This article is contributed. See the original author and article here.
Announcing SynapseML v0.11. The new version contains many new features to help you build scalable machine learning pipelines.
We are pleased to announce SynapseML v0.11, a new version of our open-source distributed machine learning library that simplifies and accelerates the development of scalable AI. In this release, we are excited to introduce many new features from the past year of developments well as many bug fixes and improvements. Though this post will give a high-level overview of the most salient new additions, curious readers can check out the full release notes for all of the new additions.
OpenAI Language Models and Embeddings
A new release wouldn’t be complete without joining the large language model (LLM) hype train and SynapseML v0.11 features a variety of new features that make large-scale LLM usage simple and easy. In particular, SynapseML v0.11 introduces three new APIs for working with foundation models: `OpenAIPrompt`, ` OpenAIEmbedding`, and `OpenAIChatCompletion`. The `OpenAIPrompt` API makes it easy to construct complex LLM prompts from columns of your dataframe. Here’s a quick example of translating a dataframe column called “Description” into emojis.
from synapse.ml.cognitive.openai import OpenAIPrompt
emoji_template = """
Translate the following into emojis
Word: {Description}
Emoji: """
results = (OpenAIPrompt()
.setPromptTemplate(emoji_template)
.setErrorCol("error")
.setOutputCol("Emoji")
.transform(inputs))
This code will automatically look for a database column called “Description” and prompt your LLM (ChatGPT, GPT-3, GPT-4) with the created prompts. Our new OpenAI embedding classes make it easy to embed large tables of sentences quickly and easily from your Apache Spark clusters. To learn more, see our docs on using OpenAI embeddings API and the SynapseML KNN model to create an LLM-based vector search engine directly on your spark cluster. Finally, the new OpenAIChatCompletion transformer allows users to submit large quantities of chat-based prompts to ChatGPT, enabling parallel inference of thousands of conversations at a time. We hope you find the new OpenAI integrations useful for building your next intelligent application.
Simple Deep Learning
SynapseML v0.11 introduces a new Simple deep learning package that allows for the training of custom text and deep vision classifiers with only a few lines of code. This package integrates the power of distributed deep network training with PytorchLightning with the simple and easy APIs of SynapseML. The new API allows users to fine-tune visual foundation models from torchvision as well as a variety of state-of-the-art text backbones from HuggingFace.
Here’s a quick example showing how to fine-tune custom vision networks:
Keep an eye out with upcoming new releases of SynapseML featuring additional simple deep-learning algorithms that will make it easier than ever to train and deploy models at scale.
LightGBM v2
LightGBM is one of the most used features of SynapseML and we heard your feedback on better performance! SynapseML v0.11 introduces a completely refactored integration between LightGBM and Spark, called LightGBM v2. This integration aims for high performance by introducing a variety of new streaming APIs in the core LightGBM library to enable fast and memory-efficient data sharing between spark and LightGBM. In particular, the new “Streaming execution mode” has a >10x lower memory footprint than earlier versions of SynapseML yielding fewer memory issues and faster training. Best of all, you can use the new mode by just passing a single extra flag to your existing LightGBM models in SynapseML.
ONNX Model Hub
SynapseML supports a variety of new deep learning integrations with the ONNX runtime for fast, hardware-accelerated inference in all of the SynapseML languages (Scala, Java, Python, R, and .NET). In version 0.11 we add support for the new ONNX model hub, which is an open collection of state-of-the-art pre-trained ONNX models that can be quickly downloaded and embedded into spark pipelines. This allowed us to completely deprecate and remove our old dependence on the CNTK deep learning library.
To learn more about how you can embed deep networks into Spark pipelines, check out our ONNX episode in the new SynapseML video series:
Causal Learning
SynapseML v0.11 introduces a new package for causal learning that can help businesses and policymakers make more informed decisions. When trying to understand the impact of a “treatment” or intervention on an outcome, traditional approaches like correlation analysis or prediction models fall short as they do not necessarily establish causation. Causal inference aims to overcome these shortcomings by bridging the gap between prediction and decision-making. SynapseML’s causal learning package implements a technique called “Double machine learning”, which allows us to estimate treatment effects without data from controlled experiments. Unlike regression-based approaches, this approach can model non-linear relationships between confounders, treatment, and outcome. Users can run the DoubleMLEstimator using a simple code snippet like the one below:
from pyspark.ml.classification import LogisticRegression
from synapse.ml.causal import DoubleMLEstimator
dml = (DoubleMLEstimator()
.setTreatmentCol("Treatment")
.setTreatmentModel(LogisticRegression())
.setOutcomeCol("Outcome")
.setOutcomeModel(LogisticRegression())
.setMaxIter(20))
dmlModel = dml.fit(dataset)
For more information, be sure to check out Dylan Wang’s guided tour of the DoubleMLEstimator on the SynapseML video series:
Vowpal Wabbit v2
Finally, SynapseML v0.11 introduces Vowpal Wabbit v2, the second-generation integration between the Vowpal Wabbit (VW) online optimization library and Apache Spark. With this update, users can work with Vowpal wabbit data directly using the new “VowpalWabbitGeneric” model. This makes working with Spark easier for existing VW users. This more direct integration also adds support for new cost functions and use cases including “multi-class” and “cost-sensitive one against all” problems. The update also introduces a new progressive validation strategy and a new Contextual Bandit Offline policy evaluation notebook to demonstrate how to evaluate VW models on large datasets.
Conclusion
In conclusion, we are thrilled to share the new SynapseML library with you with you and hope you will find that it simplifies your distributed machine learning pipelines. This blog only covered the highlights, so be sure to check out the full release notes for all the updates and new features. Whether you are working with large language models, training custom classifiers, or performing causal inference, SynapseML makes it easier and faster to develop and deploy machine learning models at scale.
This article is contributed. See the original author and article here.
We’re excited to return to Field Service Palm Springs from April 25 through April 27, 2023, at the JW Marriott Desert Springs Resort & Spa.
We will showcase how Connected Field Service helps leaders:
Move beyond the costly break/fix model to a proactive, predictive model.
Unlock the power of data and use Internet of Things (IoT), machine learning, and AI.
Transform their field operations and improve customer experience.
This year, we are hosting a thought leadership luncheon with our partner Hitachi Solutions to discuss the benefits of a connected field service and how to use data to remain competitive, and continuously improve business performance and customer experiences in an increasingly challenging environment.
Field service organizations manage hundreds of technicians with varying expertise, experiences, and skills. With 80 percent of consumers more likely to make a purchase from a brand that provides personalized experiences, organizations have come to realize how important quality service is to remain resilient despite uncertainty.1 Employees are working from remote or distributed locations, reducing the amount of personalized interaction. Meanwhile, remote monitoring of IoT devices continues to transform service from a cost center to a revenue generator.
Connected Field Service is the ability to add connected devices, powered by the Internet of Things (IoT), and uses cloud capabilities to augment your existing field service operations. It enables organizations to transform the way they provide service from a costly, reactive break-fix model to a proactive, and in some cases, even predictive service model through the holistic combination of IoT diagnostics, scheduling, asset maintenance, and inventory on the same platform.
IoT has brought a new level of efficiency to the field service industry, helping service professionals address issues more proactively and minimize downtime. As McKinsey researchers predict, IoT applications could generate a value of over $470 billion annually by 2025 by enhancing operations across various industries.2
By integrating IoT signals across the enterprise, a connected field service helps organizations predict and resolve customer issues before the customer is aware, thereby ensuring consistent and dependable customer operations through hassle-free and preemptive field service.
Four Connected Field Service solutions
Connected Field Service combines four innovative Microsoft solutions that enable service leaders to digitally transform service organizations:
Provides service technicians with upsell and cross-sell recommendations
Enables team members in non-sales roles to advance deals with step-by-step guidance
Enables sales teams and service technicians to access customer information and sales resources in non-office environments
Drives visibility into product and parts usage across the organization
Connected Field Service becomes a reality with Microsoft. Service leaders can better manage costs, enhance service delivery, and increase customer satisfaction (CSAT) by proactively resolving customer issues before the customer is aware. Take advantage of smart, internet-ready devices that can detect and diagnose issues, integrating with field service management (FSM) software like Dynamics 365 Field Service to automatically initiate troubleshooting and, when needed, create work orders to dispatch technicians for onsite service. Learn how you can use technology to schedule preventative maintenance based on consumption rather than rely on a regimented schedule. Best of all, enjoy the flexibility of implementing the solution in stages so your team can ramp up via a natural progression. Learn more about the latest Dynamics 365 Field Service features.
Engage with Microsoft at Field Service Palm Springs 2023
We invite you to join us, along with our partners, to discover how Connected Field Service using Dynamics 365 Field Service and IoT can help create a seamless service experience that enhances customer experiences, increases cost savings, and improves efficiency.
Register for Field Service Palm Springs and visit the Microsoft booth (101/103) where you can meet with Dynamics 365 Field Service experts to discuss how connected data enables better experiences across your organization.
About Field Service Palm Springs
For 20 years, Field Service Palm Springs has become the must-attend conference for service executives. From early IoT concepts to AI, Field Service is where innovative ideas spread, and future strategies are created. Today, Field Service is a global event, with major conferences in Palm Springs, Amelia Island, San Diego, Amsterdam, and Singapore.
Since 2003, the top service and support minds have gathered in Palm Springs in April for the flagship Field Service conference. With forward-looking content and unique session formats that ensure you learn and network most effectively, Field Service is designed to help you achieve service excellence and drive profitability.
Microsoft Dynamics 365 Field Service
Optimize service operations and inventory management.
This article is contributed. See the original author and article here.
Pricing is one of the fundamental tools to boost supply chain profits by better matching supply and demand. Many businesses have started to reform their pricing strategies in recent years as a result of the growth of e-commerce and the constantly changing business environments in order to improve pricing transparency, supply chain agility and margin optimization.
We are launching the Public Preview of Pricing management within Dynamics 365 Supply Chain Management from 10.0.33 to support sales managers managing and execute the attribute-based omnichannel sales pricing.
Why attribute-based omnichannel pricing?
Transaction to omnichannel pricing:
Traditional business-to-business (B2B) organizations are increasingly considering switching to omnichannel sales and selling directly to end customers in order to have greater control over price and margins. The omnichannel transformation results in significant modifications to pricing models and rules.
By offering an omnichannel price engine, a central place to manage pricing rules and automated omnichannel pricing execution, Dynamics 365 Supply Chain Management aids B2B business in the transition to omnichannel pricing.
Transaction to attribute-based pricing:
Working with the marketing and product manager to comprehend the product differentiating features, target customer segments and other pricing sensitivity elements is one of the important responsibilities of the sales managers. The package types, the delivery mode and the expected receipt date could be one of the pricing differentiators. By giving business the ability to convert data from customers, product, and orders into price attributes and building pricing on different pricing structure, Dynamics 365 Supply Chain Management supports business to adopt the attribute-based pricing model.
What is Pricing management?
Dynamics 365 Supply Chain Management Pricing Management leverages the Commerce Scale Unit (CSU) to help traditional B2B companies embrace omnichannel pricing. Pricing management enables attribute-based pricing for the price components that are across the sales pricing structures, including product base price, sales trade agreement price, discounts, charges and rebate management.
How Pricing management supports business flows:
DESIGN your pricing component types using price attributes.
CONSTRUCT your pricing structure with pricing components, such as margin elements.
MANAGE price markup based on product standard cost (for manufactured products) or vendor price catalog (for trading products).
SIMULATE pricing rules and impacts.
EXECUTE pricing calculation across channels.
MONITOR promotion fund consumption with control.
Flexible data model for building price attributes. Price attributes can be based on categorized product pricing differentiators, customer groups and order types.
Central place to offer, manage and calculate pricing. Boost pricing transparency across channels, which is essential for aligning pricing strategies across multiple channels.
Manage complex pricing structureswith price component breakdowns. When you place an order, the pricing details reflect the pricing structure for you to understand the pricing calculation sequence and price breakdowns for future in-depth analysis.
Establish the sophisticated pricing with pricing simulator to evaluate the impact. When converting from B2B pricing to B2B and B2C pricing, consider discount concurrency, bundle sales, mandatory sales items, and bonus free item pricing rules.
Fund control to ensure you don’t avoid margin leakage from fund consumption.
Real-time cross channel pricing execution with the pricing engine to quickly determine pricing while considering a variety of commercial aspects, such as the item’s general base price, the price of a sales trade agreements, long-term discount agreements, short-term promotion discounts, and retrospective rebate calculations for each sales order.
External applications can retrieve calculated pricing by leverage the Commerce Scale Unit (CSU)based Pricing APIs.
Next steps:
If your organization on the journal transition to attribute-based omnichannel selling pricing, consider taking the next step with Pricing Management within Dynamics 365 Supply Chain Management.
If you are a potential customer or partner and want to learn more, contact the https://learn.microsoft.com/en-us/dynamics365/supply-chain/pricing-management/price-attributes-overview product team directly by email.
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