GitOps is a way of implementing continuous delivery for cloud native applications. It is based on the idea of using Git as a single source of truth for declarative infrastructure and applications. In GitOps, the desired state of the infrastructure and applications is stored in version control, and an automated process is used to ensure […]
What Is a Feature Store in Machine Learning?
A feature store is a centralized platform for managing and serving the features used in machine learning (ML) models. A feature is an individual measurable property or characteristic of data that is used as input to an ML model. In order to build effective ML models, it is critical to have high-quality, well-engineered features that […]
7 Essential Machine Learning Engineering Skills
Machine learning engineering is a specialized field that combines the principles of computer science, data science, and software engineering with the techniques and methodologies of machine learning. Machine learning engineers are responsible for designing, developing, and implementing machine learning models and systems to solve complex problems or make data-driven predictions and decisions. Machine learning engineering is crucial in various […]
Operations Managers: The Unsung Heroes of MLOps
Operations managers play a critical role in the MLOps life cycle. Without the proper infrastructure, the number of CPU nodes, or security checks, models, and applications built by data science and development teams are more likely to fail – if they even reach the deployment phase. That’s why operations managers should be brought into the […]
MLOps: Why Now for Open Source
According to Gartner, a whopping 47% of machine learning experiments fail to reach experimentation. This number is stunning on the surface, but it is even more troubling when you consider the deluge of demands the MLOps workforce is facing in the wake of the COVID-19 pandemic. With the MLOps market size set to balloon to over $6 […]
3 Strategies for Creating a Successful MLOps Environment
Disconnects between development, operations, data engineers, and data science teams might be holding your organization back from extracting value from its artificial intelligence (AI) and machine learning (ML) processes. In short, you may be missing the most essential ingredient of a successful MLOps environment: collaboration. For instance, your data scientists might be using tools like JupyterHub or […]
2021 Crystal Ball: What’s in Store for AI, Machine Learning, and Data
Click to learn more about author Rachel Roumeliotis. Artificial intelligence (AI) is no longer a “nice-to-have.” From business processes and smart home technology to healthcare and life sciences, AI continues to evolve and grow as it plays an increasing role in many aspects of our work, home lives, and beyond. As we bid 2020 a […]
What to Look for in a Model Server to Build Machine Learning-Powered Services
Click to learn more about co- author Ion Stoica. Click to learn more about co- author Ben Lorica. Machine learning is being embedded in applications that involve many data types and data sources. This means that software developers from different backgrounds need to work on projects that involve ML. In our previous post, we listed key […]
Five Key Features for a Machine Learning Platform
Click to learn more about co- author Ion Stoica. Click to learn more about co- author Ben Lorica. Machine learning platform designers need to meet current challenges and plan for future workloads. As machine learning gains a foothold in more and more companies, teams are struggling with the intricacies of managing the machine learning lifecycle. […]
Cloudera Delivers Open Standards Based MLOps
A new press release states, “Cloudera, the enterprise data cloud company, today announced an expanded set of production machine learning capabilities for MLOps is now available in Cloudera Machine Learning (CML). Organizations can manage and secure the ML lifecycle for production machine learning with CML’s new MLOps features and Cloudera SDX for models. Data scientists, […]