MLflow and Azure Machine Learning - The Power Couple for ML Lifecycle Management
Explore MLflow and Azure Machine Learning integration for streamlined ML lifecycle management, covering versioning, run history, automation, deployment, and CI/CD pipelines in MLOps.
Navigating the MLOps Landscape
Expert panel discusses MLOps challenges, best practices, and innovations. Gain insights on operationalizing machine learning, leveraging open-source tools, and addressing security concerns in large-scale ML systems.
End to End Deep Learning Project Using MLOPS DVC Pipeline With Deployments Azure and AWS - Krish Naik
Comprehensive guide to building and deploying a deep learning project for chicken disease classification using MLOps tools, DVC pipeline, and cloud platforms Azure and AWS.
Flawed Machine Learning Security - Alejandro Saucedo
Explore critical security flaws in ML lifecycle and learn prevention strategies. Gain insights on MLSecOps, challenges in production systems, and OWASP Top 10 for Machine Learning from an industry expert.
Making MLOps Uncool Again
Learn to build an automated MLOps workflow using Git, GitHub, and open-source tools, covering the entire ML model lifecycle from data labeling to prediction monitoring.
Intelligent Model Deployment and Consumer Pipeline With DataRobot AI Cloud Platform
Explore DataRobot AI Cloud's MLOps capabilities, including model deployment, registry, and inference. Learn to deploy models from leaderboards and consume them via API, stream, local mode, and job workflows.
Git-Based CI-CD for ML
Learn to implement Git-based MLOps for continuous delivery, automating workflows, and enabling collaboration. Explore open-source tools and hosted ML platforms with a live demo on streamlining ML pipelines.
Model Experiments Tracking and Registration Using MLflow on Databricks
Automate ML model experimentation and tracking using MLflow on Databricks. Learn to streamline data preparation, model development, and artifact management for efficient machine learning workflows.
Taking Machine Learning to Production with MLflow - New Features and Best Practices
Explore MLflow's latest features for productionizing machine learning, including model management, CI/CD, data schemas, and integration with PyTorch for seamless deployment and operation of ML applications.
Taking Deep Learning to Production with MLflow and RedisAI
Explore MLflow and RedisAI integration for efficient deep learning model deployment, featuring multi-framework support, auto-batching, and DAGing capabilities in a reliable runtime environment.
Productionizing Machine Learning Pipelines with Databricks and Azure ML
Jumpstart ML projects with Databricks and Azure ML. Learn to build, track, and deploy production-ready pipelines using tools like MLflow, TensorFlow, and PyTorch for efficient, reproducible data science workflows.
Consolidating MLOps at Schiphol Airport Using MLFlow
Explore how Schiphol Airport leverages MLFlow for rapid iteration, deployment, and monitoring of mission-critical machine learning models, enabling multiple weekly releases and efficient adaptation to changing dynamics.
Accelerate ML Development With Cloud-Based Arm Cortex-M Models
Explore cloud-based Arm Cortex-M models for accelerated ML development, featuring Virtual Hardware integration, Python-scriptable peripherals, and TensorFlow optimizations.
Quick to Production: Integrating Spark and TensorFlow for Efficient MLOps
Learn to quickly deploy deep learning models using Spark and TensorFlow, simplifying MLOps for data scientists. Explore distributed computing, feature engineering, and leveraging TensorFlow ecosystem libraries for efficient model production.
The Challenges of Managing Kubernetes-Based Machine Learning Infrastructure
Explore challenges in managing Kubernetes-based ML infrastructure with experts from Bloomberg, Seldon, and Spotify. Learn about tools, use cases, and solutions for on-prem and cloud environments.
Machine Learning Using Various GPU Technologies with Kubeflow
Explore efficient distributed learning in Kubeflow using Multi-Instance GPU and GPUDirect RDMA technologies to maximize GPU utilization and improve performance in machine learning workflows.
Prometheus in the MLOps Lifecycle
Explore Prometheus for monitoring ML deployments, addressing data drift, concept drift, and model metrics. Learn to enhance existing setups and integrate with Flyte, Seldon Core, or FastAPI.
Hardening Kubeflow Security for Enterprise Environments
Learn about Kubeflow security vulnerabilities, best practices, and improvements for enterprise environments. Gain insights on addressing unauthorized access, user impersonation, and data protection in MLOps platforms.
Making the Most Out of Hardware Accelerators in Kubernetes Clusters
Optimize GPU and TPU usage in Kubernetes clusters for MLOps. Learn efficient hardware accelerator management, scaling, and time-sharing techniques to enhance machine learning workloads.
Building on-Premises MLOps for ISS Columbus Ground Operations
Explore the development of an on-premises MLOps platform for ISS Columbus operations, covering anomaly detection, root cause analysis, and automated deployment using GitLab, microk8s, and Kubeflow.
Building AutoML Pipelines with Argo Workflows and Katib
Explore AutoML pipelines using Argo Workflows and Katib for efficient model selection and hyperparameter tuning in Kubernetes environments, enhancing MLOps processes.