With many AutoML platforms in the market, it can be hard to pick one. We compare Trifacta and the AI & Analytics Engine, so you can choose the right...
Democratization of Data Science: What is AutoML? And How Does it Drive Accessibility?
AutoML is the future of machine learning that will empower start-ups & SMBs to leverage data to solve problems. AutoML is ideal for organizations that lack the resources and in-house teams to build algorithms from scratch
What’s the first word that comes to your mind when you hear the word machine learning?
Most people usually associate machine learning with automation i-e machines getting intelligent enough to do the tasks that are done by humans. While that is true, those people who truly understand machine learning know that it is a complex field that involves many iterative steps that are time-consuming and energy-draining.
Moreover, it requires people to draw knowledge and expertise from various fields such as statistics, computer science, linear algebra, mathematical modelling, coding and domain knowledge. Hence, it is often applied by a specialized group of people who are known as data scientists.
Building ML Algorithms is Not Practical for Most Organizations
Up till now, only large organizations have been able to harness the true power of a mature machine learning capability. That is because Fortune 500 companies and major enterprises have the resources and technical talent required to develop and operate their own ML applications.
These organizations have the means to employ teams of credentialed data scientists and engineers to build, deploy and manage custom ML algorithms, often using open-source tools like TensorFlow and the machine learning library for Python, Scikit-learn.
Getting a ROI on AI projects requires a rare combination of data science knowledge and talent, business acumen and deep knowledge of the problem to solve. Moreover, it is very, labour intensive full of manual processes requiring a high degree of technical skill.
Getting ROI from AI Projects is Hard
An example of such an AI project could include: A data scientist starting a project by manually importing data into a completely blank Jupyter notebook, performing exploratory analysis on the data, manually evaluating many alternate algorithms and engineering new features, then having to tune the model by hand.
There is a high level of risk and high level of investment into AI projects. And whilst the results are often accurate (some argue more accurate), there are only small gains compared to simpler tactics to solve the problem. It is really not surprising that AI projects are perused by large enterprises with large amounts of data, and even larger resources at their disposal.
AutoML Provides a Resource Effective, Flexible Solution
The rise of automated machine learning, also known as AutoML, is fast-changing the status quo. AutoML is empowering start-ups, SMB’s, business users and researchers to jump on the bandwagon and leverage data to build better products, identify opportunities within data, automate processes and improve decision making.
New technologies are already assisting to reduce the need for organizations to build AI and ML models from scratch. Instead, organizations are increasingly turning to developers and even non-technical employees who are wield powerful AutoML tools that automate many of the tasks a data scientist handles today.
AutoML is the process of automating the data pre-processing, feature selection, model validation, hyperparameter optimization, and model deployment steps to get production-ready ML models
How AutoML makes Machine Learning Accessible?
AutoML provides more flexibility than off the shelf ML applications, going further by compressing and automating the manual steps of the traditional machine learning flow. AutoML can empower engineers to incorporate data science elements into projects without data scientists, and empower data scientists to drop manual tasks an focus on high-value strategic ones. Increasingly AutoML solutions cater to a wider range of technical capabilities like The AI & Analytics Engine. This opens up access to AI for business users, the data curious, analysts and entrepreneurs, widening the pool of "citizen data scientists" and employees empowered to find solutions to data problems. Ultimately tools like The Engine will drive democratization of data science and AI by reducing the barriers to entry for building ML applications.
The AutoML Pipeline
1. Data pre-processing
Once you upload your dataset, an AutoML platform will automatically detect which features are numeric and which are categorical. It will impute the missing values for you and take care of data cleaning and data wrangling steps such as detecting outliers, one-hot encoding and standardizing data.
2. Feature Selection
When dealing with large datasets, you usually have to work with hundreds of features but not all of them are important. In machine learning, you have to try out different techniques such as finding correlations between the features and the target variable, using Principal Component Analysis (PCA) to reduce the feature space, or leveraging decision trees to assess feature importance to get rid of the redundant features. This is a very time-consuming process since you have to try out various techniques to figure out the optimal features.
AutoML does all this behind the scenes so you never have to worry about implementing and comparing different techniques. It simply gives you suggestions as to which features are irrelevant and you can simply drop them from the feature set.
3. Model Validation
As a data scientist, you need to run your data on a number of machine learning algorithms, evaluate their metrics and then draw a comparison as to which algorithm best serves your business use case. Most of the use cases in the industry relate to classification problems such as whether a customer will churn or not, whether a transaction is fraudulent or not etc. Hence, there is a range of machine learning classification algorithms that have to be tested such as logistic regression, decision trees, support vector machines, K-nearest neighbours, etc.
With AutoML, you can simply try out all possible algorithms with the click of a few buttons and compare the evaluation metrics on a single chart to identify the best model.
4. Hyperparameter Optimization
The performance of any machine learning model depends on its hyperparameters. A hyperparameter is a parameter that is set by the user in advance before the learning process begins. As a data scientist, you have to test out different combinations of hyperparameters using grid search or random search in machine learning. This is usually called tuning the algorithm.
AutoML will automate this intricate and cumbersome process for you, relieving you of the hassle to move back and forth to test different combinations.
5. Model Deployment
Once you have finalized your model, AutoML allows you to seamlessly deploy your model from within the platform and start making predictions without having to retrain the model manually as the production data evolves.
Want to Give AutoML a try?
If you want to implement machine learning but do not have a programming background are new to data science and it is impractical for you to learn a programming language, AutoML is the answer. In fact AutoML might be the answer if you are a seasoned data professional, reducing the steps and effort between you and what you want to achieve data.
If you're ready to give AutoML a go, we have built an all-in-one platform that takes care of the entire machine learning lifecycle. You do not need to have any technical expertise as it is a highly intuitive, user-friendly platform. From importing a dataset to cleaning and pre-processing it, from building models to visualizing the results and deployment, The Engine is geared to accelerate and simplify the journey from raw data to insights. The goal? To make machine learning and data science accessible to everyone.
You can build your own machine learning model and start making predictions
in a few minutes instead of hours. Leverage AutoML to automate the repetitive,
mundane tasks of the machine learning lifecycle and build models in a fraction of the time it takes using traditional methods