In the late 1980s, Juergen Schmidhuber, an AI researcher, wrote a thesis on "self-referential learning," or on building machine learning models with the ability to learn. This idea of AI meta-learning finally made its way into industry in the 2010s as “automated machine learning,” or AutoML, with tools like Auto-WEKA, Google AutoML, and Auto-sklearn.
These tools exist to make building machine learning models faster and easier by automating a lot of the tedious work behind the scenes.
AutoML has rapidly developed to the point where AI is accessible to everyone. You no longer have to be a data scientist or possess technical know-how to apply AI and machine learning to your business processes.
Let’s start by exploring what machine learning is. Fundamentally, machine learning refers to applications that learn on their own, instead of being given explicit instructions.
This is used when it’s inefficient or even infeasible to provide all the necessary instructions. For example, it would be impossible to build a self-driving car with traditional programming, as there are literally infinite possible scenarios when it comes to driving: The machine would need to account for all conceivable weather and traffic conditions, visibility levels, potholes, and a million and one other conditions.
With machine learning, the machine can instead find patterns in huge amounts of data, so that we don’t have to explicitly instruct the car what to do, when, say, there’s a flashing orange traffic light, it’s raining, the car is going 40 miles per hour, and there are children playing near the street. The machine learns on its own what to do in any situation, which could result in self-driving cars being safer than human drivers.
That said, traditional machine learning isn’t effortless, and machine learning algorithms come with a set of parameters and hyperparameters, or meta-rules for how the algorithm will learn. This includes technical variables like the optimization algorithm, the activation function, the number of hidden layers and neurons in a neural network, the evaluation function, initial weights, algorithm selection, and more. From scikit-learn regression to deep neural networks, there are countless variables to optimize.
You can think of these like the dials on an equalizer: You can control specific frequencies, the sharpness of the bandwidth, the treble, the mid, the bass, and more. It takes a good ear to figure out the right settings, which depends on whether you want club music with low-pitched drums or resonant lower mids for a bassoon.
A significant component of AutoML is automatically tuning these dials (hyperparameter tuning) to produce the desired result, so you don’t need the technical expert to sit there and figure it out manually. AutoML software essentially automates all the manual, tedious modeling tasks in a larger data science workflow, from feature engineering to raw data processing. This enables faster, more efficient machine learning pipelines.
Prior to AutoML, the machine learning process included time-consuming hyperparameter optimization, in addition to a lot of manual data preparation and data preprocessing work. That said, many AutoML toolkits, such as TPOT, Microsoft’s Azure AutoML, auto-keras, and so on, still require the use of programming languages like Python, and they don’t provide full automation.
On a technical level, there are three working components of AutoML:
Since machine learning works by learning from data, automated data ingestion is a key part of more efficiently building AI models. Training data can come from a number of sources, whether it’s big data in Snowflake or Salesforce, or just a simple CSV file or Excel spreadsheet.
Further, if there’s relevant data in multiple places, you need to be able to merge it. Then, the data columns need to be automatically classified and encoded for the machine learning model training process.
Next up is automated model selection and training. There are many machine learning approaches, each of which works best on a specific type of problem. Using Neural Architecture Search, we can select the best model for any given dataset. Once we’ve selected a model, we need to train it on the dataset, undergo model validation, and communicate the model’s performance through various accuracy metrics, such as visualizations of error.
Finally, AutoML makes it simple and easy to deploy models in line with your existing processes, such as through an API. It’s crucial to monitor the performance of models over time, and re-train them as the real-world business environment shifts and as new data becomes available.
There are tons of benefits around AutoML, which largely center around increased efficiency, accessibility, and performance.
Traditionally, building machine learning models would take weeks or months, with many moving parts. AutoML takes just days for business professionals and data scientists to develop and compare dozens of models, find insights and predictions, and solve business problems.
In fact, the majority of businesses working on ML models have yet to get them into production. The same Algorithmia survey reports that the large majority of businesses take over a month to deploy a model. Putting a model into production quickly is exceedingly rare, with just 1% of businesses taking a day or less to do so.
As a result, quick model building and deployment will give you a massive competitive advantage, not only over competitors that aren’t using AI, but even competitors that are using traditional AI software.
Traditional AI is a resource-intensive process, as it requires highly technical talent and tools. Further, it’s getting more expensive, as data scientists are becoming increasingly hard to find, hire, and retain.
GlassDoor ranks data scientist as the #2 job in America, with “machine learning engineer” in the top 20 as well. Further, over 80% of data science teams plan to hire in Q3 and Q4 of this year. IBM and The Business-Higher Education Forum call this data science shortage “the quant crunch” in a popular report.
In short, data scientists have options, and they know it. Data scientists demand high salaries, constant growth opportunities, and other benefits that not every business can provide.
Rather than hire additional, expensive data scientists, businesses can use AutoML to optimally utilize their existing talent - even if non-technical - to build and deploy AI models.
Accessible AI is a crucial prerequisite to widespread company adoption. When team members are unable to use their expertise to build models, let alone actually use them in their day-to-day work, such as sales teams scoring leads, then AI will fail to make a real impact.
Through new AI tools, technology that was once gated behind technical barriers of entry is now accessible to all. Marketers can make quick decisions from large quantities of data without the time, cost, and expertise barriers previously in place.
Recall the earlier analogy of how AutoML is like tuning the knobs in an equalizer. By automatically finding the best parameters and hyperparameters for an AI model in any given situation, AutoML works to improve performance as well.
Manually trying out many sets of parameters and hyperparameters would just be an inefficient process, but with AutoML, it’s all done automatically, giving businesses more accurate models.
With these automated processes, there are also fewer chances for human error.
With a more efficient, data-backed process, you’ll be sure to get accurate, actionable insights.
AutoML automatically finds the best combination of features from a set of any size to create a predictive model. With this in mind, we can use AutoML to build better AI products.
AI is an extremely hard problem to solve. It’s not just about building a machine learning algorithm that can identify patterns and make predictions — it’s also about deciding which variables are most important, how to measure them, and even which model to use in the first place.
To get around these problems, many companies are using teams of data science experts, but with AutoML, you can automate much of this work to get to accurate insights faster than ever before.
Succeeding in the startup world is largely about the speed of execution, especially in sales and marketing. AI can help with that.
According to a Business Insider report, the majority of marketers are already using artificial intelligence technologies, and the proportion will only continue to grow.
AI is already being used for tasks like lead scoring, text classification, and sales funnel optimization. And it will continue to evolve as more companies adopt AI into their business processes.
Using machine learning, companies can automatically analyze customer data to identify which prospects are likely to become customers. This allows sales teams to focus on those prospects who have the highest likelihood of becoming customers instead of spending time with every prospect they meet or calling everyone on their list.
Further, using machine learning models based on customer data and past purchase behavior, companies can improve their conversion rates by automating aspects of their online marketing campaigns such as determining which offers are most effective at different points in a buyer’s journey.
While AutoML is a powerful tool for boosting the efficiency, accessibility, and performance of your AI projects, it’s not a silver bullet.
For example, while AutoML can help you effortlessly build lead scoring models, it’s not going to fix a broken product or service. If your offering is fundamentally unattractive, AutoML can’t fix that for you - you’ll need to go back to the business drawing board and create a viable, marketable offering that your customers truly want.
AutoML, like AI, augments what you already have, but it won’t fundamentally transform your business. To give another example, while text classification can show you customer sentiment on social media, it won’t automatically turn sour sentiment positive, and while employee retention models will show you which employees are likely to quit, it won’t convince them that they should stay on-board. You’ll still need to act yourself, based on the insights that the models reveal.
AutoML is also limited when it comes to highly complex, and especially risky use-cases, such as medical diagnoses or providing legal recommendations. In situations like these, AutoML is not a replacement for data scientists - it only reduces the number of data scientists needed and aids them in the process by automating repetitive tasks. Further, an expert in that area will need to do manual feature selection, or figure out what data to use for training in the first place.
Finally, not all AutoML tools are made equal. Many AutoML tools are built by and for engineers, rather than non-technical folk, which means that they’re still largely inaccessible to businesspeople. You’ll find many open source cloud AutoML tools on GitHub, but even with tutorials, there’s a large learning curve to applying them to your business.
We’ve explored machine learning, AutoML, and their benefits and limitations. According to Forbes, over 70% of enterprises are using AI and ML to improve their business performance.
So the question is, how can you easily implement AutoML in your business? There are many AutoML tools out there. Here’s what you need to look for in a tool:
If you’re looking for a tool that doesn't burn a hole in your pocket and doesn’t force you to go on a data scientist hiring spree, Akkio is the obvious choice.
We’re built for marketing professionals and business developers who may not have a technical background, but still want access to powerful technology. With Akkio, any team can build models. There’s no added cost for training, so you don’t need to pay hefty up-front costs to test your processes.
Akkio has been used successfully across a wide range of industries and niches, from marketing applications like lead scoring all the way to financial fraud prediction and sentiment analysis of free form text. Not only can you try Akkio for free, but after signing up, you get access to a number of demos to play with. This lets you build state-of-the-art models in seconds, at no cost.