Machine learning and artificial intelligence are transforming the modern world at an unparalleled pace. From healthcare advancements, to revolutionising the e-commerce sector, these technologies are at the forefront of innovation. Despite the proliferation, a critical barrier remains: the technological complexity and expertise required to implement machine learning. However, the landscape is shifting, and fast.
According to a recent McKinsey report, 75% of businesses identify AI and machine learning as their top priority. However, the report also uncovers a daunting challenge—90% of these businesses experience significant obstacles in successful implementation. Whether it’s the high costs associated with hiring experts or the technical know-how required, implementing machine learning remains an elusive concept for most firms.
Enter the world of no-code machine learning platforms; the bridge between groundbreaking technology and everyday problem-solving.
What does this mean for businesses, startups, and individuals? Forbes suggests that AI-driven businesses will contribute over $15 trillion to the global economy by 2030. But without a way to break down the complexities, this massive potential remains untapped.
This is why the rise of no-code machine learning platforms is not just another industry trend; it’s a paradigm shift. According to a new Gartner report, the adoption of low-code and no-code platforms will constitute over 65% of application development by 2024. These platforms strip away the jargon, and give you the tools to create impactful machine learning applications without ever writing a line of code.
In this all-inclusive guide, we’ll delve deep into the world of no-code machine learning platforms. We’ll demystify what “no-code” truly means, and how it’s providing solutions to longstanding challenges in the AI field. Whether you’re an industry veteran or a newcomer to the world of AI, this guide is your passport to understanding and implementing no-code machine learning solutions in 2023.
Let’s face it—today’s machine learning landscape isn’t perfect:
These challenges make it hard for non-technical folks to unlock AI’s benefits. Enter no-code machine learning platforms, a revolutionary change for the better.
Why does this matter? Because machine learning should be about adding business value, not about writing code. No-code platforms like Octai aim to make machine learning accessible and useful to domain experts without requiring them to become tech wizards.
Intrigued? Let’s dive deeper.
Let’s get one thing straight: When we talk about no-code machine learning platforms, we’re referring to highly specialised tools that make the power of machine learning accessible to anyone, regardless of their technical background. These platforms are designed to fill in talent gaps, particularly for businesses that don’t have in-house AI or machine learning expertise.
Why is this a game-changer? It levels the playing field. No longer is machine learning a gated community for those with advanced degrees and coding skills. With no-code machine learning, the technology becomes far less intimidating, allowing business professionals to craft powerful machine learning applications without ever writing a line of code.
And this isn’t a passing fad. According to a Gartner report, by 2025, a staggering 70% of new applications will leverage low-code or no-code technologies, up from less than 25% just five years prior. This underlines the rapid adoption and inherent value of making machine learning accessible to a broader audience.
No-code platforms are a game-changer- they empower business users to not only build machine learning models but also deploy them into production—all without writing a line of code. The shift is real, and as a Deloitte study points out, the democratisation of AI is well on its way.
Predictive analytics doesn’t have to be a slog. No-code platforms automate the grunt work—think data preparation and model selection—freeing you to focus on what really matters: solving business problems. This level of efficiency is gaining industry recognition, as discussed in this Forbes article.
The journey from a trained model in a Jupyter notebook to a production environment is fraught with peril—or used to be. Now, with no-code platforms, deployment is as easy as clicking a button. This isn’t just a nice feature; it’s a game-changer as articulated in this Harvard Business Review article.
Remember the days when training a machine learning model felt like watching paint dry? With no-code platforms, that’s a thing of the past. Advanced algorithms and feature engineering, combined with the capabilities of cloud computing, make the training process faster and cheaper. For a deeper dive into this, have a look at this ScienceDirect paper.
Here’s where the rubber meets the road. No-code platforms not only save time but also money. Many of the associated costs—hardware, labour—are cut from the equation. Plus, these platforms are often cheaper to acquire and maintain, leading to substantial long-term operational savings. And if you need numbers to back this up, this Gartner report is worth a read.
After an in-depth discussion and review, we’ve settled on three crucial metrics for evaluating the strengths and limitations of various no-code machine learning platforms. Each metric is broken down into subcategories for a more nuanced understanding.
The main selling point of no-code platforms is their accessibility to individuals who may not have extensive coding experience.
Each platform will be scored from 1-10 on this metric, with a higher score meaning a more user-friendly experience.
As businesses grow and projects become more complex, scalability becomes a critical factor.
Platforms will be rated on a scale of 1-10 for scalability, with a higher score signifying a more scalable solution.
Time is money, and in the fast-paced world of machine learning, speed and cost are often closely linked.
Taking these three metrics into consideration and all the sub-metrics, we then plotted the results on a graph.
As expected whilst SageMaker, Google AutoML and Azure ML are very complete in their capabilities for ML engineers, the ability to get value out of those platforms is contingent on having both the internal domain knowledge for the models to be built and executed, but also access to extensive expertise in programming Machine Learning models.
But what about organisations that have domain knowledge but not the programming expertise or ML experience to get what they need quickly?
Let’s take a brief look now into selected no-code machine learning platforms.
Established in 2012, DataRobot has set out to make data science more accessible by automating the entire machine learning process for enterprises. The platform empowers data specialists to create predictive analytics without the need for machine learning programming. Leveraging a mix of open-source algorithms and automated machine learning techniques, DataRobot is adept at pinpointing the most suitable models for generating accurate forecasts.
Pecan AI offers a platform designed to provide actionable insights through predictive analytics, focusing on key metrics such as demand forecasting, churn rates, and conversion patterns. By utilizing this tool, teams can make data-driven decisions in areas ranging from customer engagement strategies to pricing models and supply chain management.
Dataiku serves as a comprehensive platform for data science, designed to facilitate the end-to-end process of data analytics and machine learning. With capabilities such as data wrangling, exploration, and modeling, it empowers both data scientists and business analysts to collaboratively work on data projects. Its feature-rich environment supports a variety of data sources and machine learning frameworks, enabling teams to turn raw data into actionable insights across multiple business applications.
Obviously.AI offers a no-code machine learning platform aimed at simplifying predictive analytics for businesses. Tailored for non-technical users, it automates the data science workflow, enabling quick generation of actionable insights without requiring any coding or data expertise. With an intuitive interface and pre-built templates for use-cases like churn prevention and customer segmentation, Obviously.AI empowers organizations to make data-driven decisions with minimal effort and time.
Akkio provides a no-code AI platform focused on making machine learning accessible for businesses of all sizes. The platform specializes in fast, easy-to-use predictive analytics, offering solutions for problems like customer churn, fraud detection, and sales forecasting. With a user-friendly drag-and-drop interface and seamless integrations with common business tools, Akkio.io enables companies to operationalize machine learning models without the need for specialized technical expertise.
Octai distinguishes itself in the crowded field of predictive analytics by addressing the needs of both novice and expert users. The platform excels in delivering quick, user-friendly predictive analytics, tailored for challenges such as customer attrition, anomaly detection, and revenue projection. Featuring an intuitive drag-and-drop interface, it also boasts effortless compatibility with most popular business applications.
Leveraging machine learning accelerators, the platform goes beyond templates to use artificial intelligence for model optimization. This not only simplifies the model development process but also ensures higher levels of accuracy, supported by an in-house team of world leading Kaggle machine learning specialists.
The platform’s true innovation is its dual interface, catering to no-code users while simultaneously allowing for custom code injections, facilitating cross-team collaboration like never before. In addition, Octai takes a proactive approach to explainability, automatically generating explainable documentation – xai to interpret complex machine learning models easily.
Not to be overlooked is the platform’s astute cloud management capabilities, which utilize spot and reserved instances to make its costs up to 80% cheaper than the on demand instances. With these features, Octai redefines the time to value proposition in Machine Learning.
The Future of No-Code Machine Learning Platforms
As the landscape of no-code machine learning evolves, its reach extends beyond just tech experts to include professionals from various domains. The emphasis now is not just on providing powerful tools but also on ensuring quick time-to-value and immediate ROI. This democratisation of machine learning is bolstered by a strong community spirit, as seen in collaborative platforms like Kaggle, which continue to push the boundaries of what’s possible.
The industry is now emphasising not just capability, but also usability, scalability, and practical application. Operationalising these machine learning models for real-world decision-making is becoming a key focus, transitioning from merely generating insights to driving actionable results. No-code platforms are accelerating this transformation by making machine learning both accessible and efficient.
Ready to unlock the power of Machine learning? Start your free Octai trial today and turn insights into action, faster.