Mlflowemplois

...non-technical stakeholders 5) Handover package a) Clean Python codebase + README b) Validation report (results, risks, limitations) c) “Plain English” summary for business stakeholders Tech Stack (Preferred) 1. Python 3.10+ 2. PyTorch 3. PINN tooling: DeepXDE or custom PINN in PyTorch 4. pandas, numpy, scikit-learn 5. PDF extraction: pdfplumber + (Camelot/Tabula if needed) Optional: MLflow or W&B for experiment tracking Optional: FastAPI or Streamlit for demo Required Experience (Non-Negotiable) Proven experience with time-series forecasting on messy real-world datasets Hands-on experience with Physics-Informed ML / PINNs (show work, not theory) Strong Python engineering (clean modular code, reproducible experiments) Experience building validatio...

...workflow must be fully tracked in MLflow so every experiment, metric, and artifact is reproducible. Data sources are push-first wherever the provider allows; any remaining economic indicators can be polled on a schedule. Live market data from stock exchanges is the non-negotiable core feed, with news and macro releases treated as optional extensions once the baseline is rock-solid. You will design the Kafka topics, Spark jobs, Timescale hypertable schema, and the FastAPI endpoints that deliver the latest forecast and confidence bands. The ML layer should train incrementally, register new versions, and let me roll back instantly if a model under-performs. Deliverables • Docker-compose (or similar) stack that spins up Kafka, Spark, TimescaleDB, FastAPI, and MLflow ...

...working with healthcare or clinical data Experience deploying ML models in production environments Prior work with CDSS, healthcare AI, or predictive analytics preferred Technical Skills Expert in Python Strong hands-on experience with: XGBoost / LightGBM / CatBoost Scikit-learn Explainability tools (SHAP required) Experience with time-series patient data Familiarity with Docker, FastAPI, MLflow is a plus Healthcare & Regulatory Knowledge (Preferred) Understanding of hospital workflows (ICU, wards, OPD) Familiarity with clinical data (vitals, labs, diagnoses) Knowledge of SFDA, PDPL, hospital ethics / IRB Experience in Saudi Arabia or GCC healthcare is an advantage Why Join Us? --------------------------------------- Work on AI that directly impacts patient sa...

...real-world problems using AI and security-driven approaches. I enjoy working at the intersection of machine learning, cybersecurity, and data science, with a particular interest in secure machine learning systems, threat detection, and intelligent data modeling. Key Skills: Machine Learning, Cybersecurity, Cryptography, Data Analysis, Statistical Modeling, Python, Java, C, SQL, MLops (Airflow, Mlflow, Docker, FastAPI), Forensics Tools, Network Security (Wireshark), SPSS, SAS, R....

...launch training jobs inside Azure ML (or another MCP-compatible service) • monitor metrics, handle checkpoints, and push trained weights back to Azure • keep the whole pipeline modular so I can swap models or scale later You should already be comfortable with Agentic AI patterns, the MCP orchestration layer, Azure ML SDK, and the usual Python/NLP libraries (Transformers, PyTorch/TensorFlow, MLflow for tracking). Clear comments and step-by-step explanations are essential because I’ll be extending the pipeline myself after delivery. Acceptance criteria — end-to-end script or notebook runs in my Azure subscription without edits — logs training metrics in the workspace and stores the final model artifact — includes a brief README describi...

...TimeSformer. Data Correlation: Translate biomechanical outputs into performance scores and predictive potential markers. Required Technical Stack Languages/Frameworks: Python, PyTorch (Advanced). Vision: YOLO v8-v11, RT-DETR, Ultralytics, DeepLabCut, SLEAP. Spatio-Temporal: VideoMAE, Video Swin Transformer, TimeSformer. Deployment/Optimization: ONNX Runtime, TensorRT, MLOps (Weights & Biases / MLflow). Mandatory Candidate Qualifications We are looking for a top-tier expert. Please only apply if you meet the following criteria: Serious References: You must provide verifiable case studies or GitHub repositories of similar complex computer vision projects (pose estimation, motion analysis, or animal tracking). Experience: At least 5+ years of experience in Computer Vis...

...TimeSformer. Data Correlation: Translate biomechanical outputs into performance scores and predictive potential markers. Required Technical Stack Languages/Frameworks: Python, PyTorch (Advanced). Vision: YOLO v8-v11, RT-DETR, Ultralytics, DeepLabCut, SLEAP. Spatio-Temporal: VideoMAE, Video Swin Transformer, TimeSformer. Deployment/Optimization: ONNX Runtime, TensorRT, MLOps (Weights & Biases / MLflow). Mandatory Candidate Qualifications We are looking for a top-tier expert. Please only apply if you meet the following criteria: Serious References: You must provide verifiable case studies or GitHub repositories of similar complex computer vision projects (pose estimation, motion analysis, or animal tracking). Experience: At least 5+ years of experience in Computer Vis...

...Responsibilities MLOps Responsibilities: Collaborate with data scientists to operationalize ML workflows. Build complete ML pipelines with Airflow, Kubeflow Pipelines, or Metaflow. Deploy models using KServe, Seldon Core, BentoML, TorchServe, or TF Serving. Package models into Docker containers using Flask or FastAPI or Django for APIs. Automated dataset versioning & model tracking via DVC and MLflow. Setup model registries and ensure reproducibility and audit trails. Implement model monitoring for: (i) Data drift and schema validation (using tools like Evidently AI, Alibi Detect). (ii) Performance metrics (accuracy, precision, recall). (iii) Infrastructure metrics (latency, throughput, memory usage). Implement event-driven retraining workflows triggered by drift alerts o...

...solution must cover Chemistry, Hematology and Pathology workloads and serve clinics, hospitals and government agencies—including police and army medical units—without compromise on speed, accuracy or depth of analysis. Current blueprint • Sample intake with AI-driven quality checks • Instrument capture and digital pathology scanning (3DHistech / Hamamatsu) • PyTorch models orchestrated through MLflow for pre-screening and report generation • Clinician portal pushes real-time alerts and referral prompts • Specialist routing and secure chain-of-custody for forensic specimens • Cloud LIMS (OpenELIS or LabWare Cloud) as the data backbone • FHIR/REST APIs, Keycloak SSO, TLS 1.3 and AES-256 encryption securing every transact...

...persist at least six months to Parquet. FEATURES Enrich every bar with 50 + TA-Lib studies. Moving Average, RSI and Volume are the key signals I monitor, alongside on-chain metrics and Grok sentiment. Persist engineered features to an HDF5 store for rapid sampling. ML (CRITICAL FIRST) • Daily LightGBM retrain must finish in under two minutes on a mid-tier GPU/CPU • Track experiments with MLflow and emit SHAP explanations to JSON • Absolutely no look-ahead bias—be ready to explain your defence strategy in the bid • Code should default to asyncio and type-hinted style EXECUTION Convert model signals to paper or live orders with a 1 % max risk per trade, slippage guard and Telegram push alerts. DASHBOARD A Streamlit board updates P&L a...

...Experience: 1) 5+ years of software development experience (Python) with the best practices of well-developed software engineering skills. 2) Knowledge of cloud-based computing (AWS), DevOps tools, and CI/CD pipelines.  3) Knowledge about the ML model lifecycle. 4) Containerization and orchestration tools (Docker and Airflow/Dagster/Prefect). 5) Integrating AI/ML models into ML orchestration tools (MLflow). 6) Version control systems like GitHub and bug/work tracking systems like JIRA.  Nice-to-Have: - Experience in financial systems or trading platforms. - Experience in algorithmic trading, forecasting models, and ML concepts....

...(web). ## Key Features (MVP) * **Data** * Live + historical OHLCV, corporate actions; optional news/NLP and fundamentals. * Resampling (tick/1m/5m/15m/EOD); survivorship-bias-aware universes. * **Modeling** * Signals for: **trend-follow**, **mean-revert**, **breakout-scalp**, **volatility-compression**, **RL policy** (buy/sell/hold/size). * Walk-forward & **purged K-fold** validation; MLflow model registry. * **Scalping Automation** * Micro-targets with **tight SL**, position auto-scale, partial take-profit, time-stop, spread/impact filters. * Venue/broker adapters with **latency budget**; fallback to passive/limit if slippage high. * **Recommendations & Watchlists** * Pre-open and intraday **“Future Movers”** list (top-N symbols with co...

Data Scientist (Python / Machine Learning) Company Overview: We are an Outsourcing / Contract Specialist Team dedicated to providing high-level data solutions for private clients, startups, and enterprise organizations. We are seeking a Data Scientist skilled in Py...and analysts to improve model performance. Requirements: 3+ years of experience in data science or machine learning. Proficiency in Python and ML libraries (Scikit-learn, XGBoost, TensorFlow). Strong understanding of statistics, data wrangling, and model evaluation. Experience working with SQL databases and API integrations. Preferred Skills: Familiarity with AWS Sagemaker, Databricks, or MLflow. Experience deploying models via FastAPI, Flask, or Docker. Compensation: $55–$100 per hour or $110,000–...

...cross-encoders for top results • Optimize for both precision and recall with latency under 2 seconds • Document findings, metrics, and model decisions • (Later) Help build continuous monitoring and drift detection ⸻ Tech Stack You enjoy • Python (FastAPI, PyTorch, SentenceTransformers) • PostgreSQL + pgVector • OpenAI / Cohere APIs • rank-bm25 or Elasticsearch • ranx / ir-measures (evaluation) • Jupyter, MLflow, or Weights & Biases for experiment tracking • Integration with existing TypeScript backend (via API) Test task(paid) Evaluate and improve the semantic search quality of Yena’s current setup using a small sample dataset. What to Do 1. Dataset Setup • Use a sample CSV of 100 candidate profiles (we’ll...

Overview We are seeking a highly experienced Software Solutions / Technical Expert who can assist with interview preparation and/or proxy support for an advanced enterprise-level Solutions Engi...workload orchestration • Hands-on experience with: o GPU-based model training or inference (optional but a plus) o Docker, Helm, Prometheus, Grafana, Terraform, or Ansible • Excellent communication and presentation skills to handle technical discussions and executive briefings. Preferred Skills • Exposure to AI Factory-style orchestration, Mission Control-type management layers, or / MLflow / Kubeflow pipelines. • Prior experience leading technical trainings, conference demos, or customer enablement sessions. • Application Developer (CKAD) certificati...

Looking for a python developer to work along my project team. If you match this requirment, connect with me. Job Title: Backend Engineer About the Role: We’re looking for a Backend / Infrastructure Engineer to build robust, scalabl...building tools that empower other engineers, analysts & scientists ● Have shipped production systems at scale with uptime and performance goals ● Thrive in hybrid roles where backend, infra, and data intersect ● Care deeply about data quality, lineage, and performance Bonus Points: ● Experience with Lakehouse architectures (e.g., Delta Lake, Apache Iceberg) ● Familiarity with MLOps workflows (MLflow, Feature Stores) ● Contributions to open-source projects ● Knowledge of event-driven architectures and CDC (Change Data Capture) pa...

...backbone is already selected—Kafka for ingestion and queuing, Spark Structured Streaming for computation, TimescaleDB for long-horizon storage, FastAPI for the service layer, and MLflow for experiment tracking and model registry. Here’s the core flow I need implemented: • Pull real-time data simultaneously from stock market feeds, key economic indicators, and major financial-news streams. • Push the raw events into Kafka topics, apply Spark jobs to clean and feature-engineer them, then write both the enriched streams and batch aggregates into TimescaleDB. • Orchestrate model training and scoring with MLflow so that new models can be promoted without downtime. Predictions must be streamed back through Kafka and exposed via low-latency FastAP...

...dashboard (ROI %, forecasted appreciation) Deliver results as JSON + Plotly-ready datasets. 7. Maintenance & Monitoring Implement automated retraining pipelines. Track model accuracy, drift, and versioning (using MLflow or similar). Schedule weekly or monthly retraining using updated PropStream data. Tech Stack Layer Preferred Tools Programming Python 3.10+, Flask Data Pandas, NumPy, SQLAlchemy Modeling XGBoost, LightGBM, TensorFlow/Keras Visualization Plotly, Matplotlib Storage PostgreSQL or Firebase Deployment AWS Lambda / EC2 / GCP Auth / API JWT, Flask-RESTful Tracking MLflow, DVC Deliverables Cleaned and merged dataset (CSV + schema) Feature engineering scripts Trained models (XGBoost & LSTM) Flask REST API with /predict endpoint Model evaluation r...

... Model Saving, Resuming, and VersioningCheckpoints: Save policy/value nets periodically (e.g., every 10k steps) with Stable-Baselines3's save(). Include env state for resumes. Resume Logic: Implement load() with continue_training=True. Handle interruptions (e.g., script crash) by checking for existing models at startup. Best Versions: Track via callbacks (e.g., save on reward improvement). Use MLflow/WandB for versioning/logging to compare runs. Evaluation During Training: Periodic eval on holdout env (no exploration) to catch degrading performance early. 4. Environment and Reward DesignEnv Compatibility: Ensure Gym-like setup (state/action/reward/done). For trading: Realistic sim (fees, slippage); watch for reward hacking (e.g., agent exploits bugs for infinite rewards). R...

...engineers who can explain ideas clearly. 1. Data Engineer Key Skills: - Python, SQL, Spark, dbt - Airflow, Azure Data Factory, or Prefect - Snowflake, BigQuery, or Redshift - Kafka, Kinesis, or Flink (streaming) - CI/CD (GitHub Actions, Jenkins), Docker, Terraform 2. AI/ML Engineer / Data Scientist Key Skills: - Python (Pandas, Scikit-learn, PyTorch, TensorFlow) - ML lifecycle: MLflow, SageMaker, or Databricks - LLMs, NLP, time-series, recommendation systems, Computer Vision, GenAI - FastAPI/Flask for deployment, Hugging Face, LangChain - Experience with cloud-based ML: AWS/GCP/Azure 3. Full Stack Engineer Key Skills: Backend: Node.js / .NET Core / Spring Boot / Python Frontend: React.js / Angular / Vue.js, TypeScript, HTML/CSS Databases: SQL Ser...

...& Integrations: Kafka/Redpanda for low-latency event streaming, MQTT for IoT ingress, and REST/webhooks for external system integrations. 3. Core Services: Microservices architecture using TypeScript (NestJS) and Python (FastAPI), PostgreSQL for OLTP data, TimescaleDB for telemetry, and immudb for tamper-evident audit trails. 4. AI/ML: On-device models (TFLite/ONNX) for risk-state computation, MLflow for model management, and privacy-preserving analytics pipelines. 5. Identity & Privacy: SSO/OIDC with Azure AD/Auth0, fine-grained consent management, PII segmentation with encryption (AWS KMS), and auditable governance UI. 6. Dashboards & UX: Web apps using (React) with role-based views and operational analytics via Metabase or Superset. Engagement Terms: - Milestone ...

...technical solutions and robust data models. Stay updated with the latest trends in data architecture and analytics. Required Qualifications: 6+ years in Big Data architecture and engineering (Databricks & AWS tech stack). 3+ years of experience with AWS services: S3, Glue, Redshift, Lake Formation, EMR, Kinesis, RDS, DMS. Strong hands-on experience with Databricks, Apache Spark, Delta Lake, and MLflow. Proficiency in Python, SQL, and PySpark. Experience extracting and integrating data from SAP (BW, S/4HANA, BODS). Solid understanding of data modeling, data lakehouse architecture, and ETL/ELT processes. Proven ability to lead data engineering teams and manage projects. Bachelor's degree in Computer Science, IT, Data Science, or related field. Excellent communication and s...

We are... Build and maintain robust data pipelines (ETL, audio/text preprocessing). Implement a complete MLOps workflow: training → CI/CD → monitoring → deployment. Optimize inference of NLP/STT/TTS models on GPUs (target latency under 300 ms). Industrialize workflows from data scientists and manage containerized environments (Docker, Kubernetes). Automate model tracking and performance monitoring (MLflow, Weights & Biases, or similar). Requirements Proven experience in MLOps and Data Engineering. Strong skills in Python, SQL, Docker, Kubernetes, GitLab/GitHub CI/CD. Experience with ML frameworks (PyTorch, HuggingFace, TensorFlow). Knowledge of GPU optimization (NVIDIA L4, A100, H100) is a strong plus. Bonus: experience with real-time systems or audio st...

I’m assembling a long-term partnership with an engineer who lives and breathes AI. My company is in a strong growth phase and we have a roadmap of initiatives ahead, but the starti...set together and is robust enough for a plant-floor environment. Clear, timely communication and reliability are non-negotiable. I value partners who keep learning, document their work, and are comfortable iterating quickly from proof-of-concept to production. Expect candid feedback, defined acceptance criteria, and the freedom to experiment with modern toolchains (Python, TensorFlow/PyTorch, MLflow, Docker/Kubernetes—use what you think works). If you’re looking for more than a one-off gig and want to co-create impactful AI systems, let’s talk through the manufacturing use case...

...Requirements: • Expertise in Advanced MLOps Consulting & Implementation (5+ years preferred) Focus on scalable, secure, and Responsible AI deployments. • Experience with Mid-Market SaaS Companies and Tech-Forward Startups We prefer candidates who understand the unique challenges and needs of these environments. • Technical Stack: Proficient with major cloud providers (AWS, Azure, GCP) and MLOps tools (MLflow, Kubeflow, etc.), CI/CD for ML, and IaC. • Client-Facing Under Our Brand Once the brand and platform are launched, we’ll actively promote your services under our name. We’ll handle client acquisition—you’ll deliver the services. Clients will be under our ownership, while you operate as our exclusive service provider. &bu...

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I'm looking for an experienced professional to assist with a hands-on project involving Azure Databricks, MLflow, RAGs and MLOps. Key Responsibilities: - Set up CI/CD pipelines. - Data processing and ETL with PySpark. - Develop and train machine learning models. Ideal Skills and Experience: - Proficiency in Azure Databricks and MLflow. - Strong background in MLOps and CI/CD pipelines. - Expertise in PySpark. - Familiarity with TensorFlow, PyTorch, or Scikit-learn. Please include your relevant experience and approach in your bids.

...related to AI/ML workloads, scalability, and performance. • Excellent leadership and communication skills, with a proven ability to mentor engineers and work closely with cross-functional stakeholders. • Self-starter who excels at execution, balancing short-term technical delivery with long-term scalability and efficiency goals. Bonus points for — • Experience with AI/ML infrastructure tools like MLFlow, Kubeflow, or MLOps best practices. • Experience with real-time data processing & streaming technologies (Apache Kafka, Flink, Spark). • Familiarity with NLP frameworks (e.g., Hugging Face, spaCy) • Open-source contributions, side projects, or thought leadership through technical content (blogs, videos). • Knowledge of data privacy and...

Africana Energy Limited is developing a hybrid renewable energy-powered green hydrogen and synthetic fuel production facility in Blantyre, Malawi. We are seeking a capable freelancer or team of developers to implement and customise an open-source AI-based automation, optimisation and control platform using OpenEMS, MLFlow, and related technologies. This is a mission-critical deployment for Africa’s first integrated solar-wind-hydrogen-Fischer-Tropsch fuel plant, with a focus on cost-effectiveness, modularity, and long-term scalability. Plant Systems & Components A. Renewable Energy Generation • Wind Turbines: Envision Energy 6.25 MW turbines (multiple units) • Solar PV System: ~40 MWp high-efficiency N-type panels (e.g. DMEGC, LONGi) • Inverters + MPPTs: H...

I'm looking for a skilled machine learning expert to help develop and deploy an AI-driven chatbot utilizing large language models (LLMs). The ideal candidate will have experience with Python, Spark, and integrating with APIs. Key Responsibilities: - Model Development: Work with LLMs and utilize model context protocol to create an effective AI-driven cha...to create an effective AI-driven chatbot. - Integration: Connect the chatbot with various APIs for enhanced functionality. - Deployment: Use on-prem applications, HDFS, Hive, Hadoop, Airflow DAG, and Kafka for server deployment. The perfect freelancer for this job will have a strong background in machine learning and Python, with practical experience working with chatbots and LLMs. Knowledge of MLflow and model context prot...

We are seeking highly skilled databricks data engineer who have experience in building data pipelines and data modelling in delta should have a deep understanding of Databricks advanced features, including Unity Catalog, Delta Lake optimizations, storage efficiency techniques, and security best practices. Key Respons...well-governed datasets. Nice to Have: Experience with Databricks AI/LLM capabilities, including Databricks Model Serving and Lakehouse AI. Familiarity with Databricks Agentic Framework for building AI-powered data retrieval and analysis applications. Experience in integrating LLMs with Databricks, such as vector search, embedding models, and fine-tuning models for enterprise use cases. Understanding of MLflow for model tracking, deployment, and versioning.

Summary We are looking for a Senior MLOps Engineer to support the AI CoE in building and scaling machine learning operations. This position requires both strategic oversight and direct involvement in MLOps infrastructure design, automation, and optimization. The person will lead a team while collaborating with various stakeholders to manage machine learning pipelines and model d...learning models on GCP / AWS /Azure ⮚ Hands-on experience with data catalog tools ⮚ Expert in GCP / AWS / Azure services such as Vertex AI, GKE, BigQuery, and Cloud Build, Endpoint etc for building scalable ML infrastructure (GCP / AWS / Azure official Certifications are a huge plus) ⮚ Experience with model serving frameworks (e.g., TensorFlow Serving, TorchServe), and MLOps tools like Kubeflow, MLflow, or...

...learning frameworks like TensorFlow, PyTorch, Keras, or Scikit-learn.  • Hands-on experience with cloud platforms such as AWS, Azure, or GCP.  • Experience in Machine Learning and Neural Network architectures like Ensemble Models, SVM, CNN, RNN, Transformers, etc.  • Experience in Natural language processing (NLP) tools: NLTK, Spacy, and Gensim.  • Experience in MLOps tools such as Kubeflow, MLflow, or Azure ML. • Knowledge/hands-on experience with workflow tools like Airflow. • Experience with Microservices architecture. • Experience with SQL and NoSQL databases such as MongoDB, Postgres, Neo4j, etc. • Experienced with Rest API python frameworks such as Fast API/Flask/Django. • Excellent problem-solving sk...

I'm seeking a seasoned professional in AI/ML with deep expertise in developing and deploying Machine Learning models using TensorFlow. Experience with MLflow, LLMs, and API development through FastAPI is crucial for this project. Key Skills and Requirements: - Proficient in TensorFlow for model development. - Extensive experience in developing and deploying Machine Learning models. - Skilled in using MLflow for tracking experiments and managing the ML lifecycle. - Proficient in FastAPI for building and optimizing APIs. - Hands-on experience with the Azure stack including Synapse, Spark/Python, SQL Azure, and ADF. - Familiarity with PowerBI and Microsoft SQL Server. If you have these qualifications and are interested in this project, let's connect!

...with integrating and debugging MLFlow, Docker, and Prometheus. The project is time-sensitive and any delay could impact the overall timeline. I'm facing configuration issues with MLFlow and Airflow, specifically with plotting and artifact logging. I am training a model in airflow, the metrics and parameters are correctly logged to the UI localserver, but I cannot see the artifacts / plot of a plotting function. This must be fixed. This is impacting my ability to track training metrics and model visualizations. I need an expert in MLFlow and Airflow who can help me debug and configure these components urgently. Time is of the essence, so I appreciate swift and effective solutions. It will not take more than one hour maximum. Ideal Skills: - Proficiency with ...

I'm facing configuration issues with MLFlow and Airflow, specifically with plotting and artifact logging. I am training a model in airflow, the metrics and parameters are correctly logged to the UI localserver, but I cannot see the artifacts / plot of a plotting function. This must be fixed. This is impacting my ability to track training metrics and model visualizations. I need an expert in MLFlow and Airflow who can help me debug and configure these components urgently. Time is of the essence, so I appreciate swift and effective solutions. It will not take more than one hour maximum.

I need an expert to finalize the monitoring of my ML pipeline project. The pipeline is operational with Airflow and Docker, but I require assistance in completing model tracking on MLFlow and environment oversight with Prometheus. Ideal Skills: - Proficient in MLFlow and Prometheus. - Experienced with Airflow and Docker. - Strong understanding of performance metrics in machine learning. - Capable of setting up efficient monitoring systems. I have an ML pipeline project up and running, with Airflow and Docker. Please complete monitoring of models on MLFlow and monitoring of Airflow environment on Prometheus. Also, I would like the DAG I have configured to be replicated - one that uses existing pipeline training the model, and another that uses the trained model .pkl ...

Data Science resource require...anomaly detection systems to identify irregular patterns within financial datasets. - This role requires a blend of statistical analysis, machine learning expertise, and a deep understanding of financial markets. - Excellent communication with the ability to work individually and take the work responsibilities and ownership. Machine learning expertise required for the role: Machine Learning Tools - MLflow, Kubeflow Machine Learning Techniques - Anomaly Detection Machine Learning Deliverables - Time Series Forecasting Other - Data Science, Python, Machine Learning, Data Analysis Data Engineer requirement 8-10 years No Specific JD but the client needs a senior Data Engineer with good technical skills and proven work experience with good communicati...

Need to make the script work, Integrate in the workflow. The entire project that needs to run is located in ... --model_dir /Volumes/Work/Github/Python/FSL_model/output --learning_rate 0.00002 --undersampling_rate 0.5 --batch_size 16 run command you should change file path fitz~= pandas~=2.0.2 numpy~=1.23.5 pytesseract~=0.3.10 matplotlib~=3.7.1 Pillow~=9.5.0 mltable~=1.3.0 opencv-python~=4.7.0.72 Unidecode~=1.3.6 pdf2image~=1.16.3 Camelot~=12.6.29 image~=1.5.33 argparse~=1.4.0 mlflow scikit-learn~=1.2.2 utils~=1.0.1 nltk~=3.8.1 pathlib~=1.0.1 torch~=2.0.1 scipy~=1.10.1 transformers~=4.30.1 tqdm~=4.65.0 torchmetrics~=0.11.4 try to use this list And if you get fail, then just run program using above command then install one by one

Web App for Data Science Project Preferred Programming Language: Python Timeline for Project Completion: 2 days Skills and Experience: - Proficiency in Python programming - Experience with web app development using Flask or similar frameworks - Experience with jupyter notebook - Experience with mlflow We are looking for a skilled developer who can create a web app for our data science project. Just a simple web app for simple supervised machine learning models

Create a CI/CD automation pipeline with Python, Terraform, AWS, MLFlow, AirFlow, and Jenkins.

We would like to hear the opinions of machine learning practitioners on machine learning experiment management tools. Experiment management tools support practition...tools support practitioners performing machine learning (ML) or deep learning (DL) experiments to manage all involved artifacts and metadata (datasets, features, scripts, hyperparameters, evaluation metrics, models, …). Such tools are used to reproduce or trace experiments, analyze experiment results, and collaborate with other practitioners. Popular tools are, for instance, , DVC, or MLFlow. These tools allow users to track or log artifacts when performing experiment runs. Completing the survey takes approximately 10 minutes for each ML practitioner. Due to project constraints, we can only pay a maximum of 15...

We would like to hear the opinions of machine learning practitioners on machine learning experiment management tools. Experiment management tools support practition...tools support practitioners performing machine learning (ML) or deep learning (DL) experiments to manage all involved artifacts and metadata (datasets, features, scripts, hyperparameters, evaluation metrics, models, …). Such tools are used to reproduce or trace experiments, analyze experiment results, and collaborate with other practitioners. Popular tools are, for instance, , DVC, or MLFlow. These tools allow users to track or log artifacts when performing experiment runs. Completing the survey takes approximately 10 minutes for each ML practitioner. Due to project constraints, we can only pay a maximum of 15...

We would like to hear the opinions of machine learning practitioners on machine learning experiment management tools. Experiment management tools support practition...tools support practitioners performing machine learning (ML) or deep learning (DL) experiments to manage all involved artifacts and metadata (datasets, features, scripts, hyperparameters, evaluation metrics, models, …). Such tools are used to reproduce or trace experiments, analyze experiment results, and collaborate with other practitioners. Popular tools are, for instance, , DVC, or MLFlow. These tools allow users to track or log artifacts when performing experiment runs. Completing the survey takes approximately 10 minutes for each ML practitioner. Due to project constraints, we can only pay a maximum of 15...

We would like to hear the opinions of machine learning practitioners on machine learning experiment management tools. Experiment management tools support practition...tools support practitioners performing machine learning (ML) or deep learning (DL) experiments to manage all involved artifacts and metadata (datasets, features, scripts, hyperparameters, evaluation metrics, models, …). Such tools are used to reproduce or trace experiments, analyze experiment results, and collaborate with other practitioners. Popular tools are, for instance, , DVC, or MLFlow. These tools allow users to track or log artifacts when performing experiment runs. Completing the survey takes approximately 10 minutes for each ML practitioner. Due to project constraints, we can only pay a maximum of 15...

We would like to hear the opinions of machine learning practitioners on machine learning experiment management tools. Experiment management tools support practition...tools support practitioners performing machine learning (ML) or deep learning (DL) experiments to manage all involved artifacts and metadata (datasets, features, scripts, hyperparameters, evaluation metrics, models, …). Such tools are used to reproduce or trace experiments, analyze experiment results, and collaborate with other practitioners. Popular tools are, for instance, , DVC, or MLFlow. These tools allow users to track or log artifacts when performing experiment runs. Completing the survey takes approximately 10 minutes for each ML practitioner. Due to project constraints, we can only pay a maximum of 15...

We would like to hear the opinions of machine learning practitioners on machine learning experiment management tools. Experiment management tools support practition...tools support practitioners performing machine learning (ML) or deep learning (DL) experiments to manage all involved artifacts and metadata (datasets, features, scripts, hyperparameters, evaluation metrics, models, …). Such tools are used to reproduce or trace experiments, analyze experiment results, and collaborate with other practitioners. Popular tools are, for instance, , DVC, or MLFlow. These tools allow users to track or log artifacts when performing experiment runs. Completing the survey takes approximately 10 minutes for each ML practitioner. Due to project constraints, we can only pay a maximum of 15...

We would like to hear the opinions of machine learning practitioners on machine learning experiment management tools. Experiment management tools support practition...tools support practitioners performing machine learning (ML) or deep learning (DL) experiments to manage all involved artifacts and metadata (datasets, features, scripts, hyperparameters, evaluation metrics, models, …). Such tools are used to reproduce or trace experiments, analyze experiment results, and collaborate with other practitioners. Popular tools are, for instance, , DVC, or MLFlow. These tools allow users to track or log artifacts when performing experiment runs. Completing the survey takes approximately 10 minutes for each ML practitioner. Due to project constraints, we can only pay a maximum of 15...

We would like to hear the opinions of machine learning practitioners on machine learning experiment management tools. Experiment management tools support practition...tools support practitioners performing machine learning (ML) or deep learning (DL) experiments to manage all involved artifacts and metadata (datasets, features, scripts, hyperparameters, evaluation metrics, models, …). Such tools are used to reproduce or trace experiments, analyze experiment results, and collaborate with other practitioners. Popular tools are, for instance, , DVC, or MLFlow. These tools allow users to track or log artifacts when performing experiment runs. Completing the survey takes approximately 10 minutes for each ML practitioner. Due to project constraints, we can only pay a maximum of 15...

We would like to hear the opinions of machine learning practitioners on machine learning experiment management tools. Experiment management tools support practition...tools support practitioners performing machine learning (ML) or deep learning (DL) experiments to manage all involved artifacts and metadata (datasets, features, scripts, hyperparameters, evaluation metrics, models, …). Such tools are used to reproduce or trace experiments, analyze experiment results, and collaborate with other practitioners. Popular tools are, for instance, , DVC, or MLFlow. These tools allow users to track or log artifacts when performing experiment runs. Completing the survey takes approximately 10 minutes for each ML practitioner. Due to project constraints, we can only pay a maximum of 15...