Preparing for Machine Learning Readiness to Unlock its Full Potential
With the rapid advancement of artificial intelligence (AI) and machine learning (ML), companies need to understand and evaluate their readiness to adopt these technologies and drive material business outcomes. To assess a company’s machine learning readiness, we must consider several key factors, including: ● Vision/strategy ● Data availability ● Expertise, infrastructure and resources ● MLOps […]
Enfabrica Makes Cloud Networking Performance Advances
Enfabrica Corp. today emerged from stealth at the MemCon conference to launch a class of processors for networking in cloud computing environments that are optimized for moving data between CPUs, graphical processor units (GPUs), accelerators and memory. Enfabrica CEO Rochan Sankar said the Accelerated Compute Fabric (ACF) enables scalable, streaming, multi-terabit-per-second movement of data in […]
Graduating From DevOps to MLOps? 5 Tools to Help
DevOps is a set of practices that aims to bridge the gap between software development and operations. It aims to improve collaboration and communication between these two teams and to automate the process of software delivery so that changes can be made and deployed more quickly and easily. This can include continuous integration, continuous delivery […]
ClearML Releases New Reports Feature to Share Real-Time Results of Machine Learning Projects and Ignite ML Collaboration Across the Enterprise
Tel Aviv, IL, January 19, 2023 – ClearML, the leading open source, end-to-end MLOps platform, today announced it has released a new Reports feature that is now generally available. This new feature makes it easy to create and share real-time reports within ClearML as well as connect to third-party editors such as Confluence, Monday, Notion, […]
Why MLOps Hurts: 3 Pain Points for DevOps Teams
Machine learning operations (MLOps) is a practice that aims to improve the collaboration and communication between data scientists and IT professionals in the development, deployment and maintenance of machine learning models. MLOps aims to streamline the process of building, testing and deploying machine learning models by automating and standardizing the various tasks involved. This includes […]
Navigating the MLOps Revolution: Three Missteps to Avoid
With nearly 60% of businesses set to adopt MLOps by 2024, this function has quickly established itself as an indispensable part of the enterprise technology world. And with computing needs rapidly expanding as digital transformation efforts continue, the growth of the MLOps market is unlikely to stop anytime soon. That said, as with any emerging […]
Zilliz Unfurls Managed Vector Database Service
Zilliz this week made generally available a managed cloud service that can now be spun up in less than five minutes. The service is based on the company’s open source Milvus vector database. Frank Liu, director of operations for Zilliz, said the managed Zilliz Cloud service running on the Amazon Web Services (AWS) cloud will […]
Where DataOps and Opportunities Converge
In today’s data age, getting data analytics right is more essential than ever. A robust data analytics implementation enables businesses to hit key performance metrics, build data and AI-driven customer experiences (think ‘personalize my feed’) and capture operational issues before they spiral out of control. The list of competitive advantages goes on, but the bottom […]
MLOps Vs. DevOps: What’s the Difference?
Machine learning operations, or MLOps for short, is a key aspect of machine learning (ML) engineering that focuses on simplifying and accelerating the process of delivering ML models to production and maintaining and monitoring them. MLOps involves collaboration between different teams including data scientists, DevOps engineers, IT specialists and others. MLOps can help organizations create […]
Top 3 Requirements for Next-Gen ML Tools
As the machine learning market matures, new tools are evolving that better match data science and machine learning teams’ needs. Vendors, both open source and private, have been quick to introduce new products that better meet requirements making it easier and faster to develop models and enable collaboration. These new offerings range from cloud-based platforms […]
Positioning ML Devs and Teams for Success
Intelligent applications are (by their very nature) complex. While conventional software basically consists of one thing (code), intelligent software involves code, models and data. As previously discussed, three distinct fields—DevOps, MLOps and DataOps—have evolved to govern each of these interconnected disciplines. Moving through the ML life cycle quickly and efficiently requires collaboration between teams in […]
What is MLOps? DataOps? And Why do They Matter?
Let’s look at three distinct disciplines—DevOps, MLOps and DataOps. In 2011, Marc Andreessen famously proclaimed that software was “eating the world.” A little more than a decade later, it’s all but impossible to argue with his premonition as software has embedded itself into virtually every industry. But now another shift is underway and AI is gobbling […]
Iterative Adds Experiment Versioning to MLOps Platform
Iterative today added an experiment versioning capability to an open source platform for managing machine learning operations (MLOps) using GitOps workflows. Dmitry Petrov, Iterative CEO, said the latest version of the Data Versioning Control (DVC) platform makes it simpler to save, compare and reproduce machine learning (ML) experiments at scale without requiring organizations to set […]
Oracle Adds AI Services to Cloud Portfolio
Oracle today unfurled Oracle Cloud Infrastructure (OCI) AI services, a collection of services that make it easier for developers to use application programming interfaces (APIs) to invoke a wide range of services based on algorithms trained by Oracle on their behalf. Elad Ziklik, vice president of product management for AI services and data science at […]
Mona Allies with New Relic to Converge MLOps and DevOps
New Relic and Mona, a provider of a platform for monitoring the models used to provide artificial intelligence (AI), announced today they have formed an alliance to help bridge the divide between DevOps teams and data scientists. Most data science teams today build models based on machine learning algorithms using operations platforms that enable them […]
Learn a Bit About AI
Every once in a while, a trend becomes broad enough that I feel the need to offer career advice. This year it will be simple: Know enough about AI/ML to be dangerous. We are past the point where not having at least a basic understanding is a good idea. Looking around the data center, AI […]
Why You Should Use GitOps to Experiment With AI
As the pace at which artificial intelligence (AI) models are being constructed and inevitably updated starts to increase, it’s becoming more apparent that the pace at which data science teams are currently experimenting also needs to dramatically quicken. Many data science teams today are fortunate if they manage to successfully deploy two AI models in […]
Enterprise ML Investments on the Rise
The second wave of enterprise artificial intelligence and machine learning (AI/ML) adoption is building, and is expected to crest in 2021, as many organizations increased and accelerated their investments in tools and people. That was one of the unmistakable conclusions of our 2021 enterprise trends in machine learning report: 83% of the companies we surveyed […]
How MLOps Can Help Get AI Projects to Deployment
Did you know that most AI projects never get fully deployed? In fact, a recent survey by NewVantage Partners revealed that only 15% of leading enterprises have gotten any AI into production at all. Unfortunately, many models get built and trained, but never make it to business scenarios where they can provide insights and value. […]
AWS Looks to Meld MLOps and DevOps
The rate at which machine learning operations (MLOps) and DevOps best practices are converging is poised for rapid acceleration in 2021. At the recent online AWS re:Invent conference, Amazon Web Services (AWS) announced it is adding a bevy of capabilities to Amazon SageMaker, a managed MLOps service, that includes a continuous integration/continuous delivery (CI/CD) service […]
Are Self-Service Machine Learning Models the Future of AI Integration?
DevOps teams seeking to step up their mojo in developing cutting-edge artificial intelligence (AI) features are facing a big skills bottleneck when it comes to data analytics and machine learning modeling. As a result, the market is seeing an influx of self-service machine learning models and machine learning-as-a-service offerings designed to help development teams more […]