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In the Age of AutoML, we are all Data Scientists! | Artificial Intelligence (AI) has become increasingly essential to our daily routine lives.

AutoML: the Accurate, Quick and business-savvy Machine Learning

Published on Mar 25, 2020 by Christelle Julias

Quick and accurate business insights => Competitive edge

Quick business insights are the tenets of every modern enterprise, and are forever, are at the core of sustaining a competitive edge.

While endeavoring to do so, Small and Medium Businesses continuously face the pressure of delivering high performance, while being confronted with the challenge of time-paced technological changes.

This implies the necessity to embed a form of Artificial Intelligence such as Machine Learning and Deep Learning into their data analysis systems, in order to stay on track with their competitors.

However, nor the budget for the required hardware architecture, nor the skillset for mastering these technologies could be taken for granted. All of those require a lot of investment in terms of computer resources and staff training which simply eliminates those who feel reluctant to do so.

The biggest stake on the table is that trends are ever-evolving and those who cannot afford to follow them, need to outpace themselves at the risk of being beaten to the seam in this race against time.

That’s why new technology like AutoML comes in handy and turns out to be an effective solution for companies to leverage AI for their domain.

What is this Automated Machine Learning or AutoML?

As the name suggests, AutoML or Automated Machine Learning is a process that automates all the steps in the development of a machine learning project until it is ready to be deployed into the IT environments for production. Normally, Machine Learning projects have to be coded and studied by experts, but with AutoML all the following steps are performed almost automatically with low code or with a simple few clicks: 

  • Data preprocessing
  • Feature engineering
  • ML algorithms (ML model)  selection
  • Model training 
  • Model tuning
  • Model Evaluation

In blue you’ll find the processes you will work manually in each type of Workflow:

AutoML a friend or an enemy?

Challenges regarding AI implementation are numerous but there are ways to overcome them. There exist some alternatives, some smarter, cheaper and relatively easier ways of ethically cheating in the AI race with the introduction of AutoML.

AI pioneers and giants such as Google, Amazon, and Microsoft inter alia, have already designed their own cloud solutions and gained a large base of regular users.

The AutoML technology first received controversial reception among the data scientist community in which tremendous questions were raised such as: will it either be “a friend” or a “mere enemy”? Somehow, with time, most found the numerous and undeniable advantages of incorporating AutoML in their toolsets.

Apart from offering a higher degree of automation for ML modeling and for deploying intelligent projects, AutoML or Automatic Machine Learning has been conceived to target the issues of ​ time-consuming​ and obviously ​ costly operations.

AutoML could in some way be a path for achieving quality Artificial Intelligence automation without the need of hiring expensive staff while providing users with the relief of affordable Artificial Intelligence integrations.

In other words, AutoML democratizes ML i.e renders it accessible to the mass, and enable the complex machine learning and deep learning algorithms that used to be reserved for domain experts.

What about SmartPredict AutoML?

As a low-code, no-code AI platform, SmartPredict has implemented its AutoML and called it Autoflow: or machine learning workflow. This means that anyone in any field can take advantage of State of the art technology with just a few clicks, based on their business area, such as AI-driven sales forecasting.

With SmartPredict AutoML, you can complete an end-to-end AI project, such as time series prediction, and time series forecasting, in minutes with a single click and simple configuration.

Solving Classification Problems, Regression Problems with Autoflow, Natural Language Processing Is being developed by our experienced data scientists.

Simple SmartPredict AutoML Autoflow configurations for sales forecasting:

All machine learning steps performed by SmartPredict AutoML (Autoflow):

What makes SmartPredict AutoML different?

In addition to the Machine Learning process automated by SmartPredict AutoML (Autoflow), there are differences in functionality that you can only benefit from as a user or as a company:

- 100% customizable:

SmartPredict AutoML Autoflow generates the results of its work as 100% customizable flowcharts that run automatically to train, evaluate and deploy ML models. What makes it different is that you can customize it with Python code or by dragging and dropping SmartPredict pre-builts modules. So you can run the flowcharts at will to train and update the model deployment 

- No vendor lock: the deployed AI performed project is easy to implement in any kind of platform whether it’s in an enterprise system or a website as SmartPredict generates automatically the API of the deployed model in minutes.

To see more about the platform see SmartPredict’s website and demo.

Start your Autoflow AI project for free!!

Recommended reading

To learn more about the AutoML, discover in these articles all about this recent business solution and off-the-shelf Machine Learning package of frameworks and services: Freiburg-Hannover

AI Multiple

Towards Data Science: What is AutoMl by Siobhàn K Cronin

The Death of Data Scientists – will AutoML replace them?

3 Reasons Why AutoML Won’t Replace Data Scientists Yet