From search to social media firms, technology companies are continuously looking to deliver the best customer experience. Machine learning and artificial intelligence are powerful tools used by technology companies to provide their customers with leading edge products.
Machine learning has applicability across a wide variety of technologies and products. For it to be most effective, it needs large volumes of high-quality training data.
Machine learning is currently being used to enhance a variety of use cases including:
Search relevance
Search engine algorithms use machine learning to drive stronger user engagement. By interpreting queries and assessing user intent, search results become more relevant, which creates higher user satisfaction.
Personalization
Analyzing data activity and preferences can help search engine and social media providers personalize content feeds and recommendations, enhancing online customer experiences.
Natural language processing (NLP)
NLP can analyze language patterns to understand text, for example on social media. This technology can be used to track customer sentiment and develop engagement strategies.
For machine learning models to be the most effective, they need large volumes of high-quality training data
How we help
Your machine learning models need training data to make sure that your algorithms are continually optimized. But not just any data – they need large, high-quality, human-annotated datasets. Humans are simply better than computers at managing subjectivity, understanding intent, and coping with ambiguity.
Appen’s global, curated crowd allows you to collect the amount of quality data samples needed to optimize your algorithm, in your desired timeframe. Leverage the Appen Global platform to gain efficiencies in your data collection efforts and gather large volumes of high-quality datasets to quickly train your machine learning model.
We help you enhance the following solutions
- Applications
Use our global curated crowd to ensure your applications work effectively for customers around the world.
- Automatic Speech Recognition
Improve customer interactions with Automatic Speech Recognition systems by training them to better understand human language.
- CEM/CRM
Build CEM and CRM solutions with stronger data analytics capabilities.
- Computer Vision
Develop computer vision solutions that recognize images and video as well as humans do with high quality, human annotated data.
- Consultative Services
We work closely with your team to develop a customized program that addresses your unique business challenges.
- Crowdsourcing
With Appen's skilled global crowd, you can quickly and cost-effectively scale your program to better meet customer needs.
- Data Analytics
Use Appen's services to build robust data analytics systems more quickly.
- Machine Translation
Drive higher customer satisfaction with automatic translation capabilities that are highly accurate.
- Proofing Tools
Develop more accurate proofing tools with high quality linguistic services.
- Search Relevance
Return more relevant search results be regularly training your machine learning model with high-quality data sets.
- Semantic Search
Improve your semantic search capabilities with training data that provides user intent and context.
- Social Media
Meet your users' demand for more relevant, personalized content. Train your algorithm with high quality data to increase user satisfaction.
- Social Media Analytics
Build social media analytics solutions that deliver more meaningful patterns and deeper insights.
- Text-to-Speech
Improve customer interactions with TTS systems that are fluent in every language.
- Virtual Assistants and Chatbots
Train your virtual assistant or chatbot to better understand and respond to human interaction, driving higher levels of customer satisfaction.
Additional resources
Common Sense AI: Making Deep Learning Technologies More Human
Whether through devices we use to enable convenience at home or in the way products we use all the time are manufactured, AI’s impact is everywhere, driving innovation in just about every aspect of our lives. But there are missing pieces to this puzzle that still cause frustration for end-users and present significant challenges for researchers trying to improve how AI technology performs.
How to Remove Bias in Training Data
Machine learning (ML) algorithms are generally only as good as the data they are trained on. Bias in ML training data can take many forms, but the end result is that it can cause an algorithm to miss the relevant relations between features and target outputs. Whether your organization is a small business, global enterprise, or governmental agency, it’s essential …
Machine Learning Adoption by Industry: A Q&A with Stephen Woodard
As artificial intelligence and machine learning become a greater part of the business landscape, it’s interesting to see how different vertical industries are putting it to use. How does healthcare compare to the technology sector? Do geography and location play a role? To answer some of these questions, I caught up with our own Stephen Woodard from our business development …