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Why AutoML Isn’t Enough to Democratize Data Science 

In this contributed article, Noam Brezis, co-founder and CTO of Pecan AI, explores that because AutoML was born out of academia, in its current incarnation it is only built to simplify the model building process. This is likely the reason why existing AutoML solutions are finding challenges with scaling. Plus, these types of solutions are not incorporating the aspects of data prep and feature engineering, nor the model training, deployment and monitoring, which as a result slowing down adoption of AI within the enterprise and curtailing the impact it can deliver.

New Study: Amid ChatGPT Craze, MLOps Spend Will Surge in 2023 as 60% of Machine Learning Leaders Plan to Boost Investments by Over 25%

ClearML, a leading open source, end-to-end MLOps platform, announced wide availability of its new, in-depth research report, MLOps in 2023: What Does the Future Hold? Polling 200 U.S.-based machine learning decision makers, the report examines key trends, opportunities, and challenges in machine learning and MLOps (machine learning operations).

Comet Introduces Kangas, An Open Source Smart Data Exploration, Analysis and Model Debugging Tool for Machine Learning 

Comet, provider of a leading MLOps platform for machine learning (ML) teams from startup to enterprise, announced a bold new product: Kangas. Open sourced to democratize large scale visual dataset exploration and analysis for the computer vision and machine learning community, Kangas helps users understand and debug their data in a new and highly intuitive way.

Discover the Secret to Building Effective ML Teams

What does it take to run an efficient ML team? Many wonder why some teams fail and a few others succeed. Though there is no one answer to this question, our friends over at Comet found three major success factors: visibility, reproducibility, and collaboration. Read Comet’s paper on “Building Effective Machine Learning Teams,” and gain deeper insights on how you can apply three ML components to your teams.

Seagate Launches Lyve Cloud Analytics Platform to Optimize Machine Learning Operations and Accelerate Innovation

Seagate® Technology Holdings plc (NASDAQ: STX), a world leader in mass-data storage infrastructure solutions, announced the launch of Lyve™ Cloud Analytics platform, a complete cloud-based analytics solution that includes storage, compute, and analytics, to help Lyve Cloud customers lower the total cost of ownership (TCO) and accelerate time to value with their DataOps and MLOps (machine learning operations).

ClearML Announces Availability of Unified, End-to-End MLOps Solution for Enterprises

ClearML, the frictionless, unified, end-to-end MLOps platform, announced the general availability of ClearML for Enterprise. Previously, the platform was only available to a select group of customers on an invite-only basis and is now widely available to all enterprise organizations across industries such as healthcare, healthtech, retailtech, adtech, martech, and manufacturing, among others.

The Secret to Automating Machine Learning Life Cycles

In this contributed article, Lucas Bonatto, CEO & Founder of Elemeno, suggests that the constant use, upgrade, and acceleration of AI and machine learning will create countless opportunities for enabling innovation in organizations outside IT, as well as adapting to changes in the IT Operations Model. The secret to automating ML lifecycles is to increase the adoption of AI around the world. The first step to achieve this goal is by providing an end-to-end ML-Ops platform with an AI Marketplace where users can obtain models, making the use of AI as seamless as possible.

MLOps | Is the Enterprise Repeating the Same DIY Mistakes?

In this contributed article, Aaron Friedman, VP of Operations at Wallaroo.ai, discusses why hiring data scientists isn’t the answer to unlocking ML value (especially at a time when finding qualified candidates is harder than ever).

Run:ai Releases Advanced Model Serving Functionality to Help Organizations Simplify AI Deployment 

Run:ai, a leader in compute orchestration for AI workloads, announced new features of its Atlas Platform, including two-step model deployment — which makes it easier and faster to get machine learning models into production. The company also announced a new integration with NVIDIA Triton Inference Server. These capabilities are particularly focused on supporting organizations in […]

Datatron Simplifies Platform for Operationalization of ML Models 

Datatron announced the latest version of its enterprise-grade MLOps platform. Updates include increased flexibility, a new interface that simplifies data scientists’ workflow, and ease-of-use enhancements for the operational teams, resulting in an additional productivity gain of up to 68%.