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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

It includes processes for monitoring model performance, managing risks, ensuring data quality, and maintaining transparency and accountability throughout the model’s lifecycle. It’s a binary classification problem where the goal is to predict whether a customer is a credit risk. region_name ram_client = boto3.client('ram')

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How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning Blog

Each business problem is different, each dataset is different, data volumes vary wildly from client to client, and data quality and often cardinality of a certain column (in the case of structured data) might play a significant role in the complexity of the feature engineering process.

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How Memorial Sloan Kettering Cancer Center (MSKCC) used Snorkel Flow to scale clinical trial screening

Snorkel AI

Scaling clinical trial screening with document classification Memorial Sloan Kettering Cancer Center, the world’s oldest and largest private cancer center, provides care to increase the quality of life of more than 150,000 cancer patients annually. However, lack of labeled training data bottlenecked their progress.

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Top 5 Challenges faced by Data Scientists

Pickl AI

Data Science is the process in which collecting, analysing and interpreting large volumes of data helps solve complex business problems. A Data Scientist is responsible for analysing and interpreting the data, ensuring it provides valuable insights that help in decision-making.

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How Pixability uses foundation models to accelerate NLP application development by months

Snorkel AI

Using Snorkel Flow, Pixability leveraged foundation models to build small, deployable classification models capable of categorizing videos across more than 600 different classes with 90% accuracy in just a few weeks. To help brands maximize their reach, they need to constantly and accurately categorize billions of YouTube videos.

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Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

It also enables you to evaluate the models using advanced metrics as if you were a data scientist. In this post, we show how a business analyst can evaluate and understand a classification churn model created with SageMaker Canvas using the Advanced metrics tab.