Dublin, May 24, 2023 /PRNewswire/ — “Automated Machine Learning (AutoML) Market” (Solutions and Services), Applications (Data Processing, Model Selection, Hyperparameter Optimization and Tuning, Feature Engineering, Model Ensemble), By Industry, By Region – Global Forecast to 2028 report added of ResearchAndMarkets.com Recruitment.
The market for automated machine learning is billion dollars by 2023 $6.4 billion By 2028, it will grow at a CAGR of 44.6% during the forecast period.
Explainable AI is a key aspect of AutoML that aims to provide transparency into how machine learning models make predictions. By using explainable AI techniques such as feature importance and decision trees, companies can gain insight into how their models work and make more informed decisions. can do.
The BFSI vertical market is projected to be the largest market during the forecast period.
AutoML automates time-consuming repetitive tasks, builds machine learning models with productivity, efficiency, and scale, and minimizes the knowledge base resources required to implement and train machine learning models. In addition, it is an emerging technology used in the BFSI field.
AutoML can be used for credit card fraud detection, risk assessment, and real-time investment profit and loss forecasting. AutoML also helps reduce deployment time by automating data extraction and algorithms, eliminating manual parts of analysis, and significantly reducing deployment time. For example, Consensus Corporation reduced deployment time from 3-4 weeks to 8 hours with AutoML.
By minimizing the potential for error and bias in the BFSI sector, AutoML helps companies improve insights and improve model accuracy. AutoML brings several advantages to the BFSI industry. This reduces the need for complex and time-consuming manual data science processes and accelerates the work of data scientists. AutoML also helps optimize business performance based on data, enabling business leaders to make decisions with real-time analytics.
Among applications, the model ensemble segment is recorded to grow at the highest CAGR during the forecast period.
AutoML for model ensembles involves using automated techniques to create collections of models that can be combined to improve predictive accuracy. Ensemble is a common technique in machine learning that combines predictions from multiple models to produce a more accurate final prediction. AutoML allows various techniques for model ensembles, such as bagging, boosting, and stacking.
AutoML can automatically create multiple models with different algorithms and hyperparameters and combine them using ensemble techniques. This reduces the risk of overfitting and leverages the strengths of different algorithms, thus increasing the robustness and accuracy of the final model.
An advantage of using AutoML for model ensembles is that it saves data scientists time and effort by automating the process of model selection and combination. AutoML can also evaluate the performance of different ensemble techniques and choose the technique that performs best on a given dataset.
Among services, the consulting services sector is expected to account for the largest market size during the forecast period.
Consulting services are typically provided by third-party vendors or consulting firms to provide expertise and guidance on machine learning strategies and implementations. Consulting services help organizations assess data readiness, identify use cases, and develop a roadmap for implementing machine learning within their organization. AutoML Consulting Services help organizations navigate the complexities of machine learning tools and platforms and make informed decisions about which tools and technologies to use based on their specific needs and goals. .
Consultants can also guide data preparation, model selection, and hyperparameter tuning, and can help organizations assess the performance and effectiveness of machine learning models. Our consultants work onsite or remotely and provide ongoing support and guidance throughout the machine learning lifecycle. By providing expertise, guidance, and education, consultants help organizations make informed decisions and achieve better results with their machine learning efforts.
North America It will account for the largest market size during the forecast period.
North America is estimated to hold the largest share of the automated machine learning market. The global automated machine learning market is dominated by North America. North America The United States is the most profitable region and accounts for the highest market share in the global automated machine learning market, followed by the United States. Canada.
The region has a high adoption rate of machine learning and artificial intelligence technologies across various industries such as healthcare, finance, and retail, which is expected to drive demand for AutoML solutions. Additionally, the presence of numerous data-driven start-ups and enterprises in the region is further fueling the growth of the AutoML market. North America.
Market trend
driver
- Increased Customer Satisfaction with Automl and Growing Demand for Personalized Product Recommendations
- Growing Need for Accurate Fraud Detection
- Growing data volume and complexity
- The Growing Need to Transform Your Business with Intelligent Automation Using Automl
Restraint
- Delayed adoption of machine learning tools
- Lack of standardization and regulation
chance
- Growing demand for AI-enabled solutions across industries
- Seamless integration between technologies
- Improving the accessibility of machine learning solutions
Theme
- Growing shortage of skilled labor
- Difficulties in Interpreting and Explaining Automl Models
- Growing threats to data privacy
Case study analysis
- Ascendas Singbridge Group Leverages Datarobot’s Automl Platform to Improve Real Estate Decision Making
- G5 Adopts H2O.Ai’s Driverless AI Platform to Address Challenges in Identifying Productive Prospects
- Robotica helped Avant automate key processes and streamline lending operations
- Domestic and general companies partner with Datarobot to improve customer service capabilities
- H2O.Ai’s Machine Learning Platform Boosts PayPal’s Fraud Detection Capabilities
- California Design Den partners with Google Cloud Platform to implement machine learning solutions
- Contentree helped Consensus simplify and streamline the data wrangling process
- Datarobot’s Automated Machine Learning Platform Helps Demyst Automate Data Science Processes
- Datarobot Helps Evariant Automate Patient Risk Stratification and Readmission Prediction
- Meredith Corporation worked with Google Cloud to build a data analytics platform to process massive amounts of data
- Dmway enables Pgl to integrate and analyze data from multiple sources
- Sparkcognition helped build predictive models for the oil and gas industry with automated machine learning solutions
companies mentioned
- Able
- Akkio
- alibaba cloud
- Altair
- alterix
- Apia
- AWS
- Baidu
- big mul
- Boost.AI
- data brick
- data fold
- Dataiku
- data robot
- dot data
- H2O.AI
- HPE
- IBM
- mass works
- microsoft
- Oracle
- Qlik
- Salesforce
- Service Now
- spark cognition
- Squawk
- Taj Ai
- Teradata
- Valohai
For more information on this report, please visit https://www.researchandmarkets.com/r/qw1kw1.
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