled images, on these devices.

The results were striking, with IPUs and GPUs significantly outperforming CPUs in training speed. Even with minimal optimisation, IPUs demonstrated a clear advantage over GPUs, enabling even shorter training times:

This collaboration offers AI teams unparalleled access to powerful hardware tailor-made for demanding AI and ML workloads.

By integrating Gcore Cloud, Graphcore’s IPUs, and UbiOps’ MLOps platform, teams can work more efficiently, cost-effectively, and scale their hardware as needed. The combined offering enables AI projects to realise their full potential, driving innovation and progress in the AI industry.

With this strategic alliance, Gcore, Graphcore, and UbiOps are poised to make advanced resources more accessible and empower AI teams worldwide to achieve their goals.

(Photo by Nathan Dumlao on Unsplash)

See also: Damian Bogunowicz, Neural Magic: On revolutionising deep learning with CPUs

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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AIs are beneficial tools but are indiscriminate in also providing assistance to individuals and groups that set out to cause harm.

The researchers’ study into offensive AI used both existing research into the subject in addition to responses from organisations including Airbus, Huawei, and IBM.

Three core motivations were highlighted as to why an adversary would turn to AI: coverage, speed, and success.

While offensive AI threats come in many shapes, it’s the use of the technology for impersonation that has both academia and industry highly concerned. Deepfakes, for example, are growing in prevalence for purposes ranging from relatively innocuous comedy to the far more sinister fraud, blackmail, exploitation, defamation, and spreading misinformation.

Similar campaigns in the past using fake/manipulated content has been a slow and arduous process with little chance of success. Not only is AI making the creation of such content easier but it’s also meaning that organisations can be bombarded with phishing attacks which greatly increases the chance of success.

Tools such as Microsoft’s Video Authenticator are helping to counter deepfakes but it will be an ongoing battle to keep up with their increasing sophistication.

Back when Google’s Duplex service was announced – which sounds like a real human to book appointments on a person’s behalf – concerns were raised that similar technology could be used to automate fraud. The researchers expect bots to gain the ability to make convincing deepfake phishing calls.

The researchers also predict an increased prevalence of offensive AI in “data collection, model development, training, and evaluation” in the coming years.

Here are the top 10 offensive AI concerns from both the perspectives of industry and academia:

Very few organisations are currently investing in ways to counter, or mitigate the fallout, of an offensive AI attack such as a deepfake phishing campaign.

The researchers recommend more research into post-processing tools that can protect software from analysis after development (i.e anti-vulnerability detection) and that organisations extend the current MLOps paradigm to also encompass ML security (MLSecOps) that incorporates security testing, protection, and monitoring of AI/ML models.

You can find the full paper, The Threat of Offensive AI to Organizations, on arXiv here (PDF)

(Photo by Dan Dimmock on Unsplash)

Find out more about Digital Transformation Week North America, taking place on November 9-10 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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Vertex was announced during this year’s virtual I/O developer conference and somewhat breaks from Google’s tradition of using its keynote to focus more on updates to its mobile and web development solutions. Google announcing the platform during the keynote shows how important the company believes it to be for a wide range of developers.

Google claims that using Vertex enables models to be trained with up to 80 percent fewer lines of code when compared to competing platforms.

Bradley Shimmin, Chief Analyst for AI Platforms, Analytics, and Data Management at Omdia, said:

“Data science practitioners hoping to put AI to work across the enterprise aren’t looking to wrangle tooling. Rather, they want tooling that can tame the ML lifecycle. Unfortunately, that is no small order.

It takes a supportive infrastructure capable of unifying the user experience, plying AI itself as a supportive guide, and putting data at the very heart of the process — all while encouraging the flexible adoption of diverse technologies.”

Vertex brings together Google Cloud’s AI solutions into a single environment where models can go from experimentation all the way to production.

Andrew Moore, VP and GM of Cloud AI and Industry Solutions at Google Cloud, said:

“We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds, and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production.

We are very proud of what we came up with in this platform, as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.”

Vertex provides access to Google’s MLOps toolkit which the company uses internally for workloads involving computer vision, conversation, and language.

Other MLOps features supported by Vertex include Vizier, which increases the rate of experimentation; Feature Store to help practitioners serve, share, and reuse ML features; and Experiments to accelerate the deployment of models into production with faster model selection.

Some high-profile companies were given early access to Vertex. Among them is ModiFace, a part of L’Oréal that focuses on the use of AR and AI to revolutionise the beauty industry.

Jeff Houghton, COO at ModiFace, said:

“We provide an immersive and personalized experience for people to purchase with confidence whether it’s a virtual try-on at web check out, or helping to understand what brand product is right for each individual.

With more and more of our users looking for information at home, on their phone, or at any other touchpoint, Vertex AI allowed us to create technology that is incredibly close to actually trying the product in real life.”

ModiFace uses Vertex to train AI models for all of its new services. For example, the company’s skin diagnostic service is trained on thousands of images from L’Oréal’s Research & Innovation arm and is combined with ModiFace’s AI algorithm to create tailor-made skincare routines.

Another firm that is benefiting from Vertex’s capabilities is Essence, a media agency that is part of London-based global advertising and communications giant WPP.

With Vertex AI, Essence’s developers and data analysts are able to regularly update models to keep pace with the rapidly-changing world of human behaviours and channel content.

Those are just two examples of companies whose operations are already being greatly enhanced through the use of Vertex. Now the floodgates have been opened, we’re sure there’ll be many more stories over the coming years and we can’t wait to hear about them.

You can learn how to get started with Vertex AI here.

(Photo by John Baker on Unsplash)

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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Algorithmia’s 2021 Enterprise Trends in Machine Learning report features the views of 403 business leaders involved with machine learning initiatives.

Diego Oppenheimer, CEO of Algorithmia, says:

“COVID-19 has caused rapid change which has challenged our assumptions in many areas. In this rapidly changing environment, organisations are rethinking their investments and seeing the importance of AI/ML to drive revenue and efficiency during uncertain times.

Before the pandemic, the top concern for organisations pursuing AI/ML initiatives was a lack of skilled in-house talent. Today, organisations are worrying more about how to get ML models into production faster and how to ensure their performance over time.

While we don’t want to marginalise these issues, I am encouraged by the fact that the type of challenges have more to do with how to maximise the value of AI/ML investments as opposed to whether or not a company can pursue them at all.”

The main takeaway is that AI budgets are significantly increasing. 83 percent of respondents said they’ve increased their budgets compared to last year.

Despite a difficult year for many companies, business leaders are not being put off of AI investments—in fact, they’re doubling-down.

In Algorithmia’s summer survey, 50 percent of respondents said they plan to spend more on AI this year. Around one in five even said they “plan to spend a lot more.”

76 percent of businesses report they are now prioritising AI/ML over other IT initiatives. 64 percent say the priority of AI/ML has increased relative to other IT initiatives over the last 12 months.

With unemployment figures around the world at their highest for several years – even decades in some cases – it’s at least heartening to hear that 76 percent of respondents said they’ve not reduced the size of their AI/ML teams. 27 percent even report an increase.

43 percent say their AI/ML initiatives “matter way more than we thought” and close to one in four believe their AI/ML initiatives should have been their top priority sooner. Process automation and improving customer experiences are the two main areas for AI investments.

While it’s been all good news so far, there are AI deployment issues being faced by many companies which are yet to be addressed.

Governance is, by far, the biggest AI challenge being faced by companies. 56 percent of the businesses ranked governance, security, and auditability issues as a concern.

Regulatory compliance is vital but can be confusing, especially with different regulations between not just countries but even states. 67 percent of the organisations report having to comply with multiple regulations for their AI/ML deployments.

The next major challenge after governance is with basic deployment and organisational challenges. 

Basic integration issues were ranked by 49 percent of businesses as a problem. Furthermore, more job roles are being involved with AI deployment strategies than ever before—it’s no longer seen as just the domain of data scientists.

However, there’s perhaps some light at the end of the tunnel. Organisations are reporting improved outcomes when using dedicated, third-party MLOps solutions.

While keeping in mind Algorithmia is a third-party MLOps solution, the report claims organisations using such a platform spend an average of around 21 percent less on infrastructure costs. Furthermore, it also helps to free up their data scientists—who spend less time on model deployment.

You can find a full copy of Algorithmia’s report here (requires signup)

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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