Top 10 Mobile App Development Trends for 2026

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Top 10 Mobile App Development Trends for 2026

Top 10 Mobile App Development Trends for 2026

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mobile app development trends

Every year, new technologies reshape the way we use our mobile phones, and 2026 is no exception. If you are willing to create an app or just curious about the future of mobile technology, then staying updated with the latest mobile app development trends will be considered as a smart choice. These trends are not buzzwords but instead play a role in shaping the way individuals utilize apps, how they are developed by developers and how companies can expand through mobile platforms.

The mobile app world is changing at a quick pace, and 2026 will bring to us some of its most game-changing innovations. From smart AI features to faster and safer user experiences, mobile applications are becoming more effective and helpful in our day-to-day lives. This blog will walk you through the top 10 trends in mobile app development in 2026. 

Want to turn your mobile app idea into a powerful digital solution, but unsure where to start or whom to trust? Mindpath offers expert mobile app development services, building scalable applications tailored to your business needs.

Before learning about the trends, we must have a quick look at the market value of mobile app development: 

Interpreting the Mobile App Development Market Value

According to a study by Allied Market Research, the worldwide mobile app development industry was valued at $195.7 billion in 2023 and is likely to grow to $606.1 billion by 2032. This growth, with a CAGR of 13.1% between 2024 and 2032, is primarily because an increasing number of people are using smartphones and wish to have apps according to their individual requirements.

mobile app development google trends
Mobile App Development Google Trends

New technologies are helping to make apps smarter and more useful. More people shopping on phones and working remotely after the pandemic has also made mobile apps more popular around the world. Let’s see what are the latest trends for mobile app development in 2026.

Curious about the latest tools shaping modern mobile app development and how they impact performance and scalability? Read our blog on Mobile App Development Tools to explore the frameworks, platforms, and technologies developers rely on to build powerful apps.

Latest Mobile App Development Trends in 2026

The future of mobile app development is looking brighter than ever, with groundbreaking trends that are set to reshape the way we interact with technology. As we move forward, these emerging trends will drive innovation as well as set new standards for app performance and user experience. Let us take a look at it:  

1. Artificial Intelligence (AI)

Artificial intelligence (AI) in mobile app development is revolutionizing in 2026. With advancements such as Apple’s Core ML 3, developers are now able to more easily incorporate AI into their apps. AI will make apps smarter, enhancing performance and user experience, and will be a driving force in defining the future of mobile applications.  

2. Augmented Reality (AR) and Virtual Reality (VR)

Augmented Reality (AR) and Virtual Reality (VR) will be big trends in mobile app development in 2026. AR is becoming more practical and useful across different industries, while VR is enhancing gaming experiences on mobile devices. Both technologies are helping create more interactive and engaging apps. As these trends grow, we’ll see AR and VR shaping the way users interact with apps in exciting new ways. Based on research by Statista, revenue in the AR & VR market worldwide is projected to reach US$46.6bn in 2026, with an annual growth rate (CAGR 2026-2029) of 7.42%, leading to a projected market volume of US$62.0bn by 2029. 

3. Predictive Analytics

Predictive analytics is transforming how mobile apps work. By using AI and data analysis, apps can now predict what users want or need, offering personalized experiences. In 2026, this technology is widely used to improve app interfaces and user interactions. Apps will offer content and suggestions based on each user’s actions, making them feel more intuitive and relevant. 

4. Cloud Computing Integration

In 2026, cloud computing will become a key trend in mobile app development. It allows apps to store data and perform tasks on the cloud, rather than relying on the user’s device. This will not only improve app performance but also make development more efficient and cost-effective. As cloud technology continues to grow, it will play a major role in shaping mobile app functionality. According to Precedence Research, the global cloud computing market was valued at USD 753.11 billion in 2024 and is projected to grow to approximately USD 5,150.92 billion by 2034, with a CAGR of 21.20% between 2026 and 2034. 

Also Read: Cloud Computing Trends

cloud computing market

5. Chatbots

In 2026, chatbots will see widespread adoption in mobile apps, enhancing customer service and user interactions. Driven by AI, they will provide more natural, human-like responses, improving efficiency and responsiveness. This integration will streamline support processes and elevate the overall app experience, making it faster and more intuitive for users. 

Also Read: Benefits of Chatbots

6. 5G Technology

The rollout of 5G in 2026 will enhance app performance by providing faster speeds and lower latency. This will enable apps to support more advanced features and deliver smoother user experiences. With improved network efficiency, users can expect faster data processing and better overall functionality. The faster connectivity will also open doors for new, innovative app capabilities. 

7. Internet of Things (IoT)

The Internet of Things (IoT) is transforming how we interact with everyday devices, offering more convenience and automation. From controlling home appliances to managing security systems, IoT is becoming integral to modern living. As more devices become interconnected, the potential for smarter homes, workplaces, and healthcare solutions will continue to grow, creating a more seamless and efficient experience for users. 

8. Low-code /No-code Development

Low-code/no-code development is revolutionizing how apps are created by simplifying the process and allowing users with little to no coding experience to build apps. This approach speeds up development significantly, making it easier to create and modify apps quickly. By empowering business stakeholders and non-developers to participate in the process, it ensures the final product better meets user needs and business goals. Based on Statista statistics, the global low-code platform market is expected to reach around 65 billion U.S. dollars by 2027, reflecting its rapidly growing demand. 

9. Mobile App Security

Mobile app security is crucial as more businesses use apps to handle sensitive customer data. Vulnerabilities in apps can expose users to cyberattacks, leading to financial loss, fines, and a damaged reputation. To protect apps, developers must integrate strong security measures such as encryption, multi-factor authentication, and secure coding practices. Regular updates and testing are also essential to maintaining robust security. 

10. Apps for Foldable Devices

Foldable devices are making a strong comeback, with popular models like the Samsung Galaxy Z Fold 5, Huawei Mate X5, and Motorola Razr 40 leading the way. These devices allow users to adjust the screen size, providing both portability and an expanded experience. As their popularity grows, the demand for specialized mobile apps will rise, requiring optimized interfaces that adapt seamlessly as screens fold and unfold. 

Ready to discover how modern mobile apps are built? Explore the latest Mobile App Development Technologies that power innovative and user-friendly applications.

Embrace the Future of Mobile Apps with the Latest Technology and Trends

The future of mobile technology is full of exciting changes. The top trends in mobile development for 2026 show us how fast the industry is moving. From smarter AI to apps for foldable devices, each trend brings new ways to build better user experiences. Staying updated with these trends in mobile development is important for creating apps that are fast, secure, and user-friendly. 

At Mindpath, we specialize in using the latest technology for mobile app development. Our team follows the latest trends in mobile development to deliver apps that are modern, efficient, and built for the future. If you’re looking for a mobile app development company your search ends here. We at Mindpath have a team of professionals with expertise in creating AI-powered apps, apps built for 5G, or solutions optimized for foldable devices, we’re here to turn your idea into a reliable mobile solution. 

Frequently Asked Questions

1. How can businesses prepare for mobile app development trends in 2026?

To prepare for mobile app development trends, businesses should focus on scalability, user-centric design, and emerging technologies like AI and cloud integration. Conducting market research, upgrading technical infrastructure, and partnering with skilled developers can help ensure your app remains competitive and future-ready.

2. Will 5G completely change how mobile apps are built?

5G will significantly influence app architecture by enabling real-time features, high-quality streaming, and faster data processing. Developers may design more cloud-dependent and feature-rich apps. However, apps must still be optimized for users in regions where 5G connectivity is limited.

3. Are low-code and no-code platforms reliable for professional apps?

Low-code and no-code platforms are suitable for prototypes, internal tools, and simple applications. For complex, scalable, and high-performance apps, professional development is often required. Businesses should evaluate project complexity before choosing the right development approach. 

4. How important is security in modern mobile app development trends?

Security is a critical part of mobile app development trends as apps handle sensitive personal and financial data. Implementing encryption, secure APIs, regular testing, and compliance standards is essential to build user trust and prevent data breaches in an increasingly connected environment.

5. Should startups invest in emerging technologies like AR and IoT right away?

Startups should first evaluate their target audience and business goals. While AR and IoT offer innovative experiences, they may not be necessary for every app. Investing strategically in technologies that directly improve user value and solve real problems is the smarter approach.

Related Post

generative ai in hr

Businesses want to prepare for the future by embracing new technologies like AI in different business functions. You can notice the growing influence of generative AI in HR operations as organizations transition from traditional processes to smarter and more adaptive models. Generative AI not only provides automation but also the ability to extract actionable insights from large datasets and transform hiring practices.

According to Gartner, the number of HR leaders planning or actively deploying generative AI increased from 19% in June 2023 to 61% in January 2025 (Source). The meteoric growth in generative AI adoption showcases that more HR professionals have been recognizing the power of generative AI to redefine HR processes. You can learn more about the significance of gen AI in HR by unraveling its use cases and benefits.

Looking to leverage Generative AI to boost efficiency and innovation in your business? At Mindpath, we provide custom Generative AI solutions to help your business grow.

Understanding the Impact of Generative AI in HR Processes

Generative AI is gradually becoming an inseparable component of modern HR operations. It has introduced the benefits of automation, scalability and intelligence that bring huge transformation in traditional HR processes. You must discover the utility of generative AI for HR by identifying how it helps in defining hiring practices for the future. Strategic implementation of generative AI can help HR professionals design future-proof hiring practices.

1. Faster Resume Screening

The thought of going through hundreds and even thousands of resumes manually gave sleepless nights to recruiters. With generative AI, screening resumes has become a lot easier as generative AI tools can help with automation.

HR professionals can leverage generative AI for automatic analysis of resumes and identifying suitable candidates on the basis of skills, cultural fit and experience. As a result, the shortlisting process will take lesser time than the traditional process while also reducing human error.

2. Creating Perfect Job Descriptions

Developing good job descriptions is also a critical part of the recruitment process. HR leaders have to go through various struggles to create perfect job descriptions. The significance of generative AI in human resources becomes clearly evident in how generative AI tools can create role-specific and accurate job descriptions.

Generative AI tools can evaluate existing data and labor market trends to craft descriptions that will attract the right applicants. HR managers can also rely on generative AI for reducing bias in the creation of job descriptions. The ability to reduce bias can help organizations craft inclusive job descriptions that will attract the right candidates.

Want to use AI more effectively across your business? Learn how generative AI tools can streamline content creation and boost efficiency.

3. Delivering Real-Time Employee Guidance

Employee onboarding is probably one of the prominent stages in the hiring process and also presents the biggest concerns for HR leaders. The use cases of generative AI in hiring can help in moving over the limitations that come with traditional onboarding documents. Generative AI can help in creating personalized onboarding journeys for new recruits, which help with faster onboarding.

HR teams can provide interactive walkthroughs and orientation programs to new employees with generative AI. The personalized guidance provided by generative AI tools streamlines the onboarding journey. AI chatbots can serve as helpdesks in HR portals and offer instant answers to queries about leave policies, appraisals, and compliance and remuneration details.

4. Streamlining Performance Reviews

The ability of generative AI to transform hiring practices for the future is also evident in how it transforms performance reviews. You can witness the future of hiring taking shape right now in the use of generative AI tools for performance assessment. The biggest reason to trust generative AI for employee performance reviews is the assurance of freedom from unconscious bias in the review process.

HR departments can also use generative AI for comprehensive evaluation of employee performance on different relevant metrics. Generative AI tools offer an accurate evaluation of how employees perform in different business functions. HR leaders can leverage generative AI to create new performance management frameworks with a focus on consistency.

Are you prepared for the next wave of AI-driven transformation? Discover key generative AI predictions shaping the future of technology and work.

Unraveling the Top Benefits of Generative AI in HR Management

The biggest benefit of adopting generative AI in the domain of HR comes from replacing manual and repetitive workloads. Generative AI can help HR professionals focus on more strategic functions, such as employee well-being, talent development and succession planning. As a matter of fact, embracing generative AI can not only help employees but also the organization with multiple advantages.

  • Smarter Decisions Powered by Data

The combination of generative AI and human resources empowers HR professionals with the power of data. Generative AI offers the capabilities required to analyze large datasets and find actionable insights that help in measuring employee satisfaction and attrition rates. HR professionals can make decisions supported by evidence rather than relying on intuition alone in workforce planning.

  • Reduced Costs and Efficient Operations

HR departments can discover new ways to reduce costs by adopting generative AI. You can notice how generative AI tools help with automation of repetitive tasks that require human resources. With generative AI, you won’t have to allocate personnel for scheduling interviews, data entry and processing leave requests. The reduced human intervention in different HR processes plays a vital role in enhancing efficiency. In addition, automation also helps in achieving significant reduction in administrative costs.

Wondering how generative AI innovations outside HR could impact your organization? Explore more insights in Generative AI Revolution to see how these innovations are transforming the business landscape.

  • Enhanced and Consistent Employee Experiences

The best thing about transforming HR operations with generative AI is the ability to deliver personalized support to employees. You can figure out how the role of AI in HR is not limited only to benefits for the organization in the different ways in which generative AI helps employees. Generative AI systems can provide instant answers to HR queries of employees and provide custom training plans, thereby enhancing engagement. On top of it, HR teams don’t have to worry about consistency of communication and service delivery with generative AI tools.

  • Creating More Diverse and Inclusive Workplaces

HR leaders all over the world have been wondering about the ideal approaches to promote diversity and inclusivity in the workplace. Generative AI can provide the ideal solution to eliminate gender-biased language in job descriptions. It also helps in reducing bias in candidate screening procedures and performance assessments. The utility of generative AI also empowers employees with equitable access to professional development opportunities.

How Do You Think Generative AI Future-Proofs HR Practices?

The most pressing concern for HR leaders in every industry now is about adapting to emerging trends and new technologies. You wouldn’t want to take the risk of missing out on the benefits of generative AI in HR operations when your competitors are actively deploying AI in their HR departments. The best way to understand how generative AI transforms hiring practices for the future will require examples of gen AI applications in HR.

  • Smart Resume Parsing

Generative AI can make resume screening a lot faster and more efficient. HR departments will not struggle to assign workers for sifting through resumes and identifying the right candidates. Generative AI tools can do the heavy lifting and extract relevant details from applicant resumes to choose candidates with relevant skills. As a result, HR professionals can eliminate unqualified candidates in early stages of the hiring process.

  • AI Taking Over as the Interviewer

Interviews are also one of the notable areas where generative AI can showcase transformative impact on hiring practices of an organization. As a matter of fact, generative AI tools can take over the role of interviewers in the preliminary interviews. HR managers can rely on generative AI tools to ask role-specific questions, create candidate reports and evaluate sentiment. The efficient use of generative AI can not only save time for recruiters but also drives decisions supported by data.

  • Enhancing the Experience of Candidates

The growing use of generative AI for the transformation of hiring practices will also bring positive outcomes for candidates. Generative AI models focus on skill-based valuations while removing unconscious human bias. In addition, candidates can find complete transparency in the hiring process with access to information about interview schedules, assessment criteria and feedback.

Curious about the broader applications of generative AI in the corporate world? Learn from Generative AI for Business how AI is transforming workflows, strategy, and overall business performance.

Final Thoughts

The use cases of generative AI in human resources operations will play a major role in creating new hiring practices for the future. Generative AI can streamline the hiring process by facilitating automated resume screening, easier employee onboarding and enhancing inclusivity. HR managers must acknowledge the benefits of generative AI in hiring processes and discover the ideal ways to incorporate generative AI in their workflows.

Mindpath is a trusted provider of generative AI solutions for different business functions. We have been catering to the needs of businesses seeking new avenues for digital transformation with generative AI. Our bespoke solutions have helped clients achieve desired results with optimal use of resources. Consult with our expert and build your generative AI solution for HR workflows now.

FAQs

1. How does generative AI transform HR?

Generative AI helps in creating materials that would have taken days or weeks with traditional methods. HR departments can create relevant job descriptions or interview questions for specific roles within seconds. Generative AI can also help in creating personalized training materials for new employees during the onboarding process.

2. What are the benefits of generative AI in HR?

Generative AI transforms the HR operations with benefits of data-driven decision making and automation. HR teams can use generative AI to take decisions based on data rather than intuitions. Automation of repetitive tasks in HR departments will help in reducing costs and improving efficiency and productivity.

3. What will employees gain from use of generative AI in HR?

Employees will find a significant improvement in their experiences with the adoption of generative AI in HR. The applications of generative AI tools in different HR functions will introduce the advantage of personalization. Employees can find instant responses to their queries while also leveraging the benefits of reduced administrative burden and employee friction.

4. How does generative AI transform performance management in HR?

Generative AI systems can serve as ideal tools for delivering continuous feedback in performance management plans. HR teams can use these systems to conduct regular performance conversations and tracking feedback patterns. Businesses can leverage generative AI to develop more consistent and dynamic performance management approaches driven by data-backed decisions.

5. Does generative AI present any risks for the future of HR?

While generative AI brings a lot of benefits to the table for HR departments, it also presents some critical risks. The notable risks of adopting generative AI in human resources include bias and limitations in data privacy. On top of it, the resistance to change is also one of the biggest risks that come with initiatives for adopting generative AI.

Explore how Generative AI in Human Resources is revolutionizing recruitment, making HR processes smarter and more efficient.
MLOps essentials

The global artificial intelligence market size is expected to reach USD 2480.05 billion by 2034. Yet many organisations fail to translate such huge investments in favour of their business values. Many generative AI projects are going to lose their efficiency recently due to challenges like poor data quality.

Further, inadequate risk controls have been increasing the costs. So, the major issue lies in filling the gap between predictive models and implementing them into your business workflows. This is where the relevance of MLOps essentials comes into the picture. MLOps have the capacity to align the life cycle of machine learning while incorporating continuous monitoring.

Looking to integrate AI into your existing systems without disrupting operations? At Mindpath, we offer AI development services that ensure smooth implementation and measurable results.

What is MLOps?

MLOps, or Machine Learning Operations, is a set of practices that combines machine learning, software engineering, and DevOps to automate and standardise the entire lifecycle of AI models. It bridges the gap between model development and production deployment, streamlining how you test, deploy, and maintain models for reliable and efficient results.

Unlike traditional ML development, where models were often operated in a single system, MLOps treats the complete lifecycle of ML as a software product lifecycle. This means you can be assured of better collaboration, reproducibility, scalability and automation.

  • MLOps can help your team take ML models from experimentation to production level safely and quickly.
  • It manages the models reliably over time and collaborates across engineering, data science and operations teams.

How MLOps Essentials Overpowers the Traditional DevOps while Solving Leading ML Challenges?

MLOps and DevOps take complete responsibility for automating processes while encouraging collaboration between teams. But MLOps is capable of tackling unique challenges of machine learning that DevOps can’t necessarily address.

  • In traditional software systems, once you deploy your app, it is mandatory to work within a stable environment with minimal challenges.
  • However, ML models heavily depend on data and need continuous updates and validation when new data enters.
  • DevOps prioritises managing the software code and the infrastructure. On the other hand, MLOps essentials include handling the lifecycle of models that can degrade over time.
  • This involves addressing many major issues like data drift, model degradation, and model performance. All of these risks need periodic retraining and real-time assessment.

Planning AI initiatives but unsure where to start? Explore the complete AI roadmap guide to understand how to structure, prioritize, and scale your AI initiatives effectively.

What are Some Core Components of an MLOps Architecture?

The MLOps architecture can differ as per your organisational requirements. Some of the major components are here in this section for a complete reference.

1. Data Layer

This involves storage, data ingestion, preprocessing and versioning. As your models completely depend on data, tracking and handling their quality is necessary.

2. Development Environment

This is where the data scientists create and train models using their notebooks or integrated development environments. So, your experiments must be reproducible. This means the data, code and the environment settings are versioned.

3. Model Registry

This works as the main repository where the trained models are stored along with the metadata like deployment status. This feature can ensure you of data governance.

4. Monitoring

Once you complete the deployment process, your models must offer you accurate predictions. Moreover, precise and continuous assessment can offer you better performance while predicting potential defects. So, your model can also be relevant in the real-world usage.

5. Data Regulations

The regulatory efforts have been broadening due to major AI usage. So, your organization must prioritize relevant rules. Following the relevant practices can help your model serve trustworthiness.

Facing challenges in optimizing large models for scalable deployment? Learn how Low-Rank Adaptation streamlines fine-tuning with minimal resource overhead. 

List of Top MLOps Tools You Should Adopt in 2026

Let us further highlight some of the best MLOps tools for your model development and monitoring. All these tools have the ability to simplify and streamline the ecosystem of your machine learning.

1. LangChain

It is a versatile and effective framework developers use to initiate apps powered by language models. It leverages several components that enable you to build, deploy and monitor context-rich applications. The following framework consists of some major components like libraries, templates, LangServe and LangSmith.

2. Qdrant

This is one of the best MLOps tools in 2026 that is taking the market by storm. It is an open-source search engine that offers a production-ready service with a viable API. Moreover, it allows you to store, manage and search the vector embeddings. As it is written by Rust, it remains extremely quick and efficient even you work with it under high load.

3. MLFlow

MLFlow is an open-source tool that enables you to handle the core parts of the ML lifecycle. Developers majorly use it for experiment tracking. However, you can also utilise it for deployment, reproducibility, and model registry.

4. Prefect

When talking about MLOps essentials, this tool leads the competitive edge. This is a modern data stack, people use for coordinating and monitoring the workflows across multiple applications. It is a light-weight and open-source tool created for an end-to-end MLOps pipeline.

Must Read: Low Code Developement 

What are Some Benefits of MLOps Service?

The MLOps market size is going to extend to USD 25.93 billion by 2034. So, the adoption of MLOps has been increasing efficiently while fueling model deployment and automating monitoring. Businesses now understand that adopting MLOps can bring them a series of benefits, as given below.

1. Better ROI

As per the reports from Deloitte, organisations using MLOps technology claim an average ROI of 28% with better returns. This remains consistent with the growing demand of the market trends, where AI and ML are going to drive business value.

2. Increases Efficiency

Around 94% of companies initiate time-consuming and repetitive tasks. So, MLOps service can automate such processes while boosting your job satisfaction level. Further, this allows your team to prioritise strategic activities while improving your operational efficiency.

3. Faster Delivery

MLOps essentials allow you to lessen the deployment times by automating your CI/CD pipelines. This enables your business to adapt quickly as per the market changes and use AI-driven insights without any delays.

4. Serves Cost-Effective Results

Automating the manual tasks through MLOps architecture, you can minimise the chances of human error and high operational charges. Additionally, it allows your business to simplify the workflows while allocating the resources in a better way.

Want to make your model deployment process more environmentally responsible? Explore how energy-efficient computing drives sustainable innovation.

How to Implement MLOps Seamlessly?

You need to value a structured approach when it comes to MLOps implementation. Here, you can go through some major steps that serve efficiency.

1. Get Your Data Prepared.

Before anything else, you need to prioritise the quality of your data. Start by gathering, cleaning and organising data to make them ready for action.

2. Create Your ML Model

Now that you have the best shape for your data, it’s time for you to leverage a model. So, prefer your features, algorithms and fine-tune the hyperparameters to gather the best results.

3. Deploy Your Model

Deployment is all about leveraging your model in the real world for a service or business application.

4. Assess the Performance

ML models might differ in their performance when the conditions change. So, you need to continuously monitor its accuracy.

5. Retrain for the best Results

Retraining your model time to time can help you get accurate and relevant data that can fulfil your current customers ‘requirements.

Curious about which AI models can accelerate your machine learning workflows? Learn more about the top AI models and how they fit into effective MLOps practices.

Conclusion

Whether you are looking to automate the process or to enhance your model’s governance, MLOps essentials remain the major aspect to stay competitive in the current scenario. Further, MLOps can improve your time to market. However, you need to prefer the right approach and mindset to leverage the best of MLOps.

If you are looking for expert support that can optimise your MLOps strategy and implementation, Mindpath can be your perfect pick. This company can help your machine learning management turn into production-rich success.

FAQs

1. What is MLOps in simple terms?

MLOps is the convenient practice of applying software engineering principles to your machine learning workflows.

2. How can an MLOps pipeline uplift my model deployment?

An MLOps pipeline can streamline the steps of data validation, testing and deployment. This approach reduces manual errors while speeding up your delivery.

3. What are some common MLOps tools in the recent scenario?

Some of the efficient MLOps tools include Qdrant, LangChain and MLFlow. These tools are capable of making your models work precisely.

4. Why you should monitor consistently in MLOps?

Monitoring ensures your model is continuously performing as per the real-world data. It detects issues like degraded accuracy and data quality problems.

5. Is MLOps only reliable for large-scale organisations?

While large businesses benefit effectively, small and medium businesses can also adopt MLOps practices to upgrade their needs.

Learn how automating model deployment with MLOps strategies improves efficiency, security and performance for machine learning models.

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