AWS Machine Learning Blog

SEPTEMBER 18, 2023

With Ray and AIR, the same Python code can scale seamlessly from a laptop to a large cluster. Amazon SageMaker Pipelines allows orchestrating the end-to-end ML lifecycle from data preparation and training to model deployment as automated workflows. In the next section, we highlight key code snippets from each step.

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AWS Machine Learning Blog

NOVEMBER 19, 2024

This session covers the technical process, from data preparation to model customization techniques, training strategies, deployment considerations, and post-customization evaluation. Explore how this powerful tool streamlines the entire ML lifecycle, from data preparation to model deployment.

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AWS Machine Learning Blog

MARCH 6, 2023

These factors require training an LLM over large clusters of accelerated machine learning (ML) instances. Within one launch command, Amazon SageMaker launches a fully functional, ephemeral compute cluster running the task of your choice, and with enhanced ML features such as metastore, managed I/O, and distribution.

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Flipboard

AUGUST 17, 2023

If you are prompted to choose a kernel, choose Data Science as the image and Python 3 as the kernel, then choose Select. as the image and Glue Python [PySpark and Ray] as the kernel, then choose Select. The environment preparation process may take some time to complete.

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Towards AI

JULY 19, 2023

Data Preparation — Collect data, Understand features 2. Visualize Data — Rolling mean/ Standard Deviation— helps in understanding short-term trends in data and outliers. The rolling mean is an average of the last ’n’ data points and the rolling standard deviation is the standard deviation of the last ’n’ points.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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Towards AI

JUNE 27, 2023

Scikit Learn Scikit Learn is a comprehensive machine learning tool designed for data mining and large-scale unstructured data analysis. With an impressive collection of efficient tools and a user-friendly interface, it is ideal for tackling complex classification, regression, and cluster-based problems.

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AWS Machine Learning Blog

DECEMBER 13, 2024

This allows scientists and model developers to focus on model development and rapid experimentation rather than infrastructure management Pipelines offers the ability to orchestrate complex ML workflows with a simple Python SDK with the ability to visualize those workflows through SageMaker Studio. tag = "latest" container_image_uri = "{0}.dkr.ecr.{1}.amazonaws.com/{2}:{3}".format(account_id,

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AWS Machine Learning Blog

JULY 11, 2024

Fine tuning embedding models using SageMaker SageMaker is a fully managed machine learning service that simplifies the entire machine learning workflow, from data preparation and model training to deployment and monitoring. Python script that serves as the entry point. client('s3') # Get the region name session = boto3.Session()

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AWS Machine Learning Blog

AUGUST 22, 2023

Many ML algorithms train over large datasets, generalizing patterns it finds in the data and inferring results from those patterns as new unseen records are processed. Data is split into a training dataset and a testing dataset. Details of the data preparation code are in the following notebook.

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Becoming Human

MAY 15, 2023

One is a scripting language such as Python, and the other is a Query language like SQL (Structured Query Language) for SQL Databases. Python is a High-level, Procedural, and object-oriented language; it is also a vast language itself, and covering the whole of Python is one the worst mistakes we can make in the data science journey.

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Pickl AI

NOVEMBER 28, 2024

Key programming languages include Python and R, while mathematical concepts like linear algebra and calculus are crucial for model optimisation. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. This growth signifies Python’s increasing role in ML and related fields.

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phData

FEBRUARY 7, 2024

Snowpark is the set of libraries and runtimes in Snowflake that securely deploy and process non-SQL code, including Python, Java, and Scala. On the server side, runtimes include Python, Java, and Scala in the warehouse model or Snowpark Container Services (public preview).

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Learning means identifying and capturing historical patterns from the data, and inference means mapping a current value to the historical pattern. The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. GPU PBAs, 4% other PBAs, 4% FPGA, and 0.5%

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Mlearning.ai

JUNE 28, 2023

The expeditious and efficient construction, deployment, and scalability of machine learning models assume utmost importance in unearthing the untapped potential of data-driven decision-making. This extensive repertoire includes classification, regression, clustering, natural language processing, and anomaly detection.

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phData

DECEMBER 4, 2023

Thirty seconds is a good default for human users; if you find that queries are regularly queueing, consider making your warehouse a multi-cluster that scales on-demand. Cluster Count If your warehouse has to serve many concurrent requests, you may need to increase the cluster count to meet demand.

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The MLOps Blog

APRIL 5, 2023

The platform can assign specific roles to team members involved in the packaging process and grant them access to relevant aspects such as data preparation, training, deployment, and monitoring. Developers can deploy their models on a cluster of servers and use Kubernetes to manage the resources needed for training and inference.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

An AutoML tool applies a combination of different algorithms and various preprocessing techniques to your data. For example, it can scale the data, perform univariate feature selection, conduct PCA at different variance threshold levels, and apply clustering.

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AWS Machine Learning Blog

OCTOBER 23, 2023

Feature engineering We perform two sets of feature engineering processes to extract valuable information from the raw data and feed it into the corresponding towers in the model: standard feature engineering and fine-tuned SBERT embeddings. Standard feature engineering Our data preparation process begins with standard feature engineering.

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AWS Machine Learning Blog

APRIL 16, 2024

Jupyter notebooks can differentiate between SQL and Python code using the %%sm_sql magic command, which must be placed at the top of any cell that contains SQL code. This command signals to JupyterLab that the following instructions are SQL commands rather than Python code. Choose the Redshift cluster associated with the secrets.

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Pickl AI

DECEMBER 3, 2024

It is a central hub for researchers, data scientists, and Machine Learning practitioners to access real-world data crucial for building, testing, and refining Machine Learning models. The publicly available repository offers datasets for various tasks, including classification, regression, clustering, and more.

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Pickl AI

OCTOBER 15, 2024

Applications : Stock price prediction and financial forecasting Analysing sales trends over time Demand forecasting in supply chain management Clustering Models Clustering is an unsupervised learning technique used to group similar data points together. Popular clustering algorithms include k-means and hierarchical clustering.

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Pickl AI

NOVEMBER 5, 2024

Augmented Analytics Combining Artificial Intelligence with traditional analytics allows businesses to gain insights more quickly by automating data preparation processes. Programming Skills Proficiency in programming languages like Python and R is essential for Data Science professionals.

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Pickl AI

MAY 30, 2024

Additionally, you will work closely with cross-functional teams, translating complex data insights into actionable recommendations that can significantly impact business strategies and drive overall success. Also Read: Explore data effortlessly with Python Libraries for (Partial) EDA: Unleashing the Power of Data Exploration.

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The MLOps Blog

JUNE 27, 2023

For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc., It provides tools and components to facilitate end-to-end ML workflows, including data preprocessing, training, serving, and monitoring. and Pandas or Apache Spark DataFrames.

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Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance. UnSupervised Learning Unlike Supervised Learning, unSupervised Learning works with unlabeled data. The algorithm tries to find hidden patterns or groupings in the data.

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DagsHub

APRIL 21, 2024

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD.

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Pickl AI

NOVEMBER 4, 2024

By implementing efficient data pipelines , organisations can enhance their data processing capabilities, reduce time spent on data preparation, and improve overall data accessibility. Data Storage Solutions Data storage solutions are critical in determining how data is organised, accessed, and managed.

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ODSC - Open Data Science

JANUARY 29, 2024

These outputs, stored in vector databases like Weaviate, allow Prompt Enginers to directly access these embeddings for tasks like semantic search, similarity analysis, or clustering. Python Python’s prominence is expected. Kubernetes: A long-established tool for containerized apps.

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AWS Machine Learning Blog

JULY 16, 2024

In this post, we present a step-by-step guide to run distributed training workloads on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The NVIDIA NeMo Framework provides a comprehensive set of tools, scripts, and recipes to support each stage of the LLM journey, from data preparation to training and deployment.

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The MLOps Blog

MAY 9, 2023

Again, what goes on in this component is subjective to the data scientist’s initial (manual) data preparation process, the problem, and the data used. Metaflow differs from other pipelining frameworks because it can load and store artifacts (such as data and models) as regular Python instance variables.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Nobody else offers this same combination of choice of the best ML chips, super-fast networking, virtualization, and hyper-scale clusters. This typically involves a lot of manual work cleaning data, removing duplicates, enriching and transforming it.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

Airflow for workflow orchestration Airflow schedules and manages complex workflows, defining tasks and dependencies in Python code. An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time.

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AWS Machine Learning Blog

MARCH 18, 2025

We cover the setup process and provide a step-by-step guide to running a NeMo job on a SageMaker HyperPod cluster. is a flexible, IDE-independent Python-based framework that enables flexible integration in each developers workflow. In this blog post, we explore how to integrate NeMo 2.0 NeMo Framework 2.0

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AWS Machine Learning Blog

DECEMBER 18, 2024

This strategic decision was driven by several factors: Efficient data preparation Building a high-quality pre-training dataset is a complex task, involving assembling and preprocessing text data from various sources, including web sources and partner companies. The team opted for fine-tuning on AWS.

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AWS Machine Learning Blog

JANUARY 28, 2025

RAG retrieves data from a preexisting knowledge base (your data), combines it with the LLMs knowledge, and generates responses with more human-like language. However, in order for generative AI to understand your data, some amount of data preparation is required, which involves a big learning curve.

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