Learning Spiral Private Limited is a leading provider of data annotation and data labeling services in India.
For more details visit: https://learningspiral.ai/
More About Data Annotation Company in INDIAdigitalmlspl
Learning Spiral Private Limited is a leading provider of data annotation and data labeling services in India.
For more details visit: https://learningspiral.ai/
The document describes a Driverless ML API that was created to automate machine learning workflows including feature engineering, model validation, tuning, selection, and deployment. The API uses machine learning interpretability techniques to provide visualizations and explanations of models. It aims to help scale data science efforts and enable both expert and junior data scientists to more quickly develop accurate, production-ready models. Key capabilities of the API include automated exploratory data analysis, feature selection and engineering, model selection and hyperparameter tuning using GPUs for faster training, and model interpretability visualizations.
1. Artificial intelligence can be used to automate and enhance complex analytical tasks for optimizing business processes. The document discusses a general application schema that uses various AI methods like neural networks and optimization tools to optimize business processes.
2. The schema includes intelligent predictive models to forecast processes, intelligent optimization tools to find optimal process decisions, and intelligent analysis tools to detect unexpected process behaviors.
3. An example of applying the schema is a cash management system for banks that uses AI techniques like neural networks and genetic algorithms to optimize cash logistics and reduce costs.
The document provides 8 guidelines for choosing the right data science platform for business analytics needs. It discusses factors such as whether the platform can handle all aspects of business analytics, large volumes of data, both structured and unstructured data, and real-time scoring problems. It also addresses whether the platform supports easy-to-use workflows, optimization functions, model management, and communicating insights. The document uses Angoss as an example and describes how its platform meets the guidelines.
Machine learning projects may seem similar to any software engineering endeavor, the reality is machine learning projects are onerous, demand high quality work from every person involved, and are sensitive to any tiny mistake.
It seems that we cannot go five years without having some massive technology shift that becomes an essential part of our day-to-day lives. So, we will start with a proper definition of machine learning and how it is changing the way businesses analyze information. We will then continue by discussing proper ways to begin machine learning projects, including weighing the feasibility of a project, planning timelines, and the stages of the machine learning workflow once you start your project.
After exploring the stages of the machine learning workflow, we will end the webinar with an example of a completed machine learning project. We will demonstrate how to create a similar project and give you the tools to create your own.
What you'll learn:
A deeper understanding of the end-to-end machine learning workflow.
The tools needed to effectively create, design, and manage machine learning projects.
The skills to define your goal, foresee issues, release models, and measure outcomes during the ML project lifecycle.
Demo: Skyl Platform for End-End machine learning workflow.
This is the slide deck for this webinar:
https://skyl.ai/webinars/guide-end-to-end-machine-learning-projects
Afternoons with Azure - Azure Machine Learning CCG
Journey through programming languages such as R, and Python that can be used for Machine Learning. Next, explore Azure Machine Learning Studio see the interconnectivity.
For more information about Microsoft Azure, call (813) 265-3239 or visit www.ccganalytics.com/solutions
Choosing The Right Data Annotation Option: Pros And ConsArnav Malhotra
The process of attributing, tagging, or labeling data to advance contextual understanding is known as data annotation. These processes are put in place to create relevant metadata for machines so that they can perform various tasks, such as classification and regression.
How to Build an AI/ML Product and Sell it by SalesChoice CPOProduct School
Main takeaways:
- How to identify the use cases to build an AI/ML product?
- What are the challenges that you would face and how to over come them?
- How to establish stake holder buy-in and design the go-to market strategy?
More About Data Annotation Company in INDIAdigitalmlspl
Learning Spiral Private Limited is a leading provider of data annotation and data labeling services in India.
For more details visit: https://learningspiral.ai/
The document describes a Driverless ML API that was created to automate machine learning workflows including feature engineering, model validation, tuning, selection, and deployment. The API uses machine learning interpretability techniques to provide visualizations and explanations of models. It aims to help scale data science efforts and enable both expert and junior data scientists to more quickly develop accurate, production-ready models. Key capabilities of the API include automated exploratory data analysis, feature selection and engineering, model selection and hyperparameter tuning using GPUs for faster training, and model interpretability visualizations.
1. Artificial intelligence can be used to automate and enhance complex analytical tasks for optimizing business processes. The document discusses a general application schema that uses various AI methods like neural networks and optimization tools to optimize business processes.
2. The schema includes intelligent predictive models to forecast processes, intelligent optimization tools to find optimal process decisions, and intelligent analysis tools to detect unexpected process behaviors.
3. An example of applying the schema is a cash management system for banks that uses AI techniques like neural networks and genetic algorithms to optimize cash logistics and reduce costs.
The document provides 8 guidelines for choosing the right data science platform for business analytics needs. It discusses factors such as whether the platform can handle all aspects of business analytics, large volumes of data, both structured and unstructured data, and real-time scoring problems. It also addresses whether the platform supports easy-to-use workflows, optimization functions, model management, and communicating insights. The document uses Angoss as an example and describes how its platform meets the guidelines.
Machine learning projects may seem similar to any software engineering endeavor, the reality is machine learning projects are onerous, demand high quality work from every person involved, and are sensitive to any tiny mistake.
It seems that we cannot go five years without having some massive technology shift that becomes an essential part of our day-to-day lives. So, we will start with a proper definition of machine learning and how it is changing the way businesses analyze information. We will then continue by discussing proper ways to begin machine learning projects, including weighing the feasibility of a project, planning timelines, and the stages of the machine learning workflow once you start your project.
After exploring the stages of the machine learning workflow, we will end the webinar with an example of a completed machine learning project. We will demonstrate how to create a similar project and give you the tools to create your own.
What you'll learn:
A deeper understanding of the end-to-end machine learning workflow.
The tools needed to effectively create, design, and manage machine learning projects.
The skills to define your goal, foresee issues, release models, and measure outcomes during the ML project lifecycle.
Demo: Skyl Platform for End-End machine learning workflow.
This is the slide deck for this webinar:
https://skyl.ai/webinars/guide-end-to-end-machine-learning-projects
Afternoons with Azure - Azure Machine Learning CCG
Journey through programming languages such as R, and Python that can be used for Machine Learning. Next, explore Azure Machine Learning Studio see the interconnectivity.
For more information about Microsoft Azure, call (813) 265-3239 or visit www.ccganalytics.com/solutions
Choosing The Right Data Annotation Option: Pros And ConsArnav Malhotra
The process of attributing, tagging, or labeling data to advance contextual understanding is known as data annotation. These processes are put in place to create relevant metadata for machines so that they can perform various tasks, such as classification and regression.
How to Build an AI/ML Product and Sell it by SalesChoice CPOProduct School
Main takeaways:
- How to identify the use cases to build an AI/ML product?
- What are the challenges that you would face and how to over come them?
- How to establish stake holder buy-in and design the go-to market strategy?
The document provides an overview of machine learning and artificial intelligence concepts. It discusses:
1. The machine learning pipeline, including data collection, preprocessing, model training and validation, and deployment. Common machine learning algorithms like decision trees, neural networks, and clustering are also introduced.
2. How artificial intelligence has been adopted across different business domains to automate tasks, gain insights from data, and improve customer experiences. Some challenges to AI adoption are also outlined.
3. The impact of AI on society and the workplace. While AI is predicted to help humans solve problems, some people remain wary of technologies like home health diagnostics or AI-powered education. Responsible development of explainable AI is important.
A journey of ai driven analytics insights engineSomya Anand
The document discusses building an insights engine to make data from a sales enablement platform more accessible and actionable. It describes tackling different types of data using modelling methods like Gaussian mixture models and hypothesis testing. The goal is to generate personalized and contextual insights as well as simplify analytics reporting. Natural language generation techniques are used to convert data into intuitive text summaries and notifications. The system aims to be an auto-governed platform that provides a 360-degree view of activities and reduces the time for administrators to run sales enablement initiatives.
Machine learning platforms powered by Intel technology can help organizations transform data into business insights. These platforms provide scalability, efficiency and lower costs while reducing time to market for intelligent solutions. Intel's high-performance computing reference architectures are optimized for machine learning and include scalable hardware and software for predictive analytics. Using an Intel-based machine learning platform allows organizations to gain a competitive edge through accelerated model training and deployment.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data
How can AI optimize production processes to improve.pptxAkanjLove
Artificial intelligence can optimize manufacturing processes to improve efficiency and reduce costs. It can enable production lines to minimize downtime, optimize asset utilization, and predict failures by allowing systems to govern themselves. AI is applied across manufacturing in various ways such as quality control using computer vision, generative design, and assembly line integration and optimization by pulling data from IoT devices. Machine learning and natural language processing are important techniques enabling many AI applications in industries like manufacturing.
Machine Learning course in Chandigarh Joinasmeerana605
The machine learning process is iterative. Data collection and preparation are crucial. Feature engineering transforms raw data into meaningful representations. Model selection involves trying different algorithms. Training exposes the model to data and allows it to learn. We evaluate how well it performs on new data before finally deploying it for predictions.Join Machine Learning course in Chandigarh.
Using Data Science to Build an End-to-End Recommendation SystemVMware Tanzu
This document summarizes the key steps and outcomes of a project to build an end-to-end recommendation system for a power utility company. The system was designed to integrate machine learning models with mobile and call center systems to recommend ancillary products to customers. The project involved exploring customer data, developing machine learning models through an iterative process, and operationalizing the models by building APIs and automated workflows. The new system provided recommendations via microservices and represented an improvement over the utility's previous manual, less rigorous approach to data science and modeling.
CYBERBULLYING DETECTION USING MACHINE LEARNING-1 (1).pdfKumbidiGaming
The document discusses a project that aims to detect cyberbullying using machine learning. It presents a system that trains a naive bayes classifier on a dataset of bullying and non-bullying comments to classify new comments. The system is implemented using Python with Django and SQL Server. It extracts features from comments using TF-IDF before training the naive bayes model. The trained model can then detect abusive comments on social media platforms.
Anuj Vaghani presented on his internship experience working with data analytics and machine learning teams. He discussed key concepts like data analytics, machine learning, and the methodology he used. Anuj completed two projects - one analyzing hotel booking data to understand cancellation factors, and another predicting bike demand using regression models. He found factors like booking lead time and deposit type influenced cancellations. For bike demand, random forest and gradient boosting models achieved high accuracy. Anuj concluded by discussing future areas like deep learning and new opportunities in the field.
This document discusses using machine learning to predict laptop prices based on laptop specifications. It proposes using a random forest algorithm on a dataset containing variables like laptop model, RAM, storage, GPU, CPU, display, and touchscreen to predict laptop price. Explanatory data analysis and preprocessing are performed before implementing the random forest model. The model achieves 89% prediction accuracy. A streamlit web app is created to demonstrate the model's laptop price predictions based on user-selected configurations. The conclusion is that the model can help students select appropriately priced laptops that meet their needs.
Janapati Sai Subrahmanyam is an Associate Software Engineer with experience in data science, machine learning, and IoT projects. He has a B.Tech in ECE from Karunya University and certifications in machine learning, AI, and data science. His skills include Python, R, SQL, Java, and data science tools like Keras, SkLearn, and Tableau. He has worked on various data science projects including predicting heart strokes, bankruptcy prediction using XGBoost, and sentiment analysis using LSTM. He is interested in machine learning, deep learning, and IoT.
Maximising likelihood of success: Applying Product Management to AI/ML/DS pr...Kevin Wong
According to stats, 85% of Artificial Intelligence (AI) / Machine Learning (ML) / data science (DS) projects fail, which hinders companies' appetite in investing in AI/ML/DS, and holds back data scientists from getting the recognition they deserve. In this talk dated 15 June 2019, Kevin Wong presented a gentle introduction on how he applied a re-invented Product Management approach to AI projects, in order to maximise their likelihood of success.
The document outlines the data science life cycle which includes business understanding, data acquisition and understanding, modeling, deployment, customer acceptance, and monitoring & maintenance. It discusses collecting data from various sources, analyzing and modeling the data to gain insights, deploying models, getting customer feedback, and maintaining models over time. The key aspects of each step are described, from defining business goals to regularly updating models post-deployment. Overall, the data science life cycle aims to help organizations make better data-driven decisions.
This session will overview how a data scientist performs in an organization. Its roles and responsibility and how it helps the organization achieve organizational goals. We will look into the complete life cycle of data scientists, starting from problem identification to finding the solution.
This document contains contact information, skills, courses taken, certifications, work history, education, projects, and accomplishments for Sahitya Panchapakesan. It summarizes their background in computer science and data analysis fields and their goal to work in an organization that provides challenging work exploring new areas to further their knowledge and make significant contributions while building an autonomous medical system.
The 4 Machine Learning Models Imperative for Business TransformationRocketSource
Machine learning is hot right now and for good reason. We're going to break down what you need to know about what goes into a model and give you four machine learning models your business should have in production right now.
In today's data-driven world, understanding business analytics is essential for success. upGrad Campus offers top-notch online education in business analytics. Our expert-led courses provide hands-on experience and practical knowledge, ensuring you're equipped with the skills needed to excel in the field. Ready to unlock new opportunities with business analytics? Join upGrad Campus and take the first step towards a brighter future!
Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.
Building machine learning muscle in your team & transitioning to make them do machine learning at scale. We also discuss about Spark & other relevant technologies.
06-18-2024-Princeton Meetup-Introduction to MilvusTimothy Spann
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.
More Related Content
Similar to Know About Data Annotation Company India
The document provides an overview of machine learning and artificial intelligence concepts. It discusses:
1. The machine learning pipeline, including data collection, preprocessing, model training and validation, and deployment. Common machine learning algorithms like decision trees, neural networks, and clustering are also introduced.
2. How artificial intelligence has been adopted across different business domains to automate tasks, gain insights from data, and improve customer experiences. Some challenges to AI adoption are also outlined.
3. The impact of AI on society and the workplace. While AI is predicted to help humans solve problems, some people remain wary of technologies like home health diagnostics or AI-powered education. Responsible development of explainable AI is important.
A journey of ai driven analytics insights engineSomya Anand
The document discusses building an insights engine to make data from a sales enablement platform more accessible and actionable. It describes tackling different types of data using modelling methods like Gaussian mixture models and hypothesis testing. The goal is to generate personalized and contextual insights as well as simplify analytics reporting. Natural language generation techniques are used to convert data into intuitive text summaries and notifications. The system aims to be an auto-governed platform that provides a 360-degree view of activities and reduces the time for administrators to run sales enablement initiatives.
Machine learning platforms powered by Intel technology can help organizations transform data into business insights. These platforms provide scalability, efficiency and lower costs while reducing time to market for intelligent solutions. Intel's high-performance computing reference architectures are optimized for machine learning and include scalable hardware and software for predictive analytics. Using an Intel-based machine learning platform allows organizations to gain a competitive edge through accelerated model training and deployment.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data
How can AI optimize production processes to improve.pptxAkanjLove
Artificial intelligence can optimize manufacturing processes to improve efficiency and reduce costs. It can enable production lines to minimize downtime, optimize asset utilization, and predict failures by allowing systems to govern themselves. AI is applied across manufacturing in various ways such as quality control using computer vision, generative design, and assembly line integration and optimization by pulling data from IoT devices. Machine learning and natural language processing are important techniques enabling many AI applications in industries like manufacturing.
Machine Learning course in Chandigarh Joinasmeerana605
The machine learning process is iterative. Data collection and preparation are crucial. Feature engineering transforms raw data into meaningful representations. Model selection involves trying different algorithms. Training exposes the model to data and allows it to learn. We evaluate how well it performs on new data before finally deploying it for predictions.Join Machine Learning course in Chandigarh.
Using Data Science to Build an End-to-End Recommendation SystemVMware Tanzu
This document summarizes the key steps and outcomes of a project to build an end-to-end recommendation system for a power utility company. The system was designed to integrate machine learning models with mobile and call center systems to recommend ancillary products to customers. The project involved exploring customer data, developing machine learning models through an iterative process, and operationalizing the models by building APIs and automated workflows. The new system provided recommendations via microservices and represented an improvement over the utility's previous manual, less rigorous approach to data science and modeling.
CYBERBULLYING DETECTION USING MACHINE LEARNING-1 (1).pdfKumbidiGaming
The document discusses a project that aims to detect cyberbullying using machine learning. It presents a system that trains a naive bayes classifier on a dataset of bullying and non-bullying comments to classify new comments. The system is implemented using Python with Django and SQL Server. It extracts features from comments using TF-IDF before training the naive bayes model. The trained model can then detect abusive comments on social media platforms.
Anuj Vaghani presented on his internship experience working with data analytics and machine learning teams. He discussed key concepts like data analytics, machine learning, and the methodology he used. Anuj completed two projects - one analyzing hotel booking data to understand cancellation factors, and another predicting bike demand using regression models. He found factors like booking lead time and deposit type influenced cancellations. For bike demand, random forest and gradient boosting models achieved high accuracy. Anuj concluded by discussing future areas like deep learning and new opportunities in the field.
This document discusses using machine learning to predict laptop prices based on laptop specifications. It proposes using a random forest algorithm on a dataset containing variables like laptop model, RAM, storage, GPU, CPU, display, and touchscreen to predict laptop price. Explanatory data analysis and preprocessing are performed before implementing the random forest model. The model achieves 89% prediction accuracy. A streamlit web app is created to demonstrate the model's laptop price predictions based on user-selected configurations. The conclusion is that the model can help students select appropriately priced laptops that meet their needs.
Janapati Sai Subrahmanyam is an Associate Software Engineer with experience in data science, machine learning, and IoT projects. He has a B.Tech in ECE from Karunya University and certifications in machine learning, AI, and data science. His skills include Python, R, SQL, Java, and data science tools like Keras, SkLearn, and Tableau. He has worked on various data science projects including predicting heart strokes, bankruptcy prediction using XGBoost, and sentiment analysis using LSTM. He is interested in machine learning, deep learning, and IoT.
Maximising likelihood of success: Applying Product Management to AI/ML/DS pr...Kevin Wong
According to stats, 85% of Artificial Intelligence (AI) / Machine Learning (ML) / data science (DS) projects fail, which hinders companies' appetite in investing in AI/ML/DS, and holds back data scientists from getting the recognition they deserve. In this talk dated 15 June 2019, Kevin Wong presented a gentle introduction on how he applied a re-invented Product Management approach to AI projects, in order to maximise their likelihood of success.
The document outlines the data science life cycle which includes business understanding, data acquisition and understanding, modeling, deployment, customer acceptance, and monitoring & maintenance. It discusses collecting data from various sources, analyzing and modeling the data to gain insights, deploying models, getting customer feedback, and maintaining models over time. The key aspects of each step are described, from defining business goals to regularly updating models post-deployment. Overall, the data science life cycle aims to help organizations make better data-driven decisions.
This session will overview how a data scientist performs in an organization. Its roles and responsibility and how it helps the organization achieve organizational goals. We will look into the complete life cycle of data scientists, starting from problem identification to finding the solution.
This document contains contact information, skills, courses taken, certifications, work history, education, projects, and accomplishments for Sahitya Panchapakesan. It summarizes their background in computer science and data analysis fields and their goal to work in an organization that provides challenging work exploring new areas to further their knowledge and make significant contributions while building an autonomous medical system.
The 4 Machine Learning Models Imperative for Business TransformationRocketSource
Machine learning is hot right now and for good reason. We're going to break down what you need to know about what goes into a model and give you four machine learning models your business should have in production right now.
In today's data-driven world, understanding business analytics is essential for success. upGrad Campus offers top-notch online education in business analytics. Our expert-led courses provide hands-on experience and practical knowledge, ensuring you're equipped with the skills needed to excel in the field. Ready to unlock new opportunities with business analytics? Join upGrad Campus and take the first step towards a brighter future!
Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.
Building machine learning muscle in your team & transitioning to make them do machine learning at scale. We also discuss about Spark & other relevant technologies.
Similar to Know About Data Annotation Company India (20)
06-18-2024-Princeton Meetup-Introduction to MilvusTimothy Spann
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.
2. 1. Active Learning:
● Imagine an AI that picks the most informative data points for you to label,
reducing your workload and maximizing the value of your annotations.
● By analyzing the model’s uncertainty, they prioritize data points that will
have the most significant impact on its learning, leading to faster and
more efficient annotation.
4. 2. Semi-Supervised
Learning:
● Semi-supervised learning leverages both labeled and unlabeled
data to train AI models.
● This technique is a great combination of the EQ and IQ that comes
by adjoining a human and a machine.
5.
6. 3. Transfer Learning:
● Transfer learning takes pre-trained models on related tasks and
adapts them to your specific domain.
● This can significantly reduce the amount of data you need to
annotate from scratch, especially for tasks with common
underlying structures.
7. 4. Collaborative Annotation:
● Crowdsourcing the annotation process can be a powerful tool.
Several platforms allow you to tap into a global pool of
annotators, breaking down large tasks into smaller, more
manageable chunks.
8. 5. Gamification:
● Turn data annotation into a game! Gamification techniques like points,
badges,and leaderboards can inject fun and competition into the
process, motivating annotators and improving accuracy and
engagement.
9. 6. AI-Assisted Annotation:
● AI-assisted annotation tools can automate repetitive tasks like
bounding boxes or image segmentation, freeing up your human
annotators to focus on complex, nuanced cases that require their
judgment and expertise.
● This hybrid approach leverages the strengths of both humans and
machines for optimal efficiency.
10.
11. 7. Continuous Feedback and
Improvement
● Data annotation is not a one-time process. Continuously
monitoring model performance and feeding back insights into
the annotation process is crucial for ensuring accuracy and
adaptability.
● Active learning algorithms can be particularly beneficial here,
as they can refine their data selection based on the model’s
evolving needs.
12. For more details visit:
https://learningspiral.ai/
Call Us : +91 7224061676