Want to use machine learning in your business but don’t know where to start? This post is for you.
Machine learning (ML) can be a powerful tool to help your business predict outcomes, optimize sales, mitigate risk, and more. But ML can seem intimidating, especially if you have no coding experience.
From data ingestion and cleaning, to model selection, training, validation, and deployment, the process of building an ML solution can be daunting. But there are a number of powerful no-code ML platforms that can help you get started quickly and easily, without any coding required.
That's why Akkio’s no-code AI platform is such a powerful tool. Akkio supports the entire end to end machine learning workflow, from pre-processing data to building and deploying predictive models. Akkio can help you understand your data, build and optimize predictive models, and deploy them for a range of use-cases, from churn reduction to fraud detection or attrition prediction.
In this article, we'll show how Akkio can help you build an end to end machine learning workflow without any technical expertise needed.
Machine learning uses algorithms to discover patterns in data. While traditional algorithms are well-suited to very small, simple datasets, machine learning algorithms are useful for the large, complex data generated by modern businesses.
For example, in marketing, machine learning can help you understand your customers' unique needs so you can better target them with the right products, offers, and messages.
On Amazon, machine learning algorithms help recommend products based on people's shopping habits and browsing history. Amazon is even using AI to reduce waste by optimizing the product packaging mix. Similarly, Netflix uses machine learning to discover which shows to recommend to you based on your implicit and explicit preferences.
These large enterprises fuel deep neural networks with a massive amount of data, creating what’s known as deep learning models, but the reality is that even startups and small businesses can get started with machine learning projects.
Machine learning is used far beyond optimizing the customer experience. Insurers use ML to predict when policyholders may file claims in order to proactively assist them. Banks use ML to help flag suspicious transactions so they can protect their customers from fraud.
Meanwhile, human resources teams use ML to predict which employees are at risk of quitting so they can be proactively offered coaching and resources to help keep them.
Amidst a seemingly never-ending wave of resignations, it's more important than ever for HR teams to have a handle on employee retention. That's where machine learning comes in, by helping to predict which employees are likely to quit so that proactive measures can be taken.
With all the applications of ML, it's no surprise that 90% of MIT-surveyed executives report already having AI strategies in place. In the real-world, data-driven decision making is vital to doing business, which explains the popularity of AI algorithms such as Convolutional Neural Networks (CNNs), computer vision, natural language processing (NLP), and more.
A broad spectrum of algorithms are used in AI, each with their own strengths and weaknesses. For example, CNNs are great for image recognition tasks, while NLP algorithms are used for language-based tasks. it's important to choose the right algorithm for the task at hand in order to get the best results.
There are four stages involved in building a machine learning model:
There's more data available today than ever before. But information overload can make it difficult to understand which data is most useful for your business. After all, data creation is estimated to grow from around 60 zettabytes in 2020 to 180 zettabytes by 2025.
Not all data is created equal, though. Less than 0.5% of all data is actually analyzed and used, according to The Guardian. So, how can you make sure you're using the right data to grow your business?
When collecting and sorting data, there are two fundamental factors to keep in mind: Quality and quantity. You want to make sure you have a large enough data set to do meaningful analysis, which typically means at least a thousand rows, although models can be built with less. And, you want to make sure the data is high-quality, which means that it's relevant to the use-case at hand, so the resulting model works well.
Akkio will automatically run your input data through data engineering pipelines, including for preprocessing, standardization, and tasks like creating a cross-validation dataset. It automatically detects missing values, identifies outliers, and cleans and transforms the training data so it's ready for data analysis and model optimization.
Further, Akkio lets you integrate with the data sources you already use to minimize the effort required to get your data. You can import data from tools like Salesforce, Snowflake, Google BigQuery, HubSpot, JSON, or a simple CSV.
Once you've collected and organized your data, it's time to create models. Akkio's drag and drop interface makes it easy to build an ML workflow and quickly see results.
Akkio's workflow editor allows you to build your model in an easy-to-understand visual interface. Traditionally, the model training process for machine learning (ML) has been opaque and difficult to reproduce. Even for experienced data scientists, it can be hard to know why a particular model performs well on a given task.
This is in part because the process of tweaking algorithms and parameters to get the best performance is a series of educated guesses, rather than a well-defined process.
The advantage of using a platform like Akkio is that it can take into account the vast number of variables that can affect the performance of a machine learning model.
For example, if you're trying to predict whether or not a customer will churn, you might consider factors such as how much they've spent in the past, how often they've interacted with your company, and how recently they last interacted.
Akkio can automatically select the most important features for a particular task and then optimize the model parameters, as well as hyperparameters (known as hyperparameter tuning), to get the best performance.
Bias in machine learning is a hot topic. Numerous studies have shown that algorithms trained on data with a certain bias can reproduce that bias when making decisions. So how can we be sure that our machine learning models aren’t discriminating against certain groups?
The good news is that there are ways to correct for bias in machine learning models. The first step is to evaluate model performance and identify any areas where the model may be less accurate. This can be done by splitting the data set into two groups — a training set and a testing set. The training set is used to train the model, while the testing set is used to evaluate the accuracy of the model.
Akkio does this process automatically in the background when building a model.
Once you’ve identified any areas of weakness, you can take steps to correct for bias. One way to do this is to add more data to the training set in order to reduce the influence of bias. Akkio will also automatically try different algorithms to train the model, and adjust the parameters of the algorithm to reduce bias.
One thing to keep in mind is that bias can be difficult to detect and correct for. It’s often not easy to identify which factors are causing the bias, and even if you do identify them, it can be difficult to fix them. So it’s important to be aware of the potential for bias and take steps to correct for it whenever possible.
When it comes to model performance, bias is just one factor to consider. There are a number of other factors that can affect accuracy, such as the quality of the data and the complexity of the model. So it’s important to evaluate all of these factors when assessing the accuracy of a machine learning model.
Akkio highlights model performance, so you can see accuracy metrics like precision and recall at a glance.
A model is only as useful as the predictions it makes. Once you've created a model, you need to deploy it in a way that makes its predictions available to the people who need them. In many cases, this means putting the model into a production system where it can be used to make decisions in real time.
Deploying a model isn't always easy. You need to make sure that the system can handle the load of running the model and that the data is available when the model needs it. You also need to make sure that the system can scale up or down as needed to accommodate changing demand.
In addition, you need to be sure that the model is accurate and reliable. You'll need to test it thoroughly before putting it into production. And you'll need to monitor it closely once it's in use, making sure that it's still producing accurate results.
Akkio can help you with all of these tasks. We provide a platform for deploying models in a variety of settings, from a web app to thousands of applications through Zapier. Our platform is scalable and reliable, and we offer a variety of ways to access predictions made by our models.
Churn is a pervasive problem for companies in all industries. It is costly and can be difficult to predict and prevent. For these reasons, it has been called the "silent killer" of SaaS businesses. In fact, a 1% increase in churn can result in a 27% decrease in profitability, according to Bain & Company.
There are many reasons why customers may choose to leave a company, including poor customer service, high prices, or a lack of features. Fortunately, there are some steps that companies can take to reduce their churn rate.
In this section, we will demonstrate how Akkio can be used to set up an end to end machine learning workflow for churn prediction.
The first step is to gather data. This data will be used to train and test the machine learning model. The data should include information on customers who have left (churned) and customers who have stayed (non-churned). We will use a demo customer churn dataset that’s pre-uploaded to Akkio.
Data is the fuel that powers our machine learning models. The more data we have, the better our models will perform. However, not all data is created equal. We need to make sure that the data is of good quality and is representative of the problem we are trying to solve.
Our demo dataset includes columns that are potentially indicative of a client's likelihood to churn. These columns include things like age, tenure, and services.
Of course, you can pull in your own dataset, from a variety of sources, such as Google Sheets, a CSV, or even tools like HubSpot, SalesForce, Snowflake, and more.
In Akkio, you can train a model by hitting “Add Step” once a dataset is connected, and then “Predict.” Then, simply select the column name you want to predict - conversion, churn, attrition, fraud, or any other metric. You also have the option to select a “Training Mode,” which ranges from 10 seconds of training time to 5 minutes, where longer training times may lead to more accurate models.
While the model is training, multiple algorithms and models are working in the background like pattern recognition, probabilistic approach, statistical reduction, random forest (a machine learning method that generates multiple decision trees on the same input features) - it all depends on what you wanted to predict and the quality of your data.
Once Akkio has built a model, you get a model report, including a “Prediction Quality” section. This showcases features of the model like accuracy (how often a prediction is correct), precision (number of true positives out of the predicted positives), recall (how many of the actual positives your model captures) and F1 Score (a weighted combination of precision and recall to balance the false positives and false negatives).
For a forecasting model report, the “prediction quality” will be shown as a value that predictions are “usually within,” as well as an RMSE score. Further, the “sample predictions” will be quantitative values, whereas a classification model report, like our example for churn prediction, will have classes like “Yes” or “No.”
With Akkio, businesses can effortlessly deploy models at scale in a range of environments. Akkio also has API’s to serve predictions in practically any setting, or directly in Salesforce, Snowflake, Google Sheets, and thousands of other apps with the power of Zapier.
For those who want to test out the waters with artificial intelligence (AI), but don’t have any data, Akkio offers a no-code AI trial that can get you started.
The trial is designed to give anyone, regardless of their data or coding experience, the opportunity to try out Akkio’s AI platform. Users can build and deploy models in minutes, without any prior experience in data science or coding.
The trial includes access to Akkio’s library of pre-included datasets, algorithms and visual tools. These "demo" AI Flows include pre-built models that can be used to predict customer behavior, identify text sentiment, and more.
Traditionally, these steps would require expertise in tools like Python Pandas, scikit-learn, and TensorFlow, but today, anyone can follow our simple tutorials to get started without any technical expertise.
As AI becomes more commonplace in business, organizations are looking for ways to get on board without hiring data scientists and programmers. Akkio offers a no-code AI platform that makes it easy for anyone to build and deploy intelligent applications. With Akkio, you don’t need to be a data scientist or programmer to use AI.
Akkio’s simple drag-and-drop interface makes it easy to build custom models without writing a single line of code. You can easily incorporate pre-built algorithms or build your own models using our library of data science functions. Akkio takes care of all the heavy lifting, so you can focus on getting the most value from your data.
Akkio is also fully integrated with popular business tools like Salesforce and Snowflake. This means you can easily bring AI into your existing workflows and get started quickly.
Akkio is the future of AI. Sign up for our free trial today and see for yourself how easy it is to use end to end machine learning for your business.