One of the biggest threats to productivity in recent times is context switching. It is a term originating from computer science but applied to humans it refers to the process of stopping work on one thing, performing a different task, and then picking back up the initial task. During a work day, you might want to check something on Stack Overflow, for example, which normalization technique to choose for your project. While doing so, you start exploring the documentation of scikit-learn to see which approaches are already implemented and how they compare against each other. This might lead to you some interesting comparison articles on Medium or video tutorials on YouTube.

Machine learning (ML) is essential to video game development. Predicting specific in-game actions and identifying and reaching your most valuable players helps to drive better outcomes. To this end, we need to keep track of all experiments that are happening behind the scenes. Google Cloud recently announced the general availability of a new feature called Vertex AI Experiments that can now help gaming companies do just that – keep track of their ML experiments to uncover insights and best practices across their ML Engineering and Data Science teams. In this blog post, we will focus on a popular video game dataset coming from the EA Sports' FIFA video game series.

Single-machine and single-agent RL training have many challenges, the most important being the time it takes for the rewards to converge. Most of the time spent by the agent in RL training goes into gathering experiences. The time taken for simple applications is a few hours, and complex applications take days. Deep Learning frameworks like Tensorflow support distributed training; can the same be applied to RL as well? This article focuses on specific pain points of single-machine training with a practical example and demonstrates how scaled RL solves the problem.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Neptune.ai, a Polish startup that helps enterprises manage model metadata, today announced it has raised $8 million in series A funding. Whenever an organization experiments with machine learning (ML) models, every iteration that they go through results in metadata such as references and insights from the datasets being used, code versions, environment changes, hardware, evaluation and testing metrics, and predictions. This information is constantly evolving, leaving a complex trail of version histories. So, when something goes wrong, it becomes incredibly difficult for the ML engineers to unpick what caused the issue and when.

Waiting for scripts to terminate is an absolute pet peeve of mine. Not only is it a waste of time but the constant context-switching is distracting and tiring. One grievous offender is the ML experiment, which lasts anywhere from a few minutes to a few days. A fixture of the development lifecycle, the ML experiment takes place any time you want to evaluate your model on a new set of hyperparameters (as in hyperparameter optimization), explore new features (feature selection), or try out a new model architecture. In a way, features and model architectures are simply glorified forms of hyperparameters.

Despite the investments and commitment from leadership, many organizations are yet to realize the full potential of artificial intelligence (AI) and machine learning (ML). Data science and analytics teams are often squeezed between increasing business expectations and sandbox environments evolving into complex solutions. This makes it challenging to transform data into solid answers for stakeholders consistently. How can teams tame complexity and live up to the expectations placed on them? There is no one size fits all when it comes to implementing an MLOps solution on Amazon Web Services (AWS).

Alphabet CEO Sundar Pichai has introduced Vertex AI, a managed machine learning platform for deploying and maintaining AI models, during his keynote speech at the recently concluded Google I/O conference. The new platform brings AutoML and AI Platform together into a unified API, client library and user interface. "When we were training algorithms before, we would have to run millions of test images," said Jeff Houghton, chief operating officer of L'Oréal's ModiFace, which develops augmented reality and AI digital services for the beauty industry. "Now, we can rely on the Vertex technology stack to do the heavy lifting. Vertex has the computing power to figure out complex problems. It can do billions of iterations, and Vertex comes up with the best algorithms," Houghton added.

Line 5: We import the mlflow library Line 6: Here, we import the relevant mlflow.sklearn This entirely depends on which package the model is built on. The complete list of available modules can be found in the official MLflow Python API documentation. Line 7: Autologging is a recently introduced experimental feature that makes the MLflow integration hassle-free. This function automatically logs all the parameters, metrics and saves the model artifacts in one place.

If you encounter this error: AutoMLEnsembleException: Could not find any models for running ensembling, then you may need to provide more time for the models to be downloaded. The default value is 300 seconds for downloading these models in parallel and there is no maximum timeout limit. Configure this parameter with a higher value than 300 secs, if more time is needed. If the timeout is reached and there are models downloaded, then the ensembling proceeds with as many models it has downloaded. It's not required that all the models need to be downloaded to finish within that timeout.

Machine learning (ML) is an increasingly important scientific tool supporting decision making and knowledge generation in numerous fields. With this, it also becomes more and more important that the results of ML experiments are reproducible. Unfortunately, that often is not the case. Rather, ML, similar to many other disciplines, faces a reproducibility crisis. In this paper, we describe our goals and initial steps in supporting the end-to-end reproducibility of ML pipelines. We investigate which factors beyond the availability of source code and datasets influence reproducibility of ML experiments. We propose ways to apply FAIR data practices to ML workflows. We present our preliminary results on the role of our tool, ProvBook, in capturing and comparing provenance of ML experiments and their reproducibility using Jupyter Notebooks.