Pickles — Handcrafted Indian Pickles

@vishal1145

Your biggest risk is data drift between AX exports and S3 ingestion — if schema changes go undetected, your ML models will train on corrupted features and produce garbage predictions. This will waste compute spend and erode trust in the insights layer. Quick questions - are your AX exports arriving as flat CSVs or structured JSON, and do you have a preferred retraining cadence (daily, weekly, on-demand)? Here is the architectural approach: - AWS GLUE + LAMBDA: Build an event-driven pipeline that validates schema on S3 upload, applies transformations, and catalogs clean datasets in Glue Data Catalog for version control. - SAGEMAKER AUTOPILOT: Train your first classification model using AutoML to establish baseline accuracy, then export the notebook so you can iterate feature engineering without vendor lock-in. - ATHENA + QUICKSIGHT: Query predictions directly from S3 using SQL and push results back into Power BI via API or scheduled exports, closing the feedback loop to your reporting layer. I've built similar ML pipelines for 2 manufacturing clients migrating from on-prem BI to AWS, reducing model deployment time from weeks to 48 hours. Let's schedule a 20-minute call to review your AX schema and confirm the most cost-efficient service stack before we start the build.

@jayantkumar314

As a Senior Software Engineer with over 5 years of experience, I have honed my skills on complex projects that require data manipulation and the deployment of AWS machine learning workflows - precisely what your project needs. My extensive knowledge in handling everything from raw data to actionable insights will ensure your pickles project thrives. Additionally, I have had 99 percent retention rate thanks to long-term relationships with clients, which means I won't leave you stranded with maintenance or extension after project completion. My list of AWS services is extensive including Glue, Redshift, Athena, SageMaker Studio and Lambda. In addition, my familiarity with Microsoft Power BI and Dynamics AX uniquely positions me to seamlessly integrate the existing raw data into AWS-based machine learning workflows. I am committed to using only what is necessary for the job at hand to keep costs manageable and ensuring everything is within the AWS ecosystem for top-notch efficiency. Outside of technical competence, my friendly and communicative nature will ensure an easy walkthrough at the end of the project. If you ever have questions about the source data or need recommendations on improving our workflow, I'm here. Let's harness our skills and build you a repeatable pipeline with actionable intelligence that will skyrocket your pickle venture!

@UzeeTech

Your AX and Power BI exports need a repeatable AWS data-to-model workflow. The first milestone is a small, explainable pipeline that lands clean data and proves one business prediction or classification end to end. I can map the export schema, define an S3 layout, add validation and basic cleansing, and keep runs reproducible with versioned configuration. Depending on volume, I’ll use S3 with Glue or Lambda for ingestion, Athena for inspection, and SageMaker for a documented training, evaluation, and inference example. I’ll include cost controls, IAM boundaries, error logging, and a plain-English run-book without placing credentials in source. I’ll validate against representative records and leave the architecture easy to extend. My relevant real work includes Jyotish AI on Android and web, backed by 9+ years of software development; I won’t claim an unlisted AWS case study. Which AX/Power BI export format and refresh schedule are available, and what target should the first model predict or classify? I estimate 7 days for the initial pipeline, model example, notes, and handover. My bid is ₹7,000, and I can begin with a schema and cost-aware architecture review.

@vipworks

Hi there, I can build a cost-effective, automated AWS pipeline to ingest your Dynamics AX and Power BI data exports, clean and store them in Amazon S3, and run an end-to-end Machine Learning model in Amazon SageMaker that feeds actionable predictions back to your reporting layer. Execution Roadmap 1. S3 Bucket Setup & Glue ETL Configuration 2. SageMaker Model Development & Evaluation 3. Power BI Connector & End-to-End Test

@monicasynthesis

Hello, I can do this work. I have 5+ years of experience in AWS, data engineering, and machine learning, with a strong focus on scalable and cost-efficient solutions. I will provide: • AWS pipeline to ingest AX/Power BI data into S3 with cleansing and data preparation • Efficient AWS architecture using Glue, Athena, Lambda, or other suitable services • End-to-end SageMaker model training, evaluation, and prediction workflow • Integration-ready outputs for your reporting layer • Clear run-book and final walkthrough for maintenance and future extensions I’ll focus on building a repeatable, practical solution while keeping the AWS architecture and ongoing costs reasonable. Warm Regards, Monica Bhatia

@AZTURK1

I can set up your complete AWS data pipeline from Dynamics AX exports to SageMaker and back to your reporting layer with minimal ongoing AWS overhead. ✅ Configured S3 ingestion bucket with AWS Lambda / Glue DataBrew to validate, clean, and catalog raw export data automatically. ✅ Built an end-to-end SageMaker training notebook and pipeline for your target prediction, saving inferenced outputs to S3/Athena for direct Power BI ingestion. ✅ Applied fine-grained IAM roles, S3 lifecycle tiers, and auto-terminating compute instances to eliminate idle infrastructure costs. ✅ Plain-English operational run-book covering daily data drops, model retraining triggers, and output verification. ✅ 10+ years production experience | 400+ completed projects | 320+ five-star reviews ✅ Microsoft® Certified: MCSA | MCSE | MCT & cPanel Certified (CWSA-2) What cadence and format are your Dynamics AX exports currently generated in, and what specific business outcome do you want the first SageMaker model to predict (e.g., SKU sales volume or reorder frequency)? I can deliver this in 1 day(s) for 11500 INR and can jump on it right now.

@arshig8

Hi, I have read your requirements very well and I’m really excited to work with you on turning your existing AX/Power BI data into a practical AWS ML workflow. I can build a repeatable pipeline using S3 + Glue/Athena + SageMaker, including data ingestion, cleansing, dataset registration, model training/evaluation, and an end-to-end prediction/classification example. I’ll also provide a simple run-book and walkthrough so you can maintain and extend the solution confidently after handover. A few questions: How are you currently exporting/receiving data from Dynamics AX and Power BI? Do you already have an AWS account/S3 environment available? Is there a specific business prediction or classification you want to demonstrate first? Let’s connect on a chat to discuss well. Waiting for your response. Best regards, Arshiya

@yashvendra79

Hello, We are BhartiyaCoders, a professional software development team helping startups, businesses, and entrepreneurs build modern, secure, and scalable digital solutions. We handle all types of development projects, including websites, web applications, mobile apps, Android/iOS applications, custom software, REST APIs, backend development, third-party API integrations, payment integrations, database solutions, admin panels, dashboards, automation, bug fixing, performance optimization, and existing project improvements. Whether you need a complete product from scratch, an MVP, new features, API integration, backend development, or improvements to an existing system, we can manage the complete process from planning and development to testing, deployment, and maintenance. We focus on quality, performance, security, scalability, clean development, responsive UI, and timely delivery, with clear communication throughout the project. Share your requirements and we’ll suggest the right approach for your project. Let’s build something reliable and successful together. Best Regards, BhartiyaCoders

@Tradeware

Hi, I can build your AWS-based data and machine-learning workflow from Dynamics AX/Power BI through to actionable predictions. I’ll create a repeatable pipeline using S3, Glue/Athena and SageMaker, covering data ingestion, cleansing, transformation, model training/evaluation and prediction outputs that can feed back into Power BI. I’ll keep the architecture scalable and cost-conscious. You’ll receive the complete AWS setup, one working ML use case, testing, and a clear run-book/walkthrough for future maintenance. Keywords: AWS, SageMaker, S3, AWS Glue, Athena, Machine Learning, Power BI, Dynamics AX, ETL, Data Pipeline, Predictive Analytics, Cloud Computing

@indreshpnk

Hello, I understand you need an AWS-based ML workflow that takes existing Power BI/Dynamics AX data into the cloud, prepares it efficiently, trains a business-relevant model, and makes the results available for your reporting layer. Here’s what I can provide: A repeatable ingestion pipeline to S3 with structured storage, data cleansing, validation, and organization for reliable downstream processing. An AWS ML workflow using SageMaker for feature preparation, model training, evaluation, and generating a prediction/classification that can be consumed by your reporting layer. Cost-conscious AWS architecture using services such as S3, Glue, Athena, Lambda, and SageMaker where appropriate, along with a concise runbook covering deployment, configuration, maintenance, and extension. I bring over 4+ years of experience in Python, AWS, data processing, APIs, databases, Docker, and full-stack development, with hands-on experience using AWS services including EC2, S3, and IAM. Just to clarify a few things: Can you share the Power BI/AX export format and a sample of the available fields? What business outcome or prediction would you like the initial SageMaker model to target? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@shailender18

Hello, I have 5+ years of experience in software development and data-driven application workflows, and I can help structure your AWS-based data and machine-learning pipeline around the existing AX/Power BI data. I’ll design a practical pipeline to land exported data into S3, apply cleansing and transformation, and make curated datasets available for downstream analytics. Based on the data volume and refresh needs, I can use services such as S3, Glue, Athena and Lambda while keeping the architecture simple and cost-conscious. For the ML workflow, I’ll build one complete SageMaker example covering dataset preparation, model training, evaluation and prediction/classification output. The solution will be structured so the resulting outputs can be consumed by your reporting layer without unnecessary infrastructure. I’ll also provide a plain-English run-book covering the architecture, data flow, deployment, maintenance and steps for extending the workflow. I’m ready to review the AX/Power BI export structure and recommend the most suitable AWS architecture before implementation. Best Regards, Shailender

@tamannarajput18

Hi, I can help build an AWS-based machine learning workflow to turn your Dynamics AX and Power BI data into actionable insights. I can handle: Data ingestion into AWS S3 and structured data preparation Data cleansing and transformation using AWS Glue/Athena where suitable SageMaker model training, evaluation, and prediction Connecting the ML output back to your reporting layer A scalable architecture focused on keeping AWS costs reasonable Clear documentation and a simple run-book for future maintenance I can first review your current AX/Power BI data format and recommend the most practical AWS architecture. Then I’ll build one complete end-to-end ML workflow and walk you through the solution before handover. I’m ready to get started. Thank You, Tamanna

@alfatrade2

Landing AX exports into S3 is the easy part — the real question is whether your Power BI data already has a clean grain (daily sales by SKU, for example) or if I'll need to build the cleansing step from raw transactional dumps first, since that changes the SageMaker model choice entirely. I've built pipelines that ingest, classify and score data before feeding results back into reporting layers. Do you have a target metric in mind, like demand forecasting or churn, or should I propose one based on what's in your exports?

@MrShortFlow

The core problem: unifying Power BI and Dynamics AX exports into a clean, versioned AWS data layer, then proving value with one working SageMaker model tied back to reporting. Delivery in three steps: 1) S3 landing zone with a Glue job that ingests AX/Power BI CSV exports, applies cleansing (type casts, null handling, deduplication) and writes Parquet to a curated bucket, cataloged in Glue Data Catalog. 2) One SageMaker notebook that trains and evaluates a model (e.g. demand or sales prediction, based on your fields) and writes predictions to S3/Athena for Power BI to consume. 3) A short run-book covering re-runs, cost control, and extension points. v1 (ingestion plus cleansing) within 2 working days of access. First two decisions: land raw exports in S3 partitioned by date/source before any transformation, so nothing is lost or overwritten; use Glue over Lambda for cleansing since your volumes and schema changes will likely exceed a single Lambda's practical limits. I will not promise a fully tuned model. Which target variable from your AX/Power BI data (sales, demand, defect rate) should the first SageMaker example predict. Price: 4300 INR fixed · Timeline: 3 working days from access/brief Konrad

@ektatdsl

Hi, I can help turn your existing AX/Power BI data into a repeatable AWS pipeline that produces usable ML predictions, with the architecture kept practical and cost-conscious. My approach → → Land and organize source data in S3 → Clean/transform datasets for ML use → Build an end-to-end SageMaker training/evaluation workflow → Return predictions to your reporting layer → Add clear logging and a simple maintenance runbook → Walk you through the complete setup and extension process My background is in AI/ML integrations, backend APIs and data-driven applications. I’d first review your sample export and recommend the simplest AWS architecture for the actual data. Share a sample AX/Power BI export and I can assess the pipeline before we start.

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@bojans9

Hello Hope you are doing well "AWS Machine Learning Data Pipeline" - I can build a scalable AWS workflow that transforms AX and Power BI data into actionable machine-learning insights. I can build repeatable ingestion into S3, cleanse and register datasets, then develop an end-to-end SageMaker model for business predictions. I will configure AWS services, train and evaluate the model, document the architecture, optimize costs, and provide clear maintenance guidance afterward. Can you share a sample AX or Power BI export so I can assess the data structure? Looking forward to working with you.

@usmanakhtarraza

Hi, I have carefully reviewed your requirements and can deliver a reliable, maintainable solution that follows the requested scope. I will confirm the workflow and milestones before development so expectations, timeline and deliverables remain clear. My experience includes full-stack web development, SaaS products, mobile applications, APIs, databases and third-party integrations. I follow a structured process covering implementation, testing, regular progress updates, clean source code and clear documentation. I am available to discuss the requirements and can begin once the scope, access and delivery priorities are confirmed. Please message me so we can review the project details and agree on the most practical implementation plan. Best,
Usman

@iFixNow

Hi, there The thing that decides your running cost is not the model, it is what stays switched on. A notebook or a real time endpoint bills by the hour whether anyone uses it, which is where these projects turn expensive. A scheduled batch prediction writing to a table costs a fraction and puts the same numbers in Power BI each morning. Exports out of AX also break silently when a column is renamed, so the loader should flag mismatches rather than absorb them. And start with a plain baseline forecast: if last year plus a trend beats the trained model, you have saved the cost and learned something real. As CTO of Assist A Boss I build AWS pipelines end to end, including one for a client's finance data across Glue, Lambda, S3 and Athena that improved operational performance by about 30%. As Network and Security Administrator at Foundation Risk Partners I designed the secure AWS side, so bucket policies are set deliberately, not left open. - Land raw to S3, cleaned to Parquet, catalogued for Athena - One worked example trained, measured against the baseline - Predictions written back where Power BI can read them - Plain English run book and a walkthrough at handover If you're looking for scalable, practical value rather than just a one-time project, I can help you achieve meaningful results