#Pipeline Automation

[rajaniesh]

Optimizing Azure Container App Deployments: Best Practices for Pipelines & Security

🚀 Just shared a new blog on boosting Azure Container App deployments! Dive into best practices for Continuous Deployment, choosing the right agents, and securely managing variables. Perfect for making updates smoother and safer!

In the fifth part of our series, we explored how Continuous Deployment (CD) pipelines and revisions bring efficiency to Azure Container Apps. From quicker feature rollouts to minimal downtime, CD ensures that you’re not just deploying updates but doing it confidently. Now, let’s take it a step further by optimizing deployments using Azure Pipelines. In this part, we’ll dive into the nuts and…

[silusvesuius]

nelvas Problematiq*eyes roll back into my skull* *falls flat on my face with cardboard cutout physics* ((7 hours later)) *wakes up in barren wasteland* I know everything now.

[cadmusfly]

finally decided to look into automated OCR programs for batch processing an entire PDF, trying out OCRmyPDF - setting it up is easy because I am a python programmer and only having to install three packages is easy mode dont talk to me about cuda torch

I'm using a french model to OCR dead frenchmen french of course theres lots of diacritics

[bdccglobal]

Boost your team's productivity with Azure DevOps pipeline. Automate your development process and focus on what really matters - building great software!

[lucid-outsourcing-solutions]

Automate Data Processing Pipelines with ColdFusion

[jarrodcummerata]

Maximize ROI: Offshore QA Testing & DevOps Outsourcing for U.S. and European Businesses

Explore how U.S. and European companies can enhance efficiency and cut costs through offshore QA testing and DevOps outsourcing. This insightful blog discusses the advantages of leveraging global talent, best practices for managing remote teams, and key factors for selecting the right offshore partners. Discover how AquSag Technologies can help you boost product quality and streamline your software development process!

For more information visit : https://aqusag.com/blog/aqusag-technologies-blog-5/maximizing-roi-with-offshore-qa-testing-and-devops-outsourcing-for-u-s-and-european-businesses-86

[wholesalinghouses]

Unlock Your Wholesaling Success with InvestorFuse

As a real estate wholesaler, you know the grind all too well. Sifting through countless leads, managing your pipeline, and trying to close deals can feel overwhelming. One major challenge many wholesalers face is keeping track of leads and ensuring that no potential deal slips through the cracks. This disorganization not only wastes time but can lead to lost profits and missed opportunities.

Enter InvestorFuse: Your Ultimate Solution

InvestorFuse is designed specifically for real estate wholesalers looking to streamline their operations and close more deals efficiently. This powerful CRM simplifies lead management by automating follow-ups and organizing your pipeline, so you can focus on what you do best: making deals happen.

Key Benefits of InvestorFuse:

Lead Automation: Say goodbye to manual follow-ups. InvestorFuse automates your communications, ensuring that every lead receives timely attention.

Organized Pipeline: Easily visualize your leads at different stages, allowing you to prioritize effectively and never lose track of a deal.

Enhanced Collaboration: If you're working with a team, InvestorFuse helps everyone stay on the same page, promoting seamless collaboration and improved outcomes.

With its focus on helping wholesalers succeed, InvestorFuse is an invaluable tool for anyone looking to elevate their business. Try it risk-free for 30 days—click here to sign up today and watch your wholesaling business thrive!

For even more insights and resources tailored specifically for real estate wholesalers, visit WholesalingHousesInfo.com. The blog is packed with expert tips, real-life success stories, and essential tools to help you maximize your wholesaling success!

[faisalakhtar12]

Building Your Serverless Sandbox: A Detailed Guide to Multi-Environment Deployments (or How I Learned to Stop Worrying and Love the Cloud)

Introduction Welcome, intrepid serverless adventurers! In the wild world of cloud computing, creating a robust, multi-environment deployment pipeline is crucial for maintaining code quality and ensuring smooth transitions from development to production.Here is part 1 and part 2 of this series. Feel free to read them before continuing on. This guide will walk you through the process of setting…

[marketinghabib]

[jcmarchi]

Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore

New Post has been published on https://thedigitalinsider.com/datasets-matter-the-battle-between-open-and-closed-generative-ai-is-not-only-about-models-anymore/

Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore

Two major open source datasets were released this week.

Created Using DALL-E

Next Week in The Sequence:

Edge 403: Our series about autonomous agents continues covering memory-based planning methods. The research behind the TravelPlanner benchmark for planning in LLMs and the impressive MemGPT framework for autonomous agents.

The Sequence Chat: A super cool interview with one of the engineers behind Azure OpenAI Service and Microsoft CoPilot.

Edge 404: We dive into Meta AI’s amazing research for predicting multiple tokens at the same time in LLMs.

You can subscribe to The Sequence below:

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

📝 Editorial: Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore

The battle between open and closed generative AI has been at the center of industry developments. From the very beginning, the focus has been on open vs. closed models, such as Mistral and Llama vs. GPT-4 and Claude. Less attention has been paid to other foundational aspects of the model lifecycle, such as the datasets used for training and fine-tuning. In fact, one of the limitations of the so-called open weight models is that they don’t disclose the training datasets and pipeline. What if we had high-quality open source datasets that rival those used to pretrain massive foundation models?

Open source datasets are one of the key aspects to unlocking innovation in generative AI. The costs required to build multi-trillion token datasets are completely prohibitive to most organizations. Leading AI labs, such as the Allen AI Institute, have been at the forefront of this idea, regularly open sourcing high-quality datasets such as the ones used for the Olmo model. Now it seems that they are getting some help.

This week, we saw two major efforts related to open source generative AI datasets. Hugging Face open-sourced FineWeb, a 44TB dataset of 15 trillion tokens derived from 96 CommonCrawl snapshots. Hugging Face also released FineWeb-Edu, a subset of FineWeb focused on educational value. But Hugging Face was not the only company actively releasing open source datasets. Complementing the FineWeb release, AI startup Zyphra released Zyda, a 1.3 trillion token dataset for language modeling. The construction of Zyda seems to have focused on a very meticulous filtering and deduplication process and shows remarkable performance compared to other datasets such as Dolma or RedefinedWeb.

High-quality open source datasets are paramount to enabling innovation in open generative models. Researchers using these datasets can now focus on pretraining pipelines and optimizations, while teams using those models for fine-tuning or inference can have a clearer way to explain outputs based on the composition of the dataset. The battle between open and closed generative AI is not just about models anymore.

🔎 ML Research

Extracting Concepts from GPT-4

OpenAI published a paper proposing an interpretability technique to understanding neural activity within LLMs. Specifically, the method uses k-sparse autoencoders to control sparsity which leads to more interpretable models —> Read more.

Transformer are SSMs

Researchers from Princeton University and Carnegie Mellon University published a paper outlining theoretical connections between transformers and SSMs. The paper also proposes a framework called state space duality and a new architecture called Mamba-2 which improves the performance over its predecessors by 2-8x —> Read more.

Believe or Not Believe LLMs

Google DeepMind published a paper proposing a technique to quantify uncertainty in LLM responses. The paper explores different sources of uncertainty such as lack of knowledge and randomness in order to quantify the reliability of an LLM output —> Read more.

CodecLM

Google Research published a paper introducing CodecLM, a framework for using synthetic data for LLM alignment in downstream tasks. CodecLM leverages LLMs like Gemini to encode seed intrstructions into the metadata and then decodes it into synthetic intstructions —> Read more.

TinyAgent

Researchers from UC Berkeley published a detailed blog post about TinyAgent, a function calling tuning method for small language models. TinyAgent aims to enable function calling LLMs that can run on mobile or IoT devices —> Read more.

Parrot

Researchers from Shanghai Jiao Tong University and Microsoft Research published a paper introducing Parrot, a framework for correlating multiple LLM requests. Parrot uses the concept of a Semantic Variable to annotate input/output variables in LLMs to enable the creation of a data pipeline with LLMs —> Read more.

🤖 Cool AI Tech Releases

FineWeb

HuggingFace open sourced FineWeb, a 15 trillion token dataset for LLM training —> Read more.

Stable Audion Open

Stability AI open source Stable Audio Open, its new generative audio model —> Read more.

Mistral Fine-Tune

Mistral open sourced mistral-finetune SDK and services for fine-tuning models programmatically —> Read more.

Zyda

Zyphra Technologies open sourced Zyda, a 1.3 trillion token dataset that powers the version of its Zamba models ��> Read more.

🛠 Real World AI

Salesforce discusses their use of Amazon SageMaker in their Einstein platform —> Read more.

📡AI Radar

Cisco announced a $1B AI investment fund with some major positions in companies like Cohere, Mistral and Scale AI.

Cloudera acquired AI startup Verta.

Databricks acquired data management company Tabular.

Tektonic, raised $10 million to build generative agents for business operations —> Read more.

AI task management startup Hoop raised $5 million.

Galileo announced Luna, a family of evaluation foundation models.

Browserbase raised $6.5 million for its LLM browser-based automation platform.

AI artwork platform Exactly.ai raised $4.3 million.

Sirion acquired AI document management platform Eigen Technologies.

Asana added AI teammates to complement task management capabilities.

Eyebot raised $6 million for its AI-powered vision exams.

AI code base platform Greptile raised a $4 million seed round.

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

[happsalesindia]

Automate Your Sales Process with CRM: A Comprehensive Guide

In the fast-paced world of sales, efficiency is key. Sales automation Customer Relationship Management (CRM) systems are powerful tools that can streamline your sales process, improve productivity, and boost revenue. In this guide, we’ll explore what sales automation CRM is, its benefits, and how you can implement it in your business.

What is Sales Automation CRM?

Sales automation CRM is a software solution that helps businesses automate their sales processes, from lead generation to closing deals. It combines customer relationship management (CRM) with automation tools to streamline tasks such as lead scoring, email marketing, and pipeline management. By automating these repetitive tasks, sales teams can focus more on building relationships with prospects and closing deals.

Benefits of Sales Automation CRM

Improved Efficiency: Sales automation CRM eliminates manual tasks, such as data entry and lead tracking, saving your team valuable time and allowing them to focus on selling.

Better Lead Management: With sales automation CRM, you can easily track and prioritize leads based on their likelihood to convert, ensuring that your team is focusing on the most promising opportunities.

Increased Sales Productivity: By automating repetitive tasks, your sales team can be more productive, allowing them to handle more leads and close more deals.

Enhanced Customer Relationships: Sales automation CRM allows you to personalize your interactions with customers, leading to stronger relationships and increased customer loyalty.

Improved Sales Forecasting: By tracking sales data and performance metrics, sales automation CRM can help you make more accurate sales forecasts, allowing you to better plan for the future.

Implementing Sales Automation CRM

Identify Your Needs: Before implementing a sales automation CRM, identify your specific needs and goals. Determine which processes you want to automate and what features are essential for your business.

Choose the Right CRM: There are many sales automation CRM systems available, so it’s essential to choose one that aligns with your business requirements. Look for a CRM that offers the features you need, such as lead management, email automation, and reporting tools.

Integrate with Existing Systems: If you already use other software systems, such as marketing automation or customer support software, ensure that your sales automation CRM can integrate with them seamlessly. This will help you streamline your processes and improve efficiency.

Train Your Team: Once you’ve chosen a sales automation CRM, provide training to your team to ensure they understand how to use the new system effectively. This will help them adapt to the changes quickly and maximize the benefits of the CRM.

Monitor and Optimize: Regularly monitor your sales automation CRM’s performance and gather feedback from your team. Use this information to optimize your processes and make improvements where necessary.

Conclusion

Sales automation CRM is a powerful tool that can help businesses streamline their sales processes, improve efficiency, and boost revenue. By automating repetitive tasks, such as lead tracking and email marketing, sales teams can focus more on building relationships with prospects and closing deals. Implementing a sales automation CRM requires careful planning and consideration, but the benefits are well worth the effort. With the right CRM in place, your sales team can achieve new levels of success and drive your business forward.

Original Source: Sales Productivity

[123albert]

This blog showcase data pipeline automation and how it helps to boost your business to achieve its business goals.

[automationqa]

Elevate your CI pipelines with Playwright integration!

Learn how to streamline testing processes and ensure seamless performance with this powerful tool.

[vividverses]

Exciting developments in MLOps await in 2024! 🚀 DevOps-MLOps integration, AutoML acceleration, Edge Computing rise – shaping a dynamic future. Stay ahead of the curve! #MLOps #TechTrends2024 🤖✨

[garymdm]

DataOps: From Data to Decision-Making

In today’s complex data landscapes, where data flows ceaselessly from various sources, the ability to harness this data and turn it into actionable insights is a defining factor for many organization’s success. With companies generating over 50 times more data than they were just five years ago, adapting to this data deluge has become a strategic imperative. Enter DataOps, a transformative…