DEVOPS FOR MACHINE LEARNING
Accelerate speed to insights
Agile and DevOps have drastically improved the way companies deliver software. And we can use many of the same ideas to great effect in data science and machine learning models. By breaking down silos and adopting MLOps with end-to-end continuous integration, delivery, deployment, and training workflows, you can deploy working machine learning insights to production more frequently and increase your speed to market by 10x.
HIGH QUALITY DATA
Empower data scientists
Machine learning models are only as good as the data that was used to train them. So MLOps always starts with the data. Data scientists and machine learning engineers spend the majority of their time trying to source and wrangle the right data as well as select the right features for model training. But investing in data accessibility and quality capabilities, as well as providing data scientists with a convenient way to work with big data can make a 10x difference in productivity and the accuracy of decisions.
END-TO-END MODEL LIFECYCLE
Consistently deliver actionable insights
Data driven companies can no longer afford to use disjointed manual data science processes. MLOps offers a blueprint for streamlining and automating all stages of machine learning: from data preparation to deep learning model development, training, validation, versioning, deployment, and monitoring. Automated machine learning lifecycle management with a powerful ML platform not only increases the productivity of data scientists, but helps companies scale machine learning efforts without a loss of efficiency or quality.
LAST MILE DELIVERY OF INSIGHTS
Design AI-powered applications
With only 22 percent of companies successfully deploying models to production, the last mile of MLOps remains a difficult problem. This is when companies need to acquire ML engineering and software development skills to tune production models, develop microservices, deploy Model-as-a-Service in the cloud, embed models directly into the consuming applications, or deploy them at the edge. With the help of traditional continuous integration and continuous delivery approaches and a powerful ML platform, the last mile challenge can be easily solved.
CONTINUOUS TRAINING AND MONITORING
Keep models relevant and impactful
While DevOps deals only with code, MLOps has to deal with data. With changing environments, model performance can deteriorate. Two of the most critical MLOps capabilities include the ability to monitor deep learning model performance and automatically retrain the models. Automating the workflows and doing it regularly increases the productivity of machine learning engineers, improves the quality of decisions, and enables effective autonomous model operations.
FINANCE & INSURANCE
Machine Learning Platform Starter Kit for GCP
Build a production-ready, cloud-native machine learning platform within weeks on Google Cloud. Improve data accessibility and quality, increase speed to insights, and achieve significant ROI with our starter kit.
Machine Learning Platform Starter Kit for AWS
Build a production-ready, cloud-native machine learning platform within weeks on AWS cloud. Improve data accessibility and quality, increase speed to insights, and achieve significant ROI with our starter kit.
How do we achieve efficient machine learning operations?
Machine learning operations industries
We develop advanced artificial intelligence use cases and implement automated machine learning operations processes for Fortune-1000 enterprises in various industries including telecom, retail, media, gaming, and financial services.
Technology and media
Technology and media companies recognized the value of data years ago and have accumulated significant amounts of structured and unstructured data. They often experiment with advanced machine learning algorithms including deep learning and reinforcement learning. We have helped the best of them stay ahead of the competition with a robust MLOps process and platform that allows scaling of machine learning efforts quickly, faster deployment of models, model retraining in real time, and production of high quality insights.
Retail and brands
Retailers and brands have to move quickly to optimize the customer experience and back-office operations, including inventory and supply chain. They are quickly growing their machine learning teams but without the right culture, processes, and tools, models rarely get deployed to production. We have helped Fortune-1000 retailers increase speed to insights by implementing an MLOps process, deploying experimentation and ML platforms, and making high quality data available to their data science teams.
Most banks and insurance companies are not new to advanced analytics and machine learning. However, they often started machine learning programs a long time ago, with most tools and processes they use now outdated. Since security and compliance remain critical concerns, they need an MLOps process that can support secure access to data with tokenization and masking. It must also enable robust model testing and monitoring of model performance and provide a variety of deployment options.
Read more about machine learning operations
5 technology enablers for DataOps
DataOps and MLOps are closely related concepts and every company needs both to increase the business impact of data analytics. Read this article to learn about how to improve data engineering and make data collection easier. Adopting DataOps in the company should also improve the culture in the data engineering team and make MLOps adoption easier.
How to use GCP and AWS big data and AI cloud services from Jupyter Notebook
Jupyter Notebooks are integral parts of every ML Platform and any MLOps process. Unfortunately, they cover only a limited part of the MLOps process. When working with cloud-based platforms, data preparation and model deployment is done outside of the notebook with console or cloud API. We extended Jupyter Notebooks to allow machine learning engineers to work with big data and deploy models by accessible cloud APIs with a convenient DSL.
Accelerate your journey to artificial intelligence
We use MLOps in all our AI projects. We can help you implement machine learning operations in your organization and power it with the modern ML platform. To get started, choose from the following engagement options and contact us to discuss the first steps.
We offer free half-day workshops with our top experts in machine learning operations to demonstrate the benefits of MLOps, discuss your machine learning strategy, challenges, optimization opportunities, and industry best practices.
Proof of concept
If you have already decided to improve your machine learning process, but can’t commit to a large investment, we will help you identify an AI use case, onboard MLOps, and deliver tangible results together with your team in 4-8 weeks.
If you are committed to transforming your organization so you can start consistently delivering insights with AI and ML, we can start with a 2–3 week discovery phase to identify the top challenges, design the ML platform solution, draft the MLOps process, and create an implementation roadmap.
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