A comprehensive guide that explores what machine learning is, how it works, and why it matters.
Machine learning is changing the way entire industries run. That's an incredible concept, in theory. But what does machine learning actually look like in practice? Why is it so important? And what is no-code machine learning?
We’ve put together this comprehensive guide to help answer all these questions. By the end of this article, you’ll have a better understanding of machine learning, the kinds of strategies successful businesses are deploying, what exactly no-code machine learning is, and how you can tap into the technology that’s poised to revolutionize the way businesses run.
What is Machine Learning?
Machine learning is a subfield of artificial intelligence (AI) that solves problems using algorithms and statistical models to extract knowledge from data.
We can think of AI as the ability of a machine to imitate intelligent human behavior, and machine learning is one expression of that. Through data and algorithms, machine learning draws inferences from patterns to automatically improve.
Think back to the way you learned to ride a bike. The goal for riding a bike is to stay upright and move forward. Successfully riding that bike meant knowing how and when to pedal and where your body should be to stay up.
But you don’t learn to ride a bike by learning the rules. You learn by doing! While you may have fallen a few times, you got better each time you got up and applied what you learned.
Machine learning does the same thing: it uses statistical analysis to learn autonomously and improve its functions.
We see machine learning in a lot of our everyday routines and experiences. It’s there when:
- Netflix suggests new titles for you to watch
- Siri answers your questions
- Google Maps predicts your commute time
Why is Machine Learning Important?
Machine learning is important because data is the lifeblood of businesses. And as the amount of data grows, so too will the need to understand it all, quickly. Machine learning can help unlock valuable insights from the mountains of data they possess.
From retail to insurance to marketing, businesses are beginning to understand the value of unlocking and harnessing the potential of their data. It’s getting to the point where teams can no longer afford not to leverage machine learning technology in one form or another.
For example, machine learning helps business to:
- Quickly and automatically produce models that analyze complex data
- Generate faster and more accurate results
- Conduct time series forecasting
- Identify profitable opportunities
- Forecast revenue and predict loss/risk.
- Detect credit card fraud
The bottom line is: Machine learning delivers the kind of automation needed to help businesses scale and gain agility. That’s because machine learning is the art of finding patterns in data.
Let’s break down what we mean by ‘patterns.’
Say you were to scan a small parking lot: you could easily find patterns in what you see, some red cars, some white cars, some fords and some SUVs.
Now, if the number of cars were to increase to 1,000,000 cars, our task to find commonality becomes much more difficult and time consuming.
This is where machines can inherently do this work faster. But they need to know what to look for; and this is where it becomes an art. We need to state what it should look for and where to look for these patterns, then through trial and error you’ll be able to identify the commonalities of these cars incredibly quickly. Once this trial and error is refined, you can eventually predict future events.
And as technology continues to advance, companies are turning to business intelligence platforms to help leverage machine learning capabilities. According to Forrester, it’s expected that in 2022, traditional businesses will adopt an AI-first approach to platform and digital transformation. The more “AI inside,” the more enterprises can shrink the latency between insights, decisions, and results.
In fact, by 2024, as much as 65% of application development will be done on no-code/low-code platforms, according to a Gartner Magic Quadrant report (but we’ll get to what exactly no-code and low-code platforms are later).
First, let's take a look at the difference between the technologies advancing our society.
Machine Learning vs. Artificial Intelligence vs. Deep Learning: What’s the Difference?
Today, businesses use the terms machine learning, deep learning, and artificial intelligence interchangeably. While they’re all easy to confuse, each one is uniquely different from its siblings. And it’s a good idea to understand the difference between the technology that’s changing our world.
Artificial Intelligence uses computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.
Machine Learning, as we mentioned earlier, is one way to achieve artificial intelligence. It solves problems using algorithms and statistical models to extract knowledge from data.
Deep Learning is a subfield of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.
The major difference between deep learning and machine learning is the way data is presented to the machine. Machine learning algorithms usually require structured data, whereas deep learning networks work on multiple layers of artificial neural networks.
As you can see, while all three are related, they differ in their application. Machine learning is not the only means for us to create intelligent systems, but it is the most successful so far.
Now, let's look at some industries that use machine learning.
Who Uses Machine Learning?
Machine learning is used in multiple industries. From medical diagnosis, image processing, prediction, classification, and more. And as machine learning grows, so do the jobs associated with it.
Below are some examples of machine learning at work in different industries.
Finance leverages machine learning to validate transactions, detect fraud, make recommendations, and more.
Here are some examples of how finance uses machine learning:
- Automate routine tasks: AI can help you automate tasks that are repetitive or require human input. For example, it can help you automate tasks that require human judgment, such as reviewing loan applications and flagging those that may be fraudulent.
- Improve decision making: AI can help you make faster decisions by analyzing large volumes of data in real time. It can also help you make better decisions by providing insights into patterns and trends in your data.
- Increase revenue: AI can help you increase revenue by identifying opportunities to sell more products or services to your existing customer base. For example, it can identify patterns of high spending that indicate customers may be interested in additional products or services.
As the global climate changes due to human activity, we’re all seeing increased frequency and intensity of climate events, such as droughts, heatwaves, and wildfires.
According to the Congressional Research Service, from 2011 to 2020, there were an average of 62,805 wildfires annually and an average of 7.5 million acres impacted annually. Their size, frequency, and destruction continues to grow larger and is only exacerbated by climate change.
With machine learning, data can be harnessed to accurately predict wildfires and their danger levels. Having these kinds of predictions is useful from a global understanding, but it also helps those who combat wildfires on the ground. Real-time predictions of how dangerous a patch of forest will be in a fire can help firefights better allocate resources and crews so they can focus on those areas.
Employees are not always going to participate in the off-boarding process, or they might not be forthcoming in their HR exit interview. And by the time the exit interview comes around, it’s too late to address issues that caused that employee to leave in the first place.
In fact, according to the Work Institute’s 2020 Retention Report, 78% of the reasons employees quit could have been prevented by the employer.
Attrition is a major cost for any organization. According to the Center of American Progress, predicting turnover would help save money in the long run:
“For positions that earn between $30,000 and $50,000 per year, the cost of replacement was found to be 20% of annual salary. For executives earning high salaries, the cost of replacement was found to be 213% of annual salary. (Boushey & Glynn, 2012). For example, an executive who earns $100,000 would cost $213,000 to replace.”
Not to mention, with less skilled workers available to do the work, it becomes harder to get the results needed to keep profits up and morale high. As well, what’s lost when a productive employee quits isn’t just the person or the cost of their replacement. It’s the new product ideas, great project management, customer relations, and more.
Predicting employee turnover helps you understand which employees are at risk of leaving and get a hint of what interventions could be implemented to reduce the change of attrition.
With machine learning, HR managers are able to see not just to see what happened, but understand why it happened, what will happen next, and how to adapt their workforce strategy to align with company objectives. This helps HR to foresee vacant positions, team budget needs, what employee benefits they can improve to keep employees happy, what departments are the most and least likely to stay at a position for a length of time, and more.
As an HR manager, you can predict questions like:
- What causes employee churn?
- Why do we lose valuable employees?
- When will an employee most likely leave the company?
When this data is leveraged with no-code machine learning tools like ours to gain valuable insight about your staff, you can be proactive and manage potential issues before they arise. That kind of business intelligence makes for a positive impact on revenue.
If you’ve ever added a recommended item to your existing cart, whether on Amazon.com or in a grocery store, you’ve experienced a “cross-sale.” Cross-selling means selling one item with another that a customer already planned on buying.
For example, if you’re buying coffee beans on Amazon, you might get a recommendation for a coffee grinder. A store clerk might take advantage of the same cross-selling opportunity.
This might be easy enough to do manually if you only have a few products, but if you want to accurately recommend the right products to the right people, it becomes impossible to do manually, at scale.
By predicting which customers are interested in a cross-sale, retailers can increase revenue, and also ensure that we don’t annoy customers who aren’t interested in additional products.
You can also leverage your retail data to predict and analyze:
- Common shipping methods
- Average order priority by product sub-category
- Average time between order data and shipping date
- Quantity related to location and product
Hospitals are under pressure to cut costs and improve patient outcomes. One of the most effective ways to do this is by reducing length of stay.
Across OECD countries, the average length of stay is just under 8 days, but it varies widely by hospital. Here are the statistics for the United States:
- The national average for a hospital stay is 4.5 days.
- The average cost of one day of a hospital stay is $10,400.
Machine learning can be leveraged to predict length of hospital stay and help improve the quality of inpatient care and ultimately improve resource allocation.
Obviously, these are only just a few examples of industries using machine learning. And as technology continues to evolve, you’d be hard pressed not to find AI in every industry.
Now, let’s look at the types of machine learning.
Types of machine learning
There are three types of machine learning, and each of them have their own advantages and disadvantages. But before we explore them, it’s important to understand what kind of data they use.
Machine learning leverages two kinds of data—labeled and unlabeled.
- Labeled data has both input and output parameters in a machine-readable pattern. It requires a lot of human labor to label the data.
- Unlabeled data has only one or none of the parameters in a machine-readable pattern. This means human labor isn’t required.
Supervised learning is the most common sub branch of machine learning. It’s name originates from the idea that training this type of algorithm is like having a teacher supervise the whole process.
Basically, supervised learning uses a training set to teach models to yield the desired output. Input data is fed into the model and adjusts its weights until said model has been trained appropriately. The objective is to classify and predict outcomes accurately.
Supervised learning can be split into two subcategories:
- Classification: This type of supervised machine learning is anything and everything where you take data and try to predict labels, such as “Is it a good day to play tennis?” (YES or NO) or “What groceries should I stock up today?” (Bread, Pasta or Juice).
- Regression: This type of machine learning is anything and everything where you try to predict a number output for a new item. For instance, “What is the price of this apartment going to be in 2 months from now?” ($2,300). “What is the time of productivity if people work from home?” (5 hours).
Supervised machine learning helps businesses solve challenges at scale. We see this supervised learning used when businesses want to predict housing prices, customer churn, find out whether a loan applicant is high-risk or not, or even classify whether or not an email is spam.
Unsupervised learning finds hidden patterns in data. It’s used to draw conclusions from datasets that consist of input data without labeled responses. This is different from supervised learning, where training data includes pre-assigned category labels.
As such, unsupervised learning does not have a “teacher” correcting the model, as in the case of supervised learning. Algorithms used in unsupervised learning must first self-discover any naturally occurring patterns in that training data set.
There are many types of unsupervised learning, but we typically see two main subcategories:
- Clustering - This is an unsupervised learning problem that involves finding groups in data.
- Density Estimation - This type of unsupervised learning problem involves summarizing the distribution of data
Unlike supervised learning, unsupervised learning can handle large volumes of data in real time. It’s useful in situations where a human is looking to find patterns in their data, but doesn’t know quite what they’re looking for, and might have a hard time doing it on their own.
You’re likely to see unsupervised learning in use cases such as:
- Customer segmentation in marketing
- Segmenting data by purchase history
- Anomaly detection, such as fraud
- Classifying people based on different interests
- Grouping inventories by manufacturing and sales metrics
Reinforcement learning is when we tell a computer agent to perform some tasks without giving it too much guidance. Its algorithm relies on some labeled data as well as active feedback. Instead, the computer is allowed to make its own choices. Depending on whether these choices lead to an outcome that we want or not, these choices are weighted with reward or penalty.
This process is repeated multiple times, allowing the computer to learn the optimal way of doing something by trial and error and repeated iterations.
Examples of reinforcement learning include:
- Self-driving cars
- Personalized product recommendations
- Ad recommendation system
Related Reading: 32 Useful Machine Learning Stats for 2022
How to Choose Which Machine Learning Algorithm to Use
There are dozens of supervised and unsupervised machine learning algorithms, and each one takes a unique approach to learning.
Determining the right algorithm to use is often just a case of trial and error. The most experienced data scientists will tell you that even they can’t distinguish what machine learning algorithm to apply until they try it out.
At the end of the day, choosing the right machine learning algorithm depends on a variety of factors, such as:
- The problem statement
- Output you want
- Type and size of your data
- Available computational time
- Observations in your data
If, say, you want to train a model to make a prediction, like a stock price, then use supervised learning. If you are looking to simply explore your data and need to train a model to represent it, use unsupervised learning.
Looking for something more advanced? Check out How to Choose the Right AI Model for Your Application
The Importance of Data Preparation in Machine Learning
Businesses have a plethora of data. But simply amassing large stacks of that data won’t be much use to a business if it isn’t accurate or organized.
Having machine-learning ready data helps you maintain quality and makes for more accurate analytics. And to do this involves doing a bit of manual labor. In data science, this process is called data preprocessing, also known as data cleaning.
What is Data Cleaning?
First things first: let’s define what we mean when we say data cleaning in machine learning.
Data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted.
But, as we mentioned above, it isn’t as simple as organizing some rows or erasing information to make space for new data.
Data cleaning is a lot of muscle work. There’s a reason data cleaning is the most important step if you want to create a data-culture, let alone make airtight predictions. It involves:
- Fixing spelling and syntax errors
- Standardizing data sets
- Correcting mistakes such as empty fields
- Identifying duplicate data points
It’s said that the majority of a data scientist's time is spent on cleaning, rather than machine learning. In fact, in data science, the golden rule is 80/20: 80% of a data scientist's valuable time is spent simply finding, cleansing, and organizing data, leaving only 20% to actually perform analysis.
And to us, that makes sense - if there’s data that doesn’t belong in your dataset, you aren’t going to get accurate results. And with so much data these days, usually combined from multiple sources, and so many critical business decisions to make, you want to be extra sure that your data is clean.
But simply amassing large stacks of that data won’t be much use to a business if it isn’t accurate or organized.
Cleaning your data helps you maintain quality and makes for more accurate analytics.
These are the kinds of benefits you’ll see:
- Better decision making
- Boost in revenue
- Save time
- Increase productivity
- Streamline business practices
Unless you are trained in data science, or already what needs to go into a machine-learning dataset, it’s usually a good idea to walk through your dataset with a data scientist before you start trying to build a model. This is because data cleaning is more than just formatting a spreadsheet.
How to Create and Build a Machine Learning Model
We’ve talked a lot about the different kinds of machine learning algorithms. And you’ve probably inferred that machine learning, no matter how easily the concept is broken down, is hard.
You’d be right.
We all have a plethora of data. Millions of it pours into companies, every day, from hundreds of sources. This gives them the "fuel" they need to create machine learning predictions.
The problem is they lack the talent to do so.
That's because the skills required to develop models and solutions are hard to come by. That makes proprietary development not only costly, but time-consuming.
Typically, a business runs into one of three problems:
- The company doesn’t have a data scientist
- The company might have one or two data scientists, but the people wearing these hats often don't have the programming skills to create machine learning models.
- The company can afford a large team of data scientists, but they are unable to work with speed, since creating these models from scratch can take months—and that's on top of everything else they need to do.
On top of all this, too, are issues of rampant miscommunication. It is not always easy to ensure business executives and data scientists are on the same page. Miscommunication due to differing priorities can lead to delays and complications, which impedes on decision-making.
Related Reading: How Much Does it Cost to Build a Data Science Team?
Machine Learning & Business Intelligence
We love this quote from HBR, which perfectly sums up the limitations of businesses:
"For several decades, organizations have had two alternatives when they needed new information systems. They could build a new system using their own developers, or they could buy a system from an external vendor. The “build” approach, like a custom suit or dress, offers a close fit to business requirements. But as with custom tailoring of clothing, it typically means higher costs and a long wait. Systems from vendors, like off-the-rack clothing, don’t fit as well but are typically much cheaper and can be installed faster. Sometimes companies can configure these systems, but firms often find it easier to change their business to suit the system than vice-versa."
Simply put: If a business doesn’t have a data science team, the next best thing is to typically search for an all-in-one business intelligence platform. Which may or may not work with their system.
The end goal of Business Intelligence (BI) tools is typically the same: to provide end users with information about their data. This data should, theoretically, help end users make smarter business decisions.
More often than not, however, BI tools don’t align well with the way most businesses are structured.
This is because they’re usually designed for analysts and data scientists, people with highly advanced, technical degrees. Which makes sense. But if you consider that the end users of all that data aren’t typically well-versed in data (such as Marketers, HR, sales teams, and more) then traditional BI tools are actually quite inefficient: You have people at the frontline of decision-making that need to make data-backed decisions, but rely on data scientists to use BI tools.
That creates a lot of backlog and bottleneck. Data scientists end up spending an inordinate amount of time doing things for other teams, like building monthly reports, instead of getting to use their advanced degree. As well, traditional BI solutions have inefficient workflows. Most well-known enterprise solutions consist of multiple systems and applications, which force users to exit their current workflow and jump into another application to secure valuable data.
As well, many BI tools, even if they have machine learning embedded, are not designed for business users. Traditional vendors often try to cover the complexity of their solution with self-service options and features, but users continue to feel like they need an advanced engineering or computer science degree to navigate them.
So while BI tools have many benefits, they’re not built for today’s modern business, and actually create a lot of inefficiency.
No-code Machine Learning
This is where no-code machine learning is changing the game.
No-code machine learning means using a no-code development platform with visual, code-free and often drag-and-drop interface to deploy AI and machine learning models.
These types of solutions democratize AI by making it widely and easily available at a low cost. Which is huge. In an age where the demand for software far exceeds the supply of coders, no-code tools are helping an increasing number of businesses escape the software developer skills shortage.
For instance, a small business that lacks the budget to hire a software developer, you can leverage no code platforms and move the power of innovation into the hands of your team.
You can also equip more of your workforce with the kinds of tools they would need to become citizen data scientists. Business users and other non-technical roles can step in and meet the demands of the ever-changing business world.
With no-code machine learning, you can generate datasets, train, and deploy models with minimal to no coding knowledge in significantly less time while staying economical.
Related Reading: Can You Do Machine Learning Without Code?
No-code vs. Low-code: What’s the Difference?
We can't talk about no-code without discussing low-code.
The difference between no-code and low code platforms are the end user:
- Low-code platforms are aimed at developers
- No-code platform target business users
Low-code platforms typically require more technical knowledge, and require some coding. To make sure the developer has the control they need, coding is still an important part of the development process.
No-code platforms on the other hand focus on creating the best and easiest user experience possible, abstracting away from technical details. Because of this, no-code platforms are easier and faster to use.
Related: How No-code Algorithms Work
No-Code Myths and Misconceptions
Although no-code is gaining traction, deciding to use a no-code platform may still be met with skepticism.
That’s understandable. Whenever something new comes along that disrupts an already established system, it’s typically met with hesitation. After all, 10 years ago, everyone was skeptical about electric cars. Now? Teslas are as numerous as Chevys.
No-code Myth #1: Speed Means Sacrificing Accuracy
Some believe that no-code is all about quickly creating applications, and in this process, these platforms lay less emphasis on accuracy. In fact, a question we get asked frequently is if the speed of our product means compromising accuracy.
The reality is that you can have both speed and accuracy - with the right platform. And this is where it’s crucial for you and your team to vet what platforms are out there to ensure you’re going with the right one.
Fact: The best no-code platforms give you both speed and accuracy
No-code Myth #2: No-code is Just for Beginners
This myth could be further from the truth, but let’s break it down first.
People believe no-code is for beginners. In reality, no one benefits more from no-code platforms than developers themselves!
Platforms like ours automate high-volume (but not necessarily high-value) development tasks and cascade any updates throughout an application. This means engineers can focus their experience on building value-additive software.
The value achieved with no-code is time saved.
Fact: No-code is for everyone
No-code Myth #3: No-code Platforms Make Coding Obsolete
With no code, one can create applications without coding. But that doesn’t render developers or engineers or coding obsolete.
No-code empowers people in HR, finance, sales, and customer service to create their own applications. And this is why IT welcomes no-code - it frees up developers to focus on more complex software or strategic projects.
Fact: No-code frees up IT and development teams for more complex coding work.
Myth #4: No-code is Just a Trend
No-code is here to stay. And we’re not just saying that because we’re in the business of no-code. No-code tools are gaining popularity amongst businesses of all sizes - for good reason.
For starters, no-code tools help improve business operations. Nontechnical users gain the functionality to become more autonomous. Technical teams get time to focus on strategic projects that propel the business forward. Business teams can take advantage of automated tasks and processes. And customers get access to easy-to-use digital forms and workflows.
For another, it's predicted that no-code will soon become ubiquitous. A recent Gartner report made the prediction that 80% of technology products and services will be built by people outside of technology fields by 2024.
Fact: Like Tesla, no-code is not just a passing trend - it’s poised to revolutionize entire industries.
Use Case: No-code Machine Learning and Credit Risk Scoring
When an individual applies for a loan, the lender must evaluate whether that person can reliably repay the loan principal and interest. Lenders commonly use measures of profitability and leverage to assess credit risk.
If a lender needs to evaluate two loan applicants – one with high income and high leverage, and the other with low income and low leverage – which individual has lower credit risk?
To properly assess credit risk management, lenders need to understand not only your income and collateral, they also assess:
- Credit history (credit risk and score)
- Capacity to repay
- Debt-to-income ratio
Summarizing all of these various dimensions into one score is challenging and sifting through all that data can be time-consuming. This is where no-code machine learning helps to not only estimate default probability and loss severity, but also loss forecasting, all while using past client behavior data.
For instance, a micro-lending company was looking to predict which customers were most likely to pay back micro-loans on time and what is the best amount to offer to them.
Their team used to do this by hand, keeping a track of each individual person. But this proved to be time-consuming and was prone to human error. It was a process that just couldn’t scale.
Since an internal data science team was out of reach, they turned to Obviously AI to help them predict loan repayment and default rates on new inbound customers.
The company connected their historical data, which consisted of inputs like demographics, employment data, who the customer banked with, and overall, a historical data of loans given to these customers in the past and whether they paid them back on time, delayed, or defaulted.
In just a few clicks, and in a matter of minutes, the company had fully trained an AI model that proactively predicted loan defaults and provided a preemptive strategy by demographics, time, and product engagement.
These predictions were then fed directly into their CRM systems in real time. This meant their team could enter in data while talking to a potential customer and, in seconds, they could predict whether or not that potential customer would likely default on their loan. All without Ph.D. AI engineers.
With no-coding machine learning, they:
- Identified 83% new customers that are likely to default
- Increased their internal efficiency building models by 10x
- Empowered the business team to build AI predictions
What to Look for When Consider a No-code Machine Learning Tool
AutoML platform? BI tool? Best automated machine learning software?
When it comes to searching for the best predictive analytics tool, many of the preliminary searches look like the above. Most are simply trying to articulate what it is that they’re after: a platform that will deliver the kind of business intelligence needed to stay, and remain, proactive and competitive.
Related Reading: Here's How You Know if You're Ready for AI
There’s magic in the ability to take data and turn it into meaningful insights that transform decisions. And with the rise of no-code platforms, the barrier to entry for AI, and development more generally, is lower than ever before. Anyone with an idea can build it.
But it’s just not always easy to know what to look for. And with new and more innovative products coming out every day, not all of them have staying power.
Here’s what services to look for so you can be sure you’re partnering with an established, well-supported platform that will give you the longevity, agility, and confidence you need.
Ease of Use
No-code machine learning platforms are advertised as easy to use. But not all platforms are created equal. And you’ll find that some platforms are easier to use than others. Look for one that gives you easy, drag and drop functionality. It should not feel like pulling teeth to integrate your data, nor should it take more than a few clicks to make predictions.
When it comes to accuracy and speed, the best platforms won’t make you choose one or the other. You can have both. Your no-code machine learning platform should allow you to generate predictions in less than a minute. In fact, the best in the industry let you make predictions in seconds.
A platform’s ability to adapt and grow with you is crucial. How many predictions can you make with it? How many users or seats are you allotted? For instance, do you have to pay per user, or do you have unlimited seats? When you see a pay-per-user, this could mean that this technology is not designed for your app to scale.
Documentation and Support
How often are they updating their product? What is their release cycle like? Do they have a Status page? What kind of support system does the platform have? It’s easier to succeed with no-code when a vendor offers robust educational materials and a strong system of support services. Look for resources that help you learn at your own pace, like a university or academy.
When it comes to support, ensure that whatever platform you choose has a fully-functioning team of data scientists. While no-code platforms are designed to be intuitive and let your team figure things out on their own, the vendor you select should be readily available to help answer any questions.
What are people saying online about the vendor? How does one platform compare to the other? (Psst: be sure to read our blog post about how Obviously AI and H2O.ai compare). What kind of rating does it have on sites like G2 or Gartner? Look at sites like crunchbase to see who is invested in them. For instance, if they have reputable investors, you know that platform is credible. Knowing who their investors are will help you believe (or not) their claims to being the fastest in the industry.
Building and deploying any type of AI model is quickly becoming one of the sure-fire ways that businesses can elevate themselves. And with no-code AI tools like Obviously AI, it’s truly effortless.
As long as teams have the data, they can effortlessly train and deploy intelligent models, for everything from churn prediction to sales funnel optimization.
To see it all in action and understand what no-code can do for you, book a demo with our team today.