#outsource TensorFlow engineers | Explore Tumblr posts and blogs

prosperasoft · 10 days ago

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Hire TensorFlow & PyTorch Engineers for AI Projects

Unlock the potential of AI by hiring top-tier machine learning engineers skilled in TensorFlow and PyTorch. At ProsperaSoft, we offer offshore AI solutions tailored to your needs—delivering deep learning, NLP, computer vision, and real-time data processing at scale.

Our engineers specialize in:

Scalable AI Model Development

Custom Machine Learning Solutions

Advanced Data Processing Pipelines

Whether you're building intelligent recommendation engines or predictive analytics platforms, our team ensures efficient and high-performing AI model deployment across industries.

Why Choose Our ML Experts? With deep expertise in data science, predictive modeling, and AI optimization, our machine learning engineers deliver precise, scalable, and production-ready solutions using TensorFlow & PyTorch.

Outsource your AI development today with confidence—partner with ProsperaSoft for cutting-edge innovation and competitive advantage.

#outsource TensorFlow engineers #hire PyTorch developers #offshore ML engineers

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ioweb3tech · 1 day ago

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AI Product Development: Building the Smart Solutions of Tomorrow

Artificial Intelligence (AI) is no longer a futuristic idea — it’s here, transforming how businesses operate, how users interact with products, and how industries deliver value. From automating workflows to enabling predictive insights, AI product development is now a cornerstone of modern digital innovation.

Companies across sectors are realizing that integrating AI into their digital offerings isn’t just a competitive advantage — it’s becoming a necessity. If you’re thinking about building intelligent products, this is the perfect time to act.

Let’s dive into what AI product development involves, why it matters, and how to approach it effectively.

What is AI Product Development?

AI product development is the process of designing, building, and scaling digital products powered by artificial intelligence. These products are capable of learning from data, adapting over time, and automating tasks that traditionally required human input.

Common examples include:

Personalized recommendation engines (e.g., Netflix, Amazon)

Chatbots and virtual assistants

Predictive analytics platforms

AI-driven diagnostics in healthcare

Intelligent process automation in enterprise SaaS tools

The goal is to embed intelligence into the product’s core, making it smarter, more efficient, and more valuable to users.

Why Businesses are Investing in AI Products

Here’s why AI product development is surging across every industry:

Enhanced User Experience: AI can tailor interfaces, suggestions, and features to user behavior.

Increased Efficiency: Automating repetitive tasks saves time and reduces human error.

Better Decision-Making: Predictive analytics and insights help businesses make informed choices.

Cost Savings: AI can reduce the need for large manual teams over time.

Competitive Edge: Products that adapt and evolve with users outperform static alternatives.

Incorporating AI doesn’t just make your product better — it redefines what’s possible.

Key Steps in AI Product Development

Building an AI-driven product isn’t just about coding a machine learning model. It’s a structured, iterative process that includes:

1. Problem Identification

Every great AI product starts with a real-world problem. Whether it’s automating customer support or predicting user churn, the goal must be clearly defined.

2. Data Strategy

AI runs on data. That means collecting, cleaning, labeling, and organizing datasets is critical. Without quality data, even the best algorithms fail.

3. Model Design & Training

This step involves choosing the right algorithms (e.g., regression, classification, neural networks) and training them on historical data. The model must be evaluated for accuracy, fairness, and bias.

4. Product Integration

AI doesn’t operate in isolation. It needs to be integrated into a product in a way that’s intuitive and valuable for the user — whether it's real-time suggestions or behind-the-scenes automation.

5. Testing & Iteration

AI products must be constantly tested in real-world environments and retrained as new data comes in. This ensures they remain accurate and effective over time.

6. Scaling & Maintenance

Once proven, the model and infrastructure need to scale. This includes managing compute resources, optimizing APIs, and maintaining performance.

Who Should Build Your AI Product?

To succeed, businesses often partner with specialists. Whether you're building in-house or outsourcing, you’ll need to hire developers with experience in:

Machine learning (ML)

Natural Language Processing (NLP)

Data engineering

Cloud-based AI services (AWS, Azure, GCP)

Python, TensorFlow, PyTorch, and similar frameworks

But beyond technical expertise, your team must understand product thinking — how to align AI capabilities with user needs.

That’s why many companies turn to saas experts who can combine AI with a product-led growth mindset. Especially in SaaS platforms, AI adds massive value through automation, personalization, and customer insights.

AI + Web3: A New Frontier

If you’re at the edge of innovation, consider combining AI with decentralized technologies. A future-forward web3 development company can help you integrate AI into blockchain-based apps.

Some exciting AI + Web3 use cases include:

Decentralized autonomous organizations (DAOs) that evolve using AI logic

AI-driven NFT pricing or authentication

Smart contracts that learn and adapt based on on-chain behavior

Privacy-preserving machine learning using decentralized storage

This intersection offers businesses the ability to create trustless, intelligent systems — a true game-changer.

How AI Transforms SaaS Platforms

For SaaS companies, AI is not a feature — it’s becoming the foundation. Here’s how it changes the game:

Automated Customer Support: AI chatbots can resolve up to 80% of Tier 1 queries.

Churn Prediction: Identify at-risk users and re-engage them before it’s too late.

Dynamic Pricing: Adjust pricing based on usage, demand, or user profiles.

Smart Onboarding: AI can personalize tutorials and walkthroughs for each user.

Data-driven Feature Development: Understand what features users want before they ask.

If you’re already a SaaS provider or plan to become one, AI integration is the next logical step—and working with saas experts who understand AI workflows can dramatically speed up your go-to-market timeline.

Real-World Examples of AI Products

Grammarly: Uses NLP to improve writing suggestions.

Spotify: Combines AI and behavioral data for music recommendations.

Notion AI: Embeds generative AI for writing, summarizing, and planning.

Zendesk: Automates customer service with AI bots and smart routing.

These companies didn’t just adopt AI — they built it into the core value of their platforms.

Final Thoughts: Build Smarter, Not Just Faster

AI isn’t just a trend—it’s the future of software. Whether you're improving internal workflows or building customer-facing platforms, AI product development helps you create experiences that are smart, scalable, and user-first.

The success of your AI journey depends not just on technology but on strategy, talent, and execution. Whether you’re launching an AI-powered SaaS tool, a decentralized app, or a smart enterprise solution, now is the time to invest in intelligent innovation.Ready to build an AI-powered product that stands out in today’s crowded market? AI product development done right can give you that edge.

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webtechsblog · 1 day ago

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Powering Progress – Why an IT Solutions Company India Should Be Your Technology Partner

In today’s hyper‑connected world, agile technology is the backbone of every successful enterprise. From cloud migrations to cybersecurity fortresses, an IT Solutions Company India has become the go‑to partner for businesses of every size. India’s IT sector, now worth over USD 250 billion, delivers world‑class solutions at unmatched value, helping startups and Fortune 500 firms alike turn bold ideas into reality.

1 | A Legacy of Tech Excellence

The meteoric growth of the Indian IT industry traces back to the early 1990s when reform policies sparked global outsourcing. Three decades later, an IT Solutions Company India is no longer a mere offshore vendor but a full‑stack innovation hub. Indian engineers lead global code commits on GitHub, contribute to Kubernetes and TensorFlow, and spearhead R&D in AI, blockchain, and IoT.

2 | Comprehensive Service Portfolio

Your business can tap into an integrated bouquet of services without juggling multiple vendors:

Custom Software Development – Agile sprints, DevOps pipelines, and rigorous QA cycles ensure robust, scalable products.

Cloud & DevOps – Migrate legacy workloads to AWS, Azure, or GCP and automate deployments with Jenkins, Docker, and Kubernetes.

Cybersecurity & Compliance – SOC 2, ISO 27001, GDPR: an IT Solutions Company India hardens your defenses and meets global regulations.

Data Analytics & AI – Transform raw data into actionable insights using ML algorithms, predictive analytics, and BI dashboards.

Managed IT Services – 24×7 monitoring, incident response, and helpdesk support slash downtime and boost productivity.

3 | Why India Wins on the Global Stage

Talent Pool – Over four million skilled technologists graduate each year.

Cost Efficiency – Competitive rates without compromising quality.

Time‑Zone Advantage – Overlapping work windows enable real‑time collaboration with APAC, EMEA, and the Americas.

Innovation Culture – Government initiatives like “Digital India” and “Startup India” fuel continuous R&D.

Proven Track Record – Case studies show a 40‑60 % reduction in TCO after partnering with an IT Solutions Company India.

4 | Success Story Snapshot

A U.S. healthcare startup needed HIPAA‑compliant telemedicine software within six months. Partnering with an IT Solutions Company India, they:

Deployed a microservices architecture on AWS using Terraform

Integrated real‑time video via WebRTC with 99.9 % uptime

Achieved HIPAA compliance in the first audit cycle The result? A 3× increase in user adoption and Series B funding secured in record time.

5 | Engagement Models to Fit Every Need

Dedicated Development Team – Ideal for long‑term projects needing continuous innovation.

Fixed‑Scope, Fixed‑Price – Best for clearly defined deliverables and budgets.

Time & Material – Flexibility for evolving requirements and rapid pivots.

6 | Future‑Proofing Your Business

Technologies like edge AI, quantum computing, and 6G will reshape industries. By aligning with an IT Solutions Company India, you gain a strategic partner who anticipates disruptions and prototypes tomorrow’s solutions today.

7 | Call to Action

Ready to accelerate digital transformation? Choose an IT Solutions Company India that speaks the language of innovation, agility, and ROI. Schedule a free consultation and turn your tech vision into a competitive edge.

Plot No 9, Sarwauttam Complex, Manwakheda Road,Anand Vihar, Behind Vaishali Apartment, Sector 4, Hiran Magri, Udaipur, Udaipur, Rajasthan 313002

#it solutions #aspire techno solutions #company in india

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logicielsolutions-offshore · 1 month ago

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AI & Offshore Development: The New Frontier for Startups

This blog shows how AI development for startups combined with offshore development teams delivers expert-driven, cost-efficient, and around-the-clock productivity, outlining practical steps to harness global talent for building, launching, and scaling AI-powered MVPs with speed, agility, and confidence.

AI development for startups isn’t just a buzzword, it’s a catalyst for innovation and competitive advantage. When combined with offshore AI development, emerging companies can tap into global expertise, accelerate their product roadmaps, and stretch limited budgets further than ever before. This partnership between cutting-edge algorithms and nimble offshore development teams is truly the new frontier for startups aiming to leap ahead in their markets.

Why AI & Offshore Development Make Perfect Partners

1. Speed Meets Expertise

Building an in-house AI team can take months of recruiting, training, and expensive tooling. By choosing offshore software development partners who specialize in AI software development, startups instantly gain access to seasoned data scientists, ML engineers, and DevOps specialists. This means your AI outsourcing journey starts at full throttle—no slow ramp-up necessary.

2. Focus on Your Core Vision

Startups thrive on creativity and rapid iteration. Offloading infrastructure setup, model training pipelines, and maintenance to a dedicated offshore development team frees your founders and product leads to sharpen the user experience, define competitive differentiators, and cultivate customer relationships—while your remote AI developers handle the heavy lifting.

3. Flexible Engagement Models

Whether you need a small proof-of-concept or a full-blown AI-powered product, offshore partners offer modular services: from data preparation and prototyping to end-to-end MVP product development using AI. This flexible approach means you pay for exactly what you need, when you need it—ideal for cash-conscious startups.

Key Benefits for Startups

1. Access to Specialized AI Solutions

Offshore teams are often immersed in the latest frameworks—TensorFlow, PyTorch, Hugging Face—and have built solutions across natural language processing, computer vision, recommendation engines, and more. By tapping into this breadth of experience, you accelerate your time-to-market and minimize trial-and-error.

2. Around-the-Clock Progress

With teams distributed across time zones, development truly never stops. While your US-based team sleeps, your remote AI developers are refining models, cleaning data, and running experiments—delivering fresh insights by the time you log on each morning.

3. Cost-Effective Scalability

Scaling AI workloads, be it additional model training, deployment pipelines, or production monitoring can be unpredictable. Offshore AI development allows you to flex your team size up or down in response to project needs, converting fixed payroll expenses into variable project fees and preserving precious runway.

How to Leverage Offshore AI Development

1. Define Clear Objectives

Pinpoint the specific AI use case—chatbot, predictive analytics, image recognition—and outline success metrics before engaging an offshore partner. This clarity ensures focused development and faster iterations.

2. Choose the Right Engagement Model

Select from hourly, fixed-price sprints, or dedicated teams based on your project scope. Early-stage startups often favor rapid prototyping packages, while scaling ventures may opt for long-term retainers.

3. Establish Robust Collaboration

Integrate your offshore team into daily stand-ups, product backlog sessions, and demo days. Leverage shared tools (e.g., Slack, Jira, GitHub) to maintain transparency and keep everyone aligned on startup tech trends and priorities.

4. Prioritize Data Governance

AI thrives on data quality. Work with your offshore partner to define data pipelines, privacy controls, and versioning systems that uphold user trust and regulatory compliance.

Best Practices from Logiciel Solutions

At Logiciel Solutions, we’ve guided startups through dozens of successful AI outsourcing engagements. Our best practices include:

Rapid Proof-of-Concepts: Deliver basic models in weeks to validate core hypotheses before committing to full development.

Iterative Model Refinement: Blend developer sprints with user feedback loops to sharpen AI outputs and reduce bias.

Seamless Handoffs: Provide comprehensive documentation, retraining guides, and deployment scripts so your in-house team can smoothly take over.

Dedicated AI Centers of Excellence: Assign specialized squads—data engineers, ML architects, MLOps experts—to ensure end-to-end quality.

These methodologies not only speed up minimum viable product development but also build a solid foundation for future feature expansions.

Conclusion

Bringing AI development for startups together with our proven offshore development teams, Logiciel Solutions offers a truly differentiated path to market. Our deep expertise in AI software development means we don’t just write code—we architect intelligent systems that learn, adapt, and scale as your business grows.

Over the past decade, we’ve built a dedicated AI Center of Excellence that blends data science, machine learning engineering, and DevOps best practices. From rapid proof-of-concepts to fully managed MVP product development, we streamline every stage of the AI lifecycle:

AI-Driven Discovery & Strategy: We work side-by-side with your team to identify the highest-impact use cases and define measurable success metrics.

Model Design & Development: Our offshore experts leverage the latest frameworks and libraries—TensorFlow, PyTorch, Hugging Face—to build robust, production-ready models.

End-to-End MLOps: With automated pipelines for data ingestion, training, validation, and deployment, we ensure your AI features roll out smoothly and securely.

Continuous Intelligence & Optimization: Post-launch, we monitor performance, tune algorithms, and deliver incremental improvements so your product stays ahead of market demands.

By choosing Logiciel Solutions as your AI outsourcing partner, you benefit from cost-effective scalability, around-the-clock development cycles, and a singular focus on turning your AI ambitions into market-winning products. Let’s harness the power of offshore AI development together, reach out today, and let’s start building the intelligent solutions that will define tomorrow’s startups.

offshore company, offshore software company, offshore software development

#artificial intelligence #software engineering #programming #startup

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avion-technology · 2 months ago

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Beyond Coding: How We Turn Python Into a Business Growth Engine

🔍 Introduction: Python Isn’t Just Code — It’s a Growth Strategy

Python is everywhere — from AI to automation to web apps. But at Avion Technology, a leading Python development company in Chicago, USA, we see Python differently.

For us, it’s more than a programming language. It’s a business growth engine — a way to build faster, automate smarter, and scale seamlessly.

Whether you're a startup founder or a CTO of a growing enterprise, our tailored Python solutions are designed to deliver real ROI — not just working code.

⚙️ Turning Python Into a Business Growth Engine

Here’s how we go beyond basic development — and use Python to drive measurable business outcomes:

🚀 1. Intelligent Automation That Saves Time and Money

Manual processes are productivity killers. We use Python to:

Automate repetitive admin tasks

Sync CRMs and databases

Auto-generate reports and analytics

Eliminate human error from critical workflows

Our Python-powered automation solutions help clients streamline operations and focus on scaling their core business.

📊 2. Custom Data Pipelines & Predictive Insights

Python makes raw data useful. With libraries like Pandas, NumPy, and Scikit-learn, we:

Build predictive models

Create custom data dashboards

Automate marketing decisions

Analyze user behavior in real-time

This isn’t just development — it’s data-driven growth.

🌐 3. Fast, Scalable Web Applications with Python Frameworks

Whether it’s an MVP or a full-scale SaaS product, we build:

High-performance backends using Django or FastAPI

Modular architecture for long-term scaling

RESTful APIs that integrate with your existing stack

AI-enhanced features (recommendations, chatbots, NLP)

And as a Python development company in Chicago, USA, we ensure our solutions are compliant, user-focused, and built for scale.

💼 Why Avion Technology?

Here’s what makes us the go-to Python team in Chicago:

✅ 15+ years of development excellence ✅ Deep expertise in Django, Flask, FastAPI, TensorFlow ✅ Fully in-house team — no outsourcing ✅ Agile delivery, transparent process ✅ Based in Chicago, USA — with nationwide clients

We don’t just write Python. We architect growth-ready solutions.

📣 Ready to Build Smarter with Python?

If you’re looking for a Python development company in Chicago, USA, that understands both code and business — we’re ready to help.

📩 Let’s Talk — Your First Consultation is Free 📞 Call Us: +1 (847) 265-4073

#PythonDevelopment #PythonForBusiness #ChicagoTech #BusinessAutomation #DigitalTransformation #WebAppDevelopment #AIWithPython #DataDrivenDecisions #CustomSoftwareDevelopment #TechForBusiness #ScalableTech #AvionTech #Technology

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macgenceaiml · 4 months ago

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The Best Labelbox Alternatives for Data Labeling in 2025

Whether you're training machine learning models, building AI applications, or working on computer vision projects, effective data labeling is critical for success. Labelbox has been a go-to platform for enterprises and teams looking to manage their data labeling workflows efficiently. However, it may not suit everyone’s needs due to high pricing, lack of certain features, or compatibility issues with specific use cases.

If you're exploring alternatives to Labelbox, you're in the right place. This blog dives into the top Labelbox alternatives, highlights the key features to consider when choosing a data labeling platform, and provides insights into which option might work best for your unique requirements.

What Makes a Good Data Labeling Platform?

Before we explore alternatives, let's break down the features that define a reliable data labeling solution. The right platform should help optimize your labeling workflow, save time, and ensure precision in annotations. Here are a few key features you should keep in mind:

Scalability: Can the platform handle the size and complexity of your dataset, whether you're labeling a few hundred samples or millions of images?

Collaboration Tools: Does it offer features that improve collaboration among team members, such as user roles, permissions, or integration options?

Annotation Capabilities: Look for robust annotation tools that support bounding boxes, polygons, keypoints, and semantic segmentation for different data types.

AI-Assisted Labeling: Platforms with auto-labeling capabilities powered by AI can significantly speed up the labeling process while maintaining accuracy.

Integration Flexibility: Can the platform seamlessly integrate with your existing workflows, such as TensorFlow, PyTorch, or custom ML pipelines?

Affordability: Pricing should align with your budget while delivering a strong return on investment.

With these considerations in mind, let's explore the best alternatives to Labelbox, including their strengths and weaknesses.

Top Labelbox Alternatives

1. Macgence

Strengths:

Offers a highly customizable end-to-end solution that caters to specific workflows for data scientists and machine learning engineers.

AI-powered auto-labeling to accelerate labeling tasks.

Proven expertise in handling diverse data types, including images, text, and video annotations.

Seamless integration with popular machine learning frameworks like TensorFlow and PyTorch.

Known for its attention to data security and adherence to compliance standards.

Weaknesses:

May require time for onboarding due to its vast range of features.

Limited online community documentation compared to Labelbox.

Ideal for:

Organizations that value flexibility in their workflows and need an AI-driven platform to handle large-scale, complex datasets efficiently.

2. Supervisely

Strengths:

Strong collaboration tools, making it easy to assign tasks and monitor progress across teams.

Extensive support for complex computer vision projects, including 3D annotation.

A free plan that’s feature-rich enough for small-scale projects.

Intuitive user interface with drag-and-drop functionality for ease of use.

Weaknesses:

Limited scalability for larger datasets unless opting for the higher-tier plans.

Auto-labeling tools are slightly less advanced compared to other platforms.

Ideal for:

Startups and research teams looking for a low-cost option with modern annotation tools and collaboration features.

3. Amazon SageMaker Ground Truth

Strengths:

Fully managed service by AWS, allowing seamless integration with Amazon's cloud ecosystem.

Uses machine learning to create accurate annotations with less manual effort.

Pay-as-you-go pricing, making it cost-effective for teams already on AWS.

Access to a large workforce for outsourcing labeling tasks.

Weaknesses:

Requires expertise in AWS to set up and configure workflows.

Limited to AWS ecosystem, which might pose constraints for non-AWS users.

Ideal for:

Teams deeply embedded in the AWS ecosystem that want an AI-powered labeling workflow with access to a scalable workforce.

4. Appen

Strengths:

Combines advanced annotation tools with a global workforce for large-scale projects.

Offers unmatched accuracy and quality assurance with human-in-the-loop workflows.

Highly customizable solutions tailored to specific enterprise needs.

Weaknesses:

Can be expensive, particularly for smaller organizations or individual users.

Requires external support for integration into custom workflows.

Ideal for:

Enterprises with complex projects that require high accuracy and precision in data labeling.

Use Case Scenarios: Which Platform Fits Best?

For startups with smaller budgets and less complex projects, Supervisely offers an affordable and intuitive entry point.

For enterprises requiring precise accuracy on large-scale datasets, Appen delivers unmatched quality at a premium.

If you're heavily integrated with AWS, SageMaker Ground Truth is a practical, cost-effective choice for your labeling needs.

For tailored workflows and cutting-edge AI-powered tools, Macgence stands out as the most flexible platform for diverse projects.

Finding the Best Labelbox Alternative for Your Needs

Choosing the right data labeling platform depends on your project size, budget, and technical requirements. Start by evaluating your specific use cases—whether you prioritize cost efficiency, advanced AI tools, or integration capabilities.

For those who require a customizable and AI-driven data labeling solution, Macgence emerges as a strong contender to Labelbox, delivering robust capabilities with high scalability. No matter which platform you choose, investing in the right tools will empower your team and set the foundation for successful machine learning outcomes.

Source: - https://technologyzon.com/blogs/436/The-Best-Labelbox-Alternatives-for-Data-Labeling-in-2025

#Labelbox Alternatives #Alternatives to labelbox

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t-baba · 6 years ago

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V8 v8, the State of JS survey results, and CDNJS lives

#468 — December 20, 2019

Read on the Web

If you're subscribed to any of our other newsletters, you'll have seen we're doing 2019 roundups this week.. but not in JavaScript Weekly! :-) Our 2019 JavaScript roundup will be here on January 3, but today is a normal issue. Happy holidays!

JavaScript Weekly

Tesseract.js 2.0: Pure JavaScript OCR for 100 Languages — A pure JavaScript port of the popular C++ Tesseract library commonly used for visual text recognition purposes by other systems. The homepage has a neat demo where you can drop on images of your own and see how they get processed. v2.0 is now out and here's why the creator has been working on it.

Jerome Wu

V8 Release v8.0 — Yes, that’s v8 of v8 – not confusing at all 😂 Nonetheless, it’s a key step forward for the most widely deployed JavaScript engine, and introduces optional chaining, nullish coalescing, and some significant performance improvements. It’ll be landing in a Chrome and Node near you soon.

Leszek Swirski

Learn the Full Stack with Jem Young, Senior Software Engineer at Netflix — Become a more well rounded engineer by understanding what is happening on the server-side.

Frontend Masters sponsor

Results from the 'State of JavaScript' 2019 Survey — While the methodology is far from perfect (for example, Angular and Angular.js seem to get lumped together as the same thing), this is nonetheless the biggest JavaScript-specific survey. There’s a lot to dig through, so you may prefer The Changelog's 7 insights from the State of JS 2019 roundup post that looks at the most striking results.

Sacha Greif, Raphaël Benitte and Amelia Wattenberger

An Update on CDNJS — CDNJS is a popular content distribution network for JavaScript files but just a couple of months ago its continued development was in doubt. In this significant update we learn more about how CDNJS works and how Cloudflare want to take it forward (and they need our help).

Zack Bloom

Mastering console.log Like A Pro — This isn’t exactly new ground, but if you’re a heavy console.log user, this article deftly covers a variety of extra console methods to keep on your radar.

Harsh Makadia

⚡️ Quick Releases

Node.js 13.5.0

Material UI 4.8 — Popular React components system.

Relay 8.0 — GraphQL client and integration for React apps.

sql.js 1.1 — SQLite, but compiled to JavaScript.

Cash 5.0 — jQuery-style DOM manipulation for modern browsers.

💻 Jobs

Backend Engineering Position 🤘 in Beautiful Norway 🎉 — Passion for JavaScript, GraphQL, Scalability and Performance? Want to move to Norway? Join our fast growing e-commerce service Crystallize.

Crystallize

Find a Job Through Vettery — Make a profile, name your salary, and connect with hiring managers from top employers. Vettery is completely free for job seekers.

Vettery

Technical Content Producer (Interim) at Ably (London, Remote OK) — Ably builds tools and cloud infrastructure for the realtime internet. They need a developer/tech writer on a temporary basis to coordinate, outsource, and review the creation of technical content for a developer audience.

Ably

📘 Articles & Tutorials

What's Coming in Angular 9.0.0 and Ivy Improvements — Angular 9.0 was due to be released this year but is being held back until next year to give the team a break. Nonetheless, it’s going to pack in some key improvements.

Mike Hartington

An Introduction to Controlling the Raspberry Pi 4's GPIO Pins from Node — If you’ve got a Raspberry Pi sat around (I have a few!) and you’re looking to have a play over the holiday season, you could start here.

Uday Hiwarale

Migrating a Distributed System from JavaScript to TypeScript — Learn how to migrate a globally-distributed system written in JavaScript over to TypeScript.

Ably sponsor

Understanding Decorators in JavaScript — Decorators are a first-class concept in some languages that can be used to quickly modify functions or classes by way of a simple directive. The idea is still merely a proposal for JavaScript, but it’s possible to adapt some of the ideas now with Babel.

Mike Green

A Case for Using void in Modern JavaScript? — Some interesting points here. I doubt they’ll take off, but I like the examples.

slikts

Writing JavaScript With Only Six Characters — A fun article like this tends to do the rounds each year and it never ceases to delight me at how quirky JavaScript can be.

Erik Wendel

Const Assertions in Literal Expressions in TypeScript

Marius Schulz

What’s New in Preact X? — Preact is a lightweight React alternative with React API compatibility.

Ogundipe Samuel

Sentiment Analysis of Your Year with TensorFlow.js — Perform sentiment analysis with TensorFlow in JavaScript to determine the positivity of text messages received via Twilio.

Lizzie Siegle

BDD vs Executable Specifications

Gauge sponsor

▶ The Design Principles of Vue 3.0 — A 50 minute talk from the creator of Vue.js, Evan You, on the principles behind the changes coming in Vue 3.0.

Evan You

10 Useful Angular Features You’ve Probably Never Used — Assuming you actually use Angular in the first place, naturally.

Chidume Nnamdi

Why Svelte Won’t 'Kill' React

Kit Isaev

🔧 Code & Tools

A-Frame 1.0 Released: Framework for Building VR Experiences — A-Frame handles the 3D and WebVR boilerplate required to get running across numerous platforms quickly. Version 1 boasts full WebXR support.

A-Frame Team

Introducing Scully: The Angular Static Site Generator — The Angular community now has their very own static site generator.

Netlify

Polly.js 3.0: Record, Replay, and Stub HTTP Interactions — A library from Netflix for recording, replaying and stubbing HTTP interactions via native browser APIs. GitHub repo.

Netflix

▶ From NodeConf EU: What's Being Built and Where Node.js Is Heading

Heroku sponsor

Sarus: A Client-Side Library for WebSockets — Helps you handle situations where connections unexpectedly close.

Anephenix

Alpine.js: A Minimal Framework for Composing Behavior in Your Markup — “Think of it like Tailwind for JavaScript.”

Alpine.js

vue2-datepicker: A Date / DateTime Picker Component for Vue

xiemengxiong

If you're celebrating, we hope you have a happy holiday season and we'll see you in the new year on January 3, 2020.

by via JavaScript Weekly https://ift.tt/2Z90iZH

#Javascript Branding #Javascript Weekly

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softwareengineeringleader · 6 years ago

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"One of the most common hurdles with developing AI and deep learning models is to design data pipelines that can operate at scale and in real-time. Data scientists and engineers are often expected to learn, develop and maintain the infrastructure for their experiments, but the process takes time away from focussing on training and developing the models. But what if you could outsource all of the non-data science to someone else while still retaining control? In this article, you will explore how you can leverage Kubernetes, Tensorflow and Kubeflow to scale your models without having to worry about scaling the infrastructure". Reblog with caption 🙃

#kubernetes

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bharathshan · 4 years ago

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Study R Programming Language For Data Science

The Data Science Course in Mumbai is also through online pre-recorded movies and a monthly live class with a University professor. Data mining is a process used by firms to show raw data into helpful information. By utilizing software to look for patterns in massive batches of information, businesses can study more about their prospects and develop simpler advertising methods, in addition, to increase gross sales and reduce prices. Data mining depends on effective data collection and warehousing as well as pc processing. Diploma in Data Science is a Full Job-oriented training and course giving maximum emphasis on practical arms-on expertise. This might be a classroom training with skilled trainers from trade having more than 12 years of expertise within the related field. The course contents are at par with the curriculum followed in high universities and IIMS.

This Data Science Course also has a fantastic mix of a comprehensive curriculum, presents really interactive learning expertise via Live Classes by main professors and consultants, and palms-on business projects. Finding a job and planning a career both require a solid instructional foundation. The CDS program is a 6 months weekend program, with a prime concentrate on the ability wants of the three major profiles particularly machine studying engineer, data analyst, and entry-degree requirements of the information engineer profile. The classroom is going to be certainly one of its sort that you’ve got by no means experienced earlier than, with actual life case research and group actions.

The project goals to provide the ideas of associates to the social media end-person based on its research, activity, and different info like curiosity. It additionally tries to cut back the fraud accounts by analyzing the data. ExcelR is a digital marketing agency based in Mumbai, India. We present web optimization and digital marketing outsourcing to digital advertising businesses globally. IIDE offers an MBA-level submit-graduate program in Data Science. You can discover the course charges of a few of the institutes talked about under.

Mentors and Career workers would maintain your hand and assist you to ace any interview whereas imparting the experiential information. They make sure that you be taught the latest in the tech subject and apply it too. The skilled environment also assists in developing your persona. we have got an educational program devoted to SMO that goals to maximize the presence of a complete on the social media platforms.

The Data Science Masters Program will flip you into a Machine Learning Pro. Complete with hackathons and guided initiatives, this program gives you an agency grounding in Machine Learning — from fundamentals to purposes.

TensorFlow is a free and open-supply software library for dataflow and differentiable programming throughout a variety of tasks. It is a symbolic math library, and can also be used for machine studying functions similar to neural networks. ExcelR Solutions is one among the many institutes of Data Science Course in Mumbai and 5% of the highest business faculties worldwide have been accredited with the illustrious Association to Advance Collegiate Schools of Business accreditation. It offers some interesting courses in the analytics and data science area.

For More Details Contact Us ExcelR- Data Science, Data Analytics, Business Analytics Course Training Andheri Address: 301, Third Floor, Shree Padmini Building, Sanpada, Society, Teli Galli Cross Rd, above Star Health and Allied Insurance, Andheri East, Mumbai, Maharashtra 400069 Phone: 09108238354 Hours: Sunday — Saturday 7 AM — 10 PM

Data Science Course in Mumbai

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outsource02-blog · 5 years ago

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Outsource Ecommerce Software

New Relic

The New Relic Ruby agent monitors your applications to help you identify and solve performance issues. You can also extend the agent’s performance monitoring to collect and analyze business data to help you improve the customer experience and make data-driven business decisions.

The Ruby agent supports many of the most common Ruby frameworks and platforms. You can also use the Ruby agent in a Google App Engine (GAE) flexible environment.

Ruby custom instrumentation

The New Relic Ruby agent automatically collects many metrics. It also includes an API you can use to collect additional metrics about your application. If you see large Application Code segments in transaction trace details, custom instrumentation can give a more complete picture of what is going on in your application.

Method tracers

The easiest way to capture custom instrumentation is by tracing calls to a particular method. Tracing a method as described below will insert an additional node in your transaction traces for each invocation of that method, providing greater detail about where time is going in your transactions.

Method tracers are software probes you can put on a method of any class. The probes use alias method chaining to insert themselves when the target methods execute and gather custom instrumentation on their performance.

Tracing initializers

For Rails, a common way to add instrumentation is to create an initializer and “monkey patch” the instrumentation directives. For example, to add a method tracer to MyCache#get:

Make sure the Cache class is loaded before adding the method tracer.

Add the following in a file named config/initializers/rpm_instrumentation.rb:

Tracing blocks of code:

Sometimes a single method is so complex that tracking overall time doesn’t give enough detail. In these cases, you can wrap a block of code with a tracer. Call trace_execution_scoped passing the code to trace

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• Alert your team when errors or problems occur before they affect your users.

• Track performance after a deployment.

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theinvinciblenoob · 7 years ago

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Building web services and smartphone apps, which is most of what I’ve been doing professionally at HappyFunCorp1 for the last decade or so used to be pretty straightforward. Not easy, but straightforward, especially when the client was a consumer startup, which so many of them were.

The more we did the better we got at it. Design and write two native apps, usually iOS first and Android second. Don’t skimp on the design. Connect them to a JSON API, usually written in Ruby on Rails, which also powered the web site. There’s always a web site; consumers might only see the side which is a minimal billboard for the app, but there’s essentially always also an admin site, to control features and aspects of the app.

Design isn’t as important for the admin site, so you can build that in something crude but effective like ActiveAdmin; why roll your own? Similarly, authentication is tricky and easy to get wrong, so use something like Devise, which comes with built-in hooks to Facebook and Twitter login. Design your database carefully. Use jQuery for dynamic in-browser manipulation since raw Javascript is such a nightmare. Argue about whether to use Rspec or Minitest for your server tests.

All there? OK, roll it out to your Heroku scaling environment, so you can simply “git push” to push to staging and production, with various levels of Postgres support, autoscaling, pipelines, Redis caching, Resque worker jobs, and so forth. If it’s a startup, keep them on Heroku to see if they catch on, if they find the fabled product-market fit, not least because it helps you iterate faster. If so, at some point you have to graduate them to AWS, because Heroku only scales so far and it does so very expensively. If not, well, “fail fast,” right?

Those were the days, my friends, those halcyon, long-gone days of (checks notes) five years ago. The days of a lot of grief, sure, but very little decision complexity. The smartphone boom was on, and the web boom was settling down, and everyone was still surfing those two tidal waves.

Today? Well, today we still are, neither of those waves have broken, per se, software is still eating the world, but things are … different. More of the world is being eaten, but it’s also happening more slowly, like growing 50% a year from a $1 billion base rather than 500% from $1 million. There are fewer starry-eyed founders with an app idea that they’re sure will change the world and funding enough to give it a shot. Those are still out there, sure, and more power to them, but the landscape is more complex, now.

Instead we see more big businesses, media and industrial and retail alike, realizing they must adapt and be devoured, experimenting with new tech projects with a combination of excitement and trepidation. Or requisitioning custom apps for very specific — but very useful — purposes, and requiring them to interface with their awkward pre-existing custom middleware just so. Or tech companies, even big household-name ones, outsourcing ancillary tools and projects in order to focus their in-house teams purely on their core competencies and business models. Our mix of clients has definitely shifted more towards enterprise in the last few years.

Which is not to say that startups don’t still come through our doors with bright ideas and inspiring PowerPoints on a fairly regular basis. As do super starry-eyed blockchain founders (granted, I’m sometimes a bit starry-eyed about blockchains myself) replacing the consumer-app founders of yore. I doubt we’re alone in having had a spate of blockchain startup projects late last year and early this, which has diminished to only a couple active at the moment. (Not least because the tooling is still so crude it reminds me of 90s command-line hacking.) But I strongly doubt that sphere is going away.

We haven’t dealt with as many AI projects as I would have expected by now, probably partly because AI talent is still so scarce and highly valued, and partly because it turns out a lot of seeming “AI” work can be done with simple linear regressions rather than by building and training and tuning deep-learning neural networks… although if you do those linear regressions with TensorFlow, it’s still “AI” buzzword-compliant, right? Right?

Most of all, though, the tools we use have changed. Nowadays when you want to build an app, you have to ask yourself: really native? (Java or Kotlin? Objective-C or Swift?) Or React Native? Or Xamarin? Or Google’s new Flutter thing? When you want to build a web site, you have to think: traditional? Or single-page, with React or Angular or Vue? As for the server — Go is a lot faster than Rails, you know, and oh, that elegant concurrency handling, but, oh, where is my map/filter/reduce? Javascript is still a clumsy language, but there are certain advantages to having one language across the stack, and Node is powerful and package-rich these days. And of course you’ll want it all containerized, because while Docker definitely adds another layer or two of configuration complexity, it’s usually worth it.

Unless you want to go fully “serverless,” at least for aspects, with Amazon Lambda or Google Firebase? Even if you don’t use Firebase for a datastore, how about for authentication, huh? And if you’re all containerized, and Kubernetized if/as appropriate, though maybe let’s not go the many-microservices route until you’re sure your product-market fit justifies it, then where do you want to roll it out, AWS or Azure or Google Cloud or Digital Ocean? Or do you want to use one of their PaaS services, like App Engine or Beanstalk, which, like Heroku, sorta kinda live between “serverless” and “bare metal virtual machines”?

I oversimplify, but you get my point. We’ve never had more options, as developers, more tools available to us … and we’ve never had to struggle more with analysis paralysis, because it’s awfully hard to determine which of the possible toolsets is the best one for any particular situation. Sometimes — often — we have to be happy with just selecting a good one. And that selection problem doesn’t look like it’s going to get easier anytime soon, I’m afraid. It’s a strange time to be a coder. We live and work all tangled up in an embarrassment of riches.

1Yes, that’s really our name. No, this TC column isn’t a full-time gig. (Which is something people frequently assume, because it’s so much more visible and to some people writing a column every week sounds like a lot of work, but no, I’m really a CTO.)

via TechCrunch

#IFTTT #TechCrunch

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fmservers · 7 years ago

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The tools, they are a-changing