In the last 12 months, there have been myriad developments in machine learning (ML) tools and applications, yet many companies are still struggling to see tangible business results from AI models in production. Deploying AI/ML solutions isn’t easy, but Algorithmia and Appen make it simple to tackle any bumps in your project roadmap.  

Machine learning operationalization (having a deployed ML lifecycle, which could consist of tens or hundreds of models) is being attempted across all industries with software and IT firms leading the charge. Machine learning has the capacity to easily transform every business at a rate that far exceeds manual data analytics. But despite the opportunities ML can provide, there are still many obstacles stymying the goal of ML value extraction. Some of the challenge areas that hinder ML projects include:

  • Time to deploy a model into production. Algorithmia’s 2020 State of Enterprise Machine Learning report found that 55% of companies surveyed in late 2019 had not deployed a machine learning model.
  • Scaling an ML portfolio across an entire organization.
  • Version control to maintain uninterrupted codebase even as models are containerized and edited.
  • Training data availability and quality to correctly train your machine learning model.
  • Security and scalability to ensure your project success today and in the future.

The machine learning lifecycle

Accelerate the time to value

Once you’ve successfully solved for these issues and your ML team is working toward producing models that actually provide business value, you’ll want to act as quickly as possible to accelerate the time to value to reap the real business benefits for your enterprise. How? With Appen and Algorithmia.

Appen and Algorithmia make data science teams successful with their ML projects

Appen provides high quality annotated training data for your models.  By using Appen, companies can ensure that they are not only getting the volume of high quality training data they need, but also that their training data is constantly refreshed to ensure models do not become obsolete, solving both quality and scalability issues that threaten model viability. 

Algorithmia makes it easy to deploy models as scalable services, regardless of framework, language, or data source.  Our customers are empowered to manage their ML lifecycles on any infrastructure, with tools to catalog, evaluate, serve, and govern.  The AI Layer is an abstraction layer that connects your models, hardware, and applications.

Appen and Algorithmia ensure ML teams are successful building and moving models to production and delivering real business value.

Connect your models, hardware, and applications

  • Deploy faster and more frequently: Shorten deployment times from weeks to minutes with a fully automated platform.
  • Deliver more models to market: Spend less time managing operations and more time on data science. Focus on build value instead of infrastructure.
  • Collaborate and share resources: Share models, code snippets, and documentation in a secure, versions model repository. Reuse code by building model pipelines.
  • Reduce deployment friction: Deploy programmatically with a fraction of the DevOps and ML engineering resources of manual publishing.
  • Decrease serving costs: Squeeze maximum performance from CPUs and GPUs in the cloud or on-prem, with the first serverless system created specifically for ML.

To learn more about reliable training data at scale, visit To deploy models and kickstart your MLOps, visit