In an interview at the recent AI & Big Data Expo, Streambased founder and CEO Tom Scott outlined the company’s approach to enabling advanced analytics on streaming data. At the foundation of Streambased’s offering is Apache Kafka, an open-source event streaming platform that has been widely adopted by Fortune 500 companies.

“Where [Kafka] falls down is in large-scale analytics,” explained Scott. While Kafka reliably transports high-volume data streams between applications and microservices, conducting complex analytical workloads directly on streaming data has historically been challenging. 

Streambased adds a proprietary acceleration technology layer on top of Kafka that makes the platform suitable for the type of demanding analytics use cases data scientists and other analysts want to perform.

Because these continuously flowing event streams power critical operational systems and core business functions, data quality must already meet high standards in terms of accuracy, timeliness, and structure. By leveraging these existing Kafka data pipelines, Streambased ensures its analytical capabilities have access to up-to-date, clean and well-organised data.

Use cases that showcase the power of Streambased’s approach include fraud detection in financial services. If an anomalous transaction occurs, analysts can quickly query similar or related transactions to investigate – which would be difficult and inefficient to accomplish with a pure streaming architecture. Streambased’s optimization for analytical interactivity enables users to rapidly gather contextual insights without disrupting their workflow.

The convergence of operational and analytical data platforms represents an impactful trend that Streambased calls the “streaming data lake” movement

“I think we are at the period of the streaming data lake movement. And by a streaming data lake, I mean a complete convergence between data systems that we use for analytical purposes and data systems that we use for operational purposes,” explains Scott.

Recent enhancements like infinite data retention in Kafka and native streaming analytics services lay the foundation for this new paradigm. For now, Streambased remains focused on empowering business analysts through frictionless self-service access to granular real-time data, without requiring changes to existing tools and processes.

You can watch our full interview with Tom Scott below:

(Photo by Robert Zunikoff on Unsplash)

See also: AI & Big Data Expo: Unlocking the potential of AI on edge devices

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 comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

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

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The AI & Big Data Expo stood out for its great mixture of speakers, not only targeting people working within data, but making the topics feel completely accessible to somebody like me, who isn’t a data scientist by background. As the CEO of an infrastructure charity, I know our beneficiaries don’t necessarily work closely with data, or hold it at the forefront of their minds, so it was very interesting to see how AI and big data impacts a diverse range of different sectors, and how they employ and deploy different strategies to work with its new challenges. 

I especially enjoyed the talks focused on ethics and governance, which resonate with our beneficiaries and the challenges that they face. What’s interesting is that there seems to be a real drive to ensure that ethics is baked into AI strategies moving forward. It’s very heartening that ethics is being talked about at this early stage – that it isn’t being ignored as it may have been when past technologies developed as quickly.

One talk tackled governance, and how governments are still playing catch up. There seems to be an overarching feeling that AI has to be regulated, but whether the regulation that people want is possible is the next big question. Can it be regulated, and how? Is the EU act going to work well, and is the legislation in the US going to be effective, or will it be watered down? What systems do you use, and therefore what do you endorse? Is this the right thing to do, is this the right way to deploy this sort of power, and what would the fallout be if we did?

As a charity working to serve other charities, safeguarding is a huge area of concern for us, so it’s good to know that the mainstream is also thinking about transparency. AI providers and tools have not yet done enough to flag the potential risks for third sector organisations that, for example, routinely handle sensitive data about vulnerable individuals. This could result in a number of issues for charities using AI for the first time, not least data breaches. We have already seen the misuse of AI to replace services that are still necessarily led by humans – in one example, a chatbot that replaced a manned helpline gave people with eating disorders dangerous dieting advice. Tech leaders and governments must take the lead by demonstrating responsible approaches and creating frameworks around safeguarding and risks. There will always be bad actors in this space, but there seems to be a ‘coalition of the willing’ that wants to ensure AI is continually safe, not just for those with enough resources to create their own safeguarding.

As these debates continue and the technology develops apace, it’s so important that there are spaces in which the third sector can be heard alongside private or statutory organisations. At the AI & Big Data Expo, we were able to showcase our work as a representative voice, and garner enthusiasm in the ‘data for good’ movement. We made some fantastic connections with others, as a result of realising how aligned our overarching missions are. Testament to that was the enthusiasm of our audience, asking our wonderful volunteers Adam and Alvaro tons of questions, and chatting to us in person afterwards. We are thrilled to have been part of these conversations.

Finally, we want to say a big thank you to the organisers for the opportunity to get stuck into a cross-sector event like this. We’re looking forward to the next one!

To find out more about DataKind UK and how you can support our vision of a strong, thriving third sector that embraces data science to become more impactful, visit our website.

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Zheng first explained how over a decade working in digital marketing and e-commerce sparked her interest more recently in data analytics and artificial intelligence as machine learning has become hugely popular.

At Canon, Zheng’s team focuses on ethically integrating AI into business by first mapping current and potential AI applications across areas like marketing and e-commerce. They then analyse and assess risks to ensure compliance with regulations.

Canon is actively mapping out AI applications and assessing risks, as Grace explained, “to align with regulations such as the EU legislations.”

As founder of Kosh Duo, Zheng also provides coaching to help businesses scale up through the use of AI marketing and data-driven approaches. She coaches professionals on achieving greater recognition and rewards by leveraging AI tools as well.

A key challenge she encounters is misunderstandings around what AI truly means – many conflate it solely with chatbots like ChatGPT rather than appreciating the full breadth of machine learning, neural networks, natural language processing, and more that enable today’s AI.

“There’s a lot of misconceptions, definitely. One of the biggest fears, as I touched on, is the very generic understanding that GPT equals AI,” says Zheng. “[Kosh Duo] provides coaching services to businesses to scale to the next level using AI marketing and data-driven approaches.”

When asked about trends to watch, Zheng emphasised the need for continual learning given how rapidly the field evolves. She expects that 2024 will be an “awakening year” where businesses truly grasp AI’s potential and individuals appreciate the need to evaluate their current skillsets.

The interview highlighted the transformative but often misunderstood power of AI in business and the importance of developing specialised skills to properly harness it. Zheng stressed that with the right ethical foundations and coaching, AI and machine learning can become positive forces to drive growth rather than something to fear.

Watch our full interview with Grace Zheng below:

(Photo by Benjamin Davies on Unsplash)

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 comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

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

The post AI & Big Data Expo: Ethical AI integration and future trends appeared first on AI News.

During the discussion, Grande provided insightful perspectives on the current challenges, how Edge Impulse is helping address these struggles, and the tremendous promise of on-device AI.

Key hurdles with edge AI adoption

Grande highlighted three primary pain points companies face when attempting to productise edge machine learning models, including difficulties determining optimal data collection strategies, scarce AI expertise, and cross-disciplinary communication barriers between hardware, firmware, and data science teams.

“A lot of the companies building edge devices are not very familiar with machine learning,” says Grande. “Bringing those two worlds together is the third challenge, really, around having teams communicate with each other and being able to share knowledge and work towards the same goals.”

Strategies for lean and efficient models

When asked how to optimise for edge environments, Grande emphasised first minimising required sensor data.

“We are seeing a lot of companies struggle with the dataset. What data is enough, what data should they collect, what data from which sensors should they collect the data from. And that’s a big struggle,” explains Grande.

Selecting efficient neural network architectures helps, as does compression techniques like quantisation to reduce precision without substantially impacting accuracy. Always balance sensor and hardware constraints against functionality, connectivity needs, and software requirements.

Edge Impulse aims to enable engineers to validate and verify models themselves pre-deployment using common ML evaluation metrics, ensuring reliability while accelerating time-to-value. The end-to-end development platform seamlessly integrates with all major cloud and ML platforms.

Transformative potential of on-device intelligence

Grande highlighted innovative products already leveraging edge intelligence to provide personalised health insights without reliance on the cloud, such as sleep tracking with Oura Ring.

“It’s sold over a billion pieces, and it’s something that everybody can experience and everybody can get a sense of really the power of edge AI,” explains Grande.

Other exciting opportunities exist around preventative industrial maintenance via anomaly detection on production lines.

Ultimately, Grande sees massive potential for on-device AI to greatly enhance utility and usability in daily life. Rather than just raw data, edge devices can interpret sensor inputs to provide actionable suggestions and responsive experiences not previously possible—heralding more useful technology and improved quality of life.

Unlocking the potential of AI on edge devices hinges on overcoming current obstacles inhibiting adoption. Grande and other leading experts provided deep insights at this year’s AI & Big Data Expo on how to break down the barriers and unleash the full possibilities of edge AI.

“I’d love to see a world where the devices that we were dealing with were actually more useful to us,” concludes Grande.

Watch our full interview with Alessandro Grande below:

(Photo by Niranjan _ Photographs on Unsplash)

See also: AI & Big Data Expo: Demystifying AI and seeing past the hype

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 comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

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

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Craven — with an extensive background in engineering and leadership roles at McKinsey & Company, HSBC, Nokia, among others — shared his experiences as a consultant helping C-level executives navigate the complex landscape of AI adoption. The core message revolved around understanding AI beyond the hype to make informed decisions that align with organisational goals.

Breaking down the AI hype

Craven introduced a systematic approach to demystifying AI, emphasising the need to break down the overarching concept into smaller, manageable components. He outlined key attributes of neural networks, embeddings, and transformers, focusing on large language models as a shared foundation.

• Neural networks — described as probabilistic and adaptable — form the backbone of AI, mimicking human learning processes.
• Embeddings allow computers to navigate between levels of abstraction, somewhat akin to human cognition.
• Transformers — the “attention” mechanism — are the linchpin of the AI revolution, allowing machines to understand context and meaning.

LLMs as search and research engines

Craven assesses if LLMs alone make good search engines. They understand search intent exceptionally well but don’t have access to vast data, give accurate results, or reference sources—all of which are key search requirements.

However, Craven highlighted that large language models (LLMs) are powerful summarising engines for research. He emphasised their ability to summarise data, translate between languages, and serve as research assistants:

Craven went on to caution against relying solely on LLMs for complex tasks—showcasing a study where consultants using language models underperformed in nuanced analysis.

De-hyping AI: Setting realistic expectations

The presentation concluded with practical use cases for organisations, such as documentation tools, high-level decision-making, code review tools, and multimodal decision-makers. Craven advised a thoughtful evaluation of when LLMs are useful, ensuring they align with organisational values and principles.

However, Craven warns against inflated claims about AI’s performance—citing examples where language models enhanced certain tasks but fell short in others. He urged the audience to consider the context and nuances when evaluating AI’s impact, avoiding unwarranted expectations.

Craven offered actionable insights for implementation, urging organisations to capture data for future use, create test cases for specific use cases, and apply a systematic framework to develop a strategy. The emphasis remained on seeing through the hype, saving millions by strategically incorporating AI into existing workflows.

In a world inundated with AI promises, Adam Craven’s pragmatic approach provides a roadmap for organisations to leverage the power of AI while avoiding common pitfalls.

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 comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

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

The post AI & Big Data Expo: Demystifying AI and seeing past the hype appeared first on AI News.

The focal point of Smith’s presentation was a strategic experiment conducted by his team to explore the potential of AI in reducing noise and bias inherent in decision-making. Smith began by addressing the common perception of decision-making and the often-overlooked influence of human biases and external factors.

“Let’s recognise that we’re not the best at making decisions, that there are some issues when we make decisions—just as there are some issues when AI makes some decisions,” said Smith.

Smith advocates for combining the strengths of both human and AI decision-makers.

The strategic experiment involved a comprehensive analysis of the human decision-making process, where the team pitted AI against a human team in running a Facebook travel campaign. Smith delved into the intricacies of the human brain’s dual components—the amygdala for intuitive thinking and the prefrontal cortex for reasoning.

Notably, Smith drew attention to the concept of “noise,” a term he differentiated from bias, describing it as the variance in decision-making that introduces inconsistencies. He supported this with examples from various professions, such as judges delivering differing sentences based on external factors.

The challenges within the marketing and advertising space were highlighted, particularly the difficulty of managing a vast number of variables—illustrated by a campaign with a staggering 83 million variations.

“There’s no way that a human can realistically go through 83 million [ad variation] combinations,” said Smith. “AI is better at picking out important signals in large data sets.”

Initially, the results of the strategic experiment showed humans outperforming the AI-optimised campaign, However, the AI campaign quickly pulled away:

While acknowledging AI’s flaws — including bias — Smith advocated for a collaborative approach, envisioning a balance between human intuition and AI assistance. He highlighted the importance of recognising human limitations and leveraging AI to reduce decision-making flaws.

The presentation concluded with key takeaways, encouraging the recognition of human decision-making limitations, leveraging AI to reduce flaws, and finding the right balance between human input and AI assistance.

See also: Ampere Computing: Unlocking a Path to the Sustainable Cloud

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 comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

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

The post AI & Big Data Expo: AI’s impact on decision-making in marketing appeared first on AI News.

Paul O’Sullivan, Senior Vice President of Solution Engineering (UKI) at Salesforce, sheds light on the complexities of this transformative landscape, urging businesses to tread cautiously while embracing the potential of artificial intelligence.

Unprecedented opportunities

Generative AI has stormed the scene with remarkable speed. ChatGPT, for example, amassed 100 million users in a mere two months.

“If you put that into context, it took 10 years to reach 100 million users on Netflix,” says O’Sullivan.

This rapid adoption signals a seismic shift, promising substantial economic growth. O’Sullivan estimates that generative AI has the potential to contribute a staggering £3.5 trillion ($4.4 trillion) to the global economy.

“Again, if you put that into context, that’s about as much tax as the entire US takes in,” adds O’Sullivan.

One of its key advantages lies in driving automation, with the prospect of automating up to 40 percent of the average workday—leading to significant productivity gains for businesses.

The AI trust gap

However, amid the excitement, there looms a significant challenge: the AI trust gap. 

O’Sullivan acknowledges that despite being a top priority for C-suite executives, over half of customers remain sceptical about the safety and security of AI applications.

Addressing this gap will require a multi-faceted approach including grappling with issues related to data quality and ensuring that AI systems are built on reliable, unbiased, and representative datasets. 

“Companies have struggled with data quality and data hygiene. So that’s a key area of focus,” explains O’Sullivan.

Safeguarding data privacy is also paramount, with stringent measures needed to prevent the misuse of sensitive customer information.

“Both customers and businesses are worried about data privacy—we can’t let large language models store and learn from sensitive customer data,” says O’Sullivan. “Over half of customers and their customers don’t believe AI is safe and secure today.”

Ethical considerations

AI also prompts ethical considerations. Concerns about hallucinations – where AI systems generate inaccurate or misleading information – must be addressed meticulously.

Businesses must confront biases and toxicities embedded in AI algorithms, ensuring fairness and inclusivity. Striking a balance between innovation and ethical responsibility is pivotal to gaining customer trust.

“A trustworthy AI should consistently meet expectations, adhere to commitments, and create a sense of dependability within the organisation,” explains O’Sullivan. “It’s crucial to address the limitations and the potential risks. We’ve got to be open here and lead with integrity.”

As businesses embrace AI, upskilling the workforce will also be imperative.

O’Sullivan advocates for a proactive approach, encouraging employees to master the art of prompt writing. Crafting effective prompts is vital, enabling faster and more accurate interactions with AI systems and enhancing productivity across various tasks.

Moreover, understanding AI lingo is essential to foster open conversations and enable informed decision-making within organisations.

A collaborative future

Crucially, O’Sullivan emphasises a collaborative future where AI serves as a co-pilot rather than a replacement for human expertise.

“AI, for now, lacks cognitive capability like empathy, reasoning, emotional intelligence, and ethics—and these are absolutely critical business skills that humans need to bring to the table,” says O’Sullivan.

This collaboration fosters a sense of trust, as humans act as a check and balance to ensure the responsible use of AI technology.

By addressing the AI trust gap, upskilling the workforce, and fostering a harmonious collaboration between humans and AI, businesses can harness the full potential of generative AI while building trust and confidence among customers.

You can watch our full interview with Paul O’Sullivan below:

Paul O’Sullivan and the Salesforce team will be sharing their invaluable insights at this year’s AI & Big Data Expo Global. O’Sullivan will feature on a day one panel titled ‘Converging Technologies – We Work Better Together’.

The post Paul O’Sullivan, Salesforce: Transforming work in the GenAI era appeared first on AI News.

From addressing the spectrum of use cases to navigating digital transformation, Shakir shed light on the challenges, ethical considerations, and the promising future of this groundbreaking technology.

Wide spectrum of use cases

Shakir highlighted the wide array of GenAI applications, ranging from productivity enhancements and research support to high-stakes areas such as strategic data mining and knowledge bots. She emphasised the transformational power of AI in understanding customer data, moving beyond simple sentiment analysis to providing actionable insights, thus elevating customer engagement strategies.

“GenAI now can take your customer insights to another level. It doesn’t just tell you whether something’s a positive or negative sentiment like old AI would do, it now says it’s positive or negative. It’s negative because X, Y, Z, and here’s the root cause for X, Y, Z,” explains Shakir.

Powering digital transformation

Gate One adopts an adaptive strategy approach, abandoning traditional five-year strategies for more agile, adaptable frameworks.

“We have a framework – our 5P model – where it’s: identify your people, identify the problem statement that you’re trying to solve for, appoint some partnerships, think about what’s the right capability mix that you have, think about the pathway through which you’re going to deliver, be use case or risk-led, and then proof of concept,” says Shakir.

By solving specific challenges and aligning strategies with business objectives, Gate One aims to drive meaningful digital transformation for its clients.

Assessing client readiness

Shakir discussed Gate One’s diagnostic tools, which blend technology maturity and operating model innovation questions to assess a client’s readiness to adopt GenAI successfully.

“We have a proprietary tool that we’ve built, a diagnostic tool where we look at blending tech maturity capability type questions with operating model innovation questions,” explains Shakir.

By categorising clients as “vanguard” or “safe” players, Gate One tailors their approach to meet individual readiness levels—ensuring a seamless integration of GenAI into the client’s operations.

Key challenges and ethical considerations

Shakir acknowledged the challenges associated with GenAI, especially concerning the quality of model outputs. She stressed the importance of addressing biases, amplifications, and ethical concerns, calling for a more meaningful and sustainable implementation of AI.

“Poor quality data or poorly trained models can create biases, racism, sexism… those are the things that worry me about the technology,” says Shakir.

Gate One is actively working on refining models and data inputs to mitigate such problems.

The future of GenAI

Looking ahead, Shakir predicted a demand for more ethical AI practices from consumers and increased pressure on developers to create representative and unbiased models.

Shakir also envisioned a shift in work dynamics where AI liberates humans from mundane tasks to allow them to focus on solving significant global challenges, particularly in the realm of sustainability.

Later this month, Gate One will be attending and sponsoring this year’s AI & Big Data Expo Global. During the event, Gate One aims to share its ethos of meaningful AI and emphasise ethical and sustainable approaches.

Gate One will also be sharing with attendees GenAI’s impact on marketing and experience design, offering valuable insights into the changing landscape of customer interactions and brand experiences.

As businesses navigate the evolving landscape of GenAI, Gate One stands at the forefront, advocating for responsible, ethical, and sustainable practices and ensuring a brighter, more impactful future for businesses and society.

Umbar Shakir and the Gate One team will be sharing their invaluable insights at this year’s AI & Big Data Expo Global. Find out more about Umbar Shakir’s day one keynote presentation here.

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At the core of DEPT®’s approach is the strategic utilisation of large language models. Briski articulated the delicate balance between the ‘pioneering’ and ’boutique’ ethos encapsulated in their tagline, “pioneering work on a global scale with a boutique culture.”

While ‘pioneering’ and ’boutique’ evokes innovation and personalised attention, ‘global scale’ signifies the broad outreach. DEPT® harnesses large language models to disseminate highly targeted, personalised messages to expansive audiences. These models, Briski pointed out, enable DEPT® to comprehend individuals at a massive scale and foster meaningful and individualised interactions.

“The way that we have been using a lot of these large language models is really to deliver really small and targeted messages to a large audience,” says Briski.

However, the integration of AI into various domains – such as retail, sports, education, and healthcare – poses both opportunities and challenges. DEPT® navigates this complexity by leveraging generative AI and large language models trained on diverse datasets, including vast repositories like Wikipedia and the Library of Congress.

Briski emphasised the importance of marrying pre-trained data with DEPT®’s domain expertise to ensure precise contextual responses. This approach guarantees that clients receive accurate and relevant information tailored to their specific sectors.

“The pre-training of these models allows them to really expound upon a bunch of different domains,” explains Briski. “We can be pretty sure that the answer is correct and that we want to either send it back to the client or the consumer or some other system that is sitting in front of the consumer.”

One of DEPT®’s standout achievements lies in its foray into the web3 space and the metaverse. Briski shared the company’s collaboration with Roblox, a platform synonymous with interactive user experiences. DEPT®’s collaboration with Roblox revolves around empowering users to create and enjoy user-generated content at an unprecedented scale. 

DEPT®’s internal project, Prepare to Pioneer, epitomises its commitment to innovation by nurturing embryonic ideas within its ‘Greenhouse’. DEPT® hones concepts to withstand the rigours of the external world, ensuring only the most robust ideas reach their clients.

“We have this internal project called The Greenhouse where we take these seeds of ideas and try to grow them into something that’s tough enough to handle the external world,” says Briski. “The ones that do survive are much more ready to use with our clients.”

While the allure of AI-driven solutions is undeniable, Briski underscored the need for caution. He warns that AI is not inherently transparent and trustworthy and emphasises the imperative of constructing robust foundations for quality assurance.

DEPT® employs automated testing to ensure responses align with expectations. Briski also stressed the importance of setting stringent parameters to guide AI conversations, ensuring alignment with the company’s ethos and desired consumer interactions.

“It’s important to really keep focused on the exact conversation you want to have with your consumer or your customer and put really strict guardrails around how you would like the model to answer those questions,” explains Briski.

In December, DEPT® is sponsoring AI & Big Data Expo Global and will be in attendance to share its unique insights. Briski is a speaker at the event and will be providing a deep dive into business intelligence (BI), illuminating strategies to enhance responsiveness through large language models.

“I’ll be diving into how we can transform BI to be much more responsive to the business, especially with the help of large language models,” says Briski.

As DEPT® continues to redefine digital paradigms, we look forward to observing how the company’s innovations deliver a new era in AI-powered experiences.

DEPT® is a key sponsor of this year’s AI & Big Data Expo Global on 30 Nov – 1 Dec 2023. Swing by DEPT®’s booth to hear more about AI and LLMs from the company’s experts and watch Briski’s day one presentation.

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At its core, Predibase is a declarative machine learning platform that aims to streamline the process of developing and deploying machine learning models. The company is on a mission to simplify and democratise machine learning, making it accessible to both expert organisations and developers who are new to the field.

The platform empowers organisations with in-house experts, enabling them to supercharge their capabilities and reduce development times from months to just days. Additionally, it caters to developers who want to integrate machine learning into their products but lack the expertise.

By using Predibase, developers can avoid writing extensive lines of low-level machine learning code and instead work with a simple configuration file – known as a YAML file – which contains just 10 lines specifying the data schema.

Predibase reaches general availability

During the expo, Predibase announced the general availability of its platform.

One of the key features of the platform is its ability to abstract away the complexity of infrastructure provisioning. Users can seamlessly run training, deployment, and inference jobs on a single CPU machine or scale up to 1000 GPU machines with just a few clicks. The platform also facilitates easy integration with various data sources, including data warehouses, databases, and object stores, regardless of the data structure.

“The platform is designed for teams to collaborate on developing models, with each model represented as a configuration that can have multiple versions. You can analyse the differences and performance of the models,” explains Molino.

Once a model meets the required performance criteria, it can be deployed for real-time predictions as a REST endpoint or for batch predictions using SQL-like queries that include prediction capabilities.

Importance of low-code in machine learning

The conversation then shifted to the importance of low-code development in machine learning adoption. Molino emphasised that simplifying the process is essential for wider industry adoption and increased return on investment.

By reducing the development time from months to a matter of days, Predibase lowers the entry barrier for organisations to experiment with new use cases and potentially unlock significant value.

“If every project takes months or even years to develop, organisations won’t be incentivised to explore valuable use cases. Lowering the bar is crucial for experimentation, discovery, and increasing return on investment,” says Molino.

“With a low-code approach, development times are reduced to a couple of days, making it easier to try out different ideas and determine their value.”

Trends in LLMs

The discussion also touched on the rising interest in large language models. Molino acknowledged the tremendous power of these models and their ability to transform the way people think about AI and machine learning.

“These models are powerful and revolutionizing the way people think about AI and machine learning. Previously, collecting and labelling data was necessary before training a machine learning model. But now, with APIs, people can query the model directly and obtain predictions, opening up new possibilities,” explains Molino.

However, Molino highlighted some limitations, such as the cost and scalability of per-query pricing models, the relatively slow inference speeds, and concerns about data privacy when using third-party APIs.

In response to these challenges, Predibase is introducing a mechanism that allows customers to deploy their models in a virtual private cloud, ensuring data privacy and providing greater control over the deployment process.

Common mistakes

As more businesses venture into machine learning for the first time, Molino shared his insights into some of the common mistakes they make. He emphasised the importance of understanding the data, the use case, and the business context before diving headfirst into development. 

“One common mistake is having unrealistic expectations and a mismatch between what they expect and what is actually achievable. Some companies jump into machine learning without fully understanding the data or the use case, both technically and from a business perspective,” says Molino.

Predibase addresses this challenge by offering a platform that facilitates hypothesis testing, integrating data understanding and model training to validate the suitability of models for specific tasks. With guardrails in place, even users with less experience can engage in machine learning with confidence.

The general availability launch of Predibase’s platform marks an important milestone in their mission to democratise machine learning. By simplifying the development process, Predibase aims to unlock the full potential of machine learning for organisations and developers alike.

You can watch our full interview with Molino below:

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.

The post Piero Molino, Predibase: On low-code machine learning and LLMs appeared first on AI News.