Cost-Efficient ML Adoption for Enterprises with AutoML Tools
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How AutoML Boosts ML Adoption for Businesses?

Artificial intelligence, machine learning, and predictive analytics provide practical solutions to a wide range of business goals. The development and integration of these solutions often raise challenges of a different nature. One of the biggest is significant expenses with high risks of failing to achieve the desired outcome. Investing funds into a full-scale project is risky. It makes sense to build a POC to validate the solution hypothesis at a lower cost before fully implementing it.

In this article, you will discover a cost- and time-efficient method of validating hypotheses for AI/ML solutions using automated machine learning technology ā€“ AutoML. Here you will learn how to create, train, and validate ML models in a significantly shorter time and at a lower cost than using traditional manual approaches.

What Is AutoML?

Automated machine learning (AutoML) automates the production cycle of machine learning solutions. It works to optimize manual model selection and tuning model hyperparameters, usually performed by data scientists. Hyperparameter values are used to control the process of model learning and determining the value of model parameters. This is a configuration external to a model and specified manually by a data engineer. AutoML iteratively searches for optimal hyperparameters and models until the termination criteria (or the desired accuracy) is reached. The general purpose of AutoML tools is to make ML-based tasks easier to solve, using less manual coding, and avoiding manual hyperparameter tuning.

AutoML does not replace data scientists. It is aimed to quicken their work, allowing for a more rapid solution definition and implementation, and improving the obtained results.

Traditional Machine Learning vs Automated Machine Learning

The growth of ML applications across industries and science drive rapid demand for AutoML tools. BusinessWire reports that the global automated ML market will grow from $665.63 million as of 2021 to $5,406.75 million by 2027, with a CAGR of 42.97% over the 2020-2027 period.

Many cloud platforms offer their proprietary AutoML solutions. For instance, Azure Data Studio, Amazon SageMaker, Microsoft Power BI, and Data Robot AI Platform. Here is a short overview of the available AutoML tools.

AutoML Solutions

Strengths

Limitations

Ideal Organization

Azure Data Studio

- Pay-as-you-go cost model

- Usability

- MLOps capabilities

- Strong enterprise data science capabilities

- Automatic prediction of the best pipeline for the labeled data

- Requires expertise in Azure services

- Limited augmented AI capabilities

- Inconsistent performance

- Poor performance in small datasets

An organization with insufficient budget to spend on the ML framework or one that does not have enough AI requirements to justify the investment in the end-to-end ML framework

Amazon SageMaker

- Pay-as-you-go cost model

- Algorithms marketplace

- A wide range of AI services (Amazon Lex, Polly, Transcribe, etc.)

- Less flexible and customizable than competitors

- Expensive

- No drag-and-drop features

An organization with insufficient budget to spend on the ML framework or one that does not have enough AI requirements to justify the investment in the end-to-end ML framework

DataRobot AI Platform

- Adaptability

- Easy-to-use UI

- High level of customization in building models

- Steep learning curve

- No augmented AI

- Lack of advanced analytics capabilities

- Expensive

Middle to large organizations looking for automation of ML models, pipelines, etc.

Microsoft Power BI

- Affordability

- Powerful report visualization capabilities

- Extensive database connectivity capabilities

- Limited modeling capability

- Not handling large data sources well

Organizations that already use Microsoft applications and need to realize ML-based solutions at less expense.

When deciding on a specific AutoML tool, you should take into account the technical needs of the particular project and what other services and software the organization uses. It is handier and often cheaper to use software from the same vendor.

How AutoML Helps in Building POC?

Traditionally, POC development implies hiring data scientists and other specialists, as it involves passing through almost all the stages of standard project development (excluding deployment and operationalization). It includes resource-consuming data analysis, data cleansing, and one or more modeling iterations. There is a cost-effective alternative ā€“ AutoML. Here are the stages where current AutoML systems already show or at least promise the best results.

ML Development Flow and AutoML
Machine Learning Development Flow with AutoML

AutoML can be effectively used for developing an ML model at the stage of project validation. While AutoML products do not completely provide a turnkey make machine learning service, AutoML attempts to streamline the overall process by automating some of the manually intensive steps in training a machine learning model:

  • Algorithm Selection
  • Feature Selection
  • Model Tuning
  • Model Evaluation.

Still, there are stages where AutoML cannot be used, such as problem definition, data understanding and acquisition, and data preparation.

AutoML accelerates the process of producing better models, not requiring a deep and detailed understanding of each algorithm. Most AutoML tools allow building a model through a simplified drag-and-drop environment with less customization and easier use. Data scientists can also use pure code to take advantage of automated machine learning and customize the script for further use in their projects. AutoML can provide either a final ready-made model or a starting point from which a data scientist fine-tunes the model.

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AutoML Solutions: Our Hands-on Experience

Infopulse data scientists actively use AutoML products to develop ML-based data analytics solutions for our clients across industries. Our data scientists obtain the required level of data engineering skills and profound AI expertise to deliver effective AutoML solutions that cover the needs of our clients. Here are a few examples of our AutoML-based projects.

Case 1: Sales Forecasting with Over 80% Accuracy

One of Ukraine's most prominent producers and exporters of agricultural products reached Infopulse to create sales forecasting models for effective planning and business process optimization. One of the project's key goals was gaining maximum accuracy of the produced forecasts.

Our data engineers defined suitable models based on the provided data sets. After training different models, we determined the most effective ones for weekly and monthly forecasting. The resulting solution allows making forecasts two months ahead with an accuracy of over 80%.

The Discrepancy of Weekly and Monthly Forecasts by Chosen Models
Distribution of predictions by quality

The solution helped our client optimize warehouse stocks and space and improve sales planning, promotional and advertising activities performance, and the use of capital.

Case 2: Hourly Forecasting of Electricity Consumption

Infopulse created an ML-based model for a large Eastern European electricity trader to forecast electricity consumption hourly to optimize pricing that dynamically changes according to the market demand. Our data scientists developed a solution that gives hourly energy consumption forecasts with an accuracy of more than 98% with a maximum deviation of 6.58%.

Hourly Electricity Consumption Forecast
Factual Electricity Consumption vs. Predicted Consumption

We used Microsoft Power BI, Microsoft Azure ML, VS Code, Jupiter, and other technologies to implement this project. Our client obtained the ability to optimize the price forecasting for electricity and increase the profitability of operations due to accurate planning.

Case 3. Infopulse Inventory Price Forecasting Solution

Inventory price forecasting allows for maximizing the funds' distribution efficiency based on historical data about past purchases. Within the proper algorithms and ML data model, we help our clients optimize inventory costs, achieve data transparency for controlling material resources and avoid any interruptions in manufacturing and customer service with adequately planned supplies.

We created a cloud-based solution powered by advanced data management and analytics capabilities that can be customized to a specific enterprise to generate forecasts for inventory pricing accurately. Our forecasting system can be integrated with multiple ERP sources to collect data for analysis.

Conclusion

AutoML can automate ML models' selection, training, and testing, thus saving large amounts of time and money on early AI/ML product development stages. It can also help organizations get their product and services to market faster and at a lower cost.

AutoML is not a complete substitution alternative to professional data scientists. It automates specific procedures and project stages, reducing the overall time and cost of ML-based solutions development. To use it successfully, you still need data science expertise. Infopulse provides advanced analytics and AI/ML services for intelligent automation, accelerating business decision-making, improving customer satisfaction, and opening new growth opportunities.

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About the Author

With over 7 years in IT, Oleksii developed a deep expertise in Data Science and Machine Learning, as proven by certifications from SAP and Microsoft. He applies his technical skills and comprehensive understanding of finance and production domains to conduct complex BI, Big Data, and Data Management projects. At Infopulse, Oleksii takes over management and delivery for our clients in agriculture, energy, and manufacturing industries.

Oleksii Burlaka

Cloud Data Architect

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